The GTM Operating System Built on a
Data-First Platform

The GTM Operating System Built on a
Data-First Platform

The GTM Operating System Built on a
Data-First Platform

The GTM Operating System Built on a
Data-First Platform

Stop battling rigid, black-box tools. DataviCloud is the extensible data platform that acts as the connective tissue for your entire GTM stack.

We provide a flexible, end-to-end foundation to ingest, curate, and model your scattered data into a single, trusted source of truth. This gives your team the foundational strength to stop chasing data and start building—empowering you to deploy custom AI agents, chatbots, and apps (like LEO) that solve your unique technical challenges with ease.

400+

400+

Consolidate & Connect GTM data sources.

Consolidate & Connect GTM data sources.

Build, Don't Bend

Build, Don't Bend

Extensible platform for custom AI agents and apps.

Extensible platform for custom AI agents and apps.

Cut Data Prep by 90%

Cut Data Prep by 90%

Start building on a clean, trustworthy data foundation.

Start building on a clean, trustworthy data foundation.

Foundation

Foundation

Foundation

Foundation

Built on Three Foundational Pillars

Built on Three Foundational Pillars

Built on Three Foundational Pillars

Unified Data Foundation

Unified Data Foundation

100+ integrations, automated hygiene, and one unified source of truth (Time-Versioned ID Graph).

AI-Powered Intelligence

AI-Powered Intelligence

AutoML, Qurie, and predictive insights turn historical data into proactive, real-time recommendations.

Action-Oriented Execution

Action-Oriented Execution

Action-Oriented Execution

Reverse ETL + AI Agents push "next best actions" directly into operational tools to drive revenue.

problem

problem

problem

problem

The Architectural Failure of Your
Duct-Taped GTM Stack

The Architectural Failure of Your
Duct-Taped GTM Stack

The Architectural Failure of Your
Duct-Taped GTM Stack

Your GTM stack has exploded. You're the one writing brittle scripts and complex SQL JOINs to duct-tape 38+ rigid, black-box platforms together. Business leaders see the symptoms—bad forecasts, teams arguing over data—but you see the real cause.

It's an architectural failure:

It's an architectural failure:

Rigid, Walled Gardens

Every platform is a black box. You're forced to work around their limitations instead of building on top of them.

Rigid, Walled Gardens

Every platform is a black box. You're forced to work around their limitations instead of building on top of them.

Rigid, Walled Gardens

Every platform is a black box. You're forced to work around their limitations instead of building on top of them.

Rigid, Walled Gardens

Every platform is a black box. You're forced to work around their limitations instead of building on top of them.

Garbage In, Garbage Out

Leaders can't trust reports because you can't trust the underlying data. You spend more time validating and de-duping than building solutions.

Garbage In, Garbage Out

Leaders can't trust reports because you can't trust the underlying data. You spend more time validating and de-duping than building solutions.

Garbage In, Garbage Out

Leaders can't trust reports because you can't trust the underlying data. You spend more time validating and de-duping than building solutions.

Garbage In, Garbage Out

Leaders can't trust reports because you can't trust the underlying data. You spend more time validating and de-duping than building solutions.

Duct-Tape Data Models

"MQL" means three different things in three systems, and you're the one forced to maintain the complex, brittle logic that reconciles them.

Duct-Tape Data Models

"MQL" means three different things in three systems, and you're the one forced to maintain the complex, brittle logic that reconciles them.

Duct-Tape Data Models

"MQL" means three different things in three systems, and you're the one forced to maintain the complex, brittle logic that reconciles them.

Duct-Tape Data Models

"MQL" means three different things in three systems, and you're the one forced to maintain the complex, brittle logic that reconciles them.

You're a Bottleneck (And You Hate It)

You're stuck in a loop of manual data prep and ad-hoc requests instead of building the scalable, automated systems you know are possible.

You're a Bottleneck (And You Hate It)

You're stuck in a loop of manual data prep and ad-hoc requests instead of building the scalable, automated systems you know are possible.

You're a Bottleneck (And You Hate It)

You're stuck in a loop of manual data prep and ad-hoc requests instead of building the scalable, automated systems you know are possible.

You're a Bottleneck (And You Hate It)

You're stuck in a loop of manual data prep and ad-hoc requests instead of building the scalable, automated systems you know are possible.

SOLUTION

SOLUTION

SOLUTION

SOLUTION

Stop Duct-Taping. Start Building.

Stop Duct-Taping. Start Building.

Stop Duct-Taping. Start Building.

Your GTM stack has exploded into brittle, siloed platforms, forcing your team to waste engineering cycles on manual data prep and complex SQL. Business leaders see bad forecasts, but you see the architectural failure. This section reveals the technical foundation that finally solves the root cause.

The GTM OS Built on a True Data Platform.

The GTM OS Built on a True Data Platform.

The GTM OS Built on a True Data Platform.

The GTM OS Built on a True Data Platform.

DataviCloud is the connective tissue your stack is missing. We aren't another rigid, black-box app; we are an extensible GTM data platform. We give you the power to ingest, curate, and model all data from 400+ sources into a single, unified, time-versioned ID graph.


This clean foundation is what lets you stop being a report bottleneck and start building real solutions: deploy our 10+ pre-built AI agents for immediate value, or build your own custom apps (like LEO) on our platform.

DataviCloud is the connective tissue your stack is missing. We aren't another rigid, black-box app; we are an extensible GTM data platform. We give you the power to ingest, curate, and model all data from 400+ sources into a single, unified, time-versioned ID graph.


This clean foundation is what lets you stop being a report bottleneck and start building real solutions: deploy our 10+ pre-built AI agents for immediate value, or build your own custom apps (like LEO) on our platform.

DataviCloud is the connective tissue your stack is missing. We aren't another rigid, black-box app; we are an extensible GTM data platform. We give you the power to ingest, curate, and model all data from 400+ sources into a single, unified, time-versioned ID graph.


This clean foundation is what lets you stop being a report bottleneck and start building real solutions: deploy our 10+ pre-built AI agents for immediate value, or build your own custom apps (like LEO) on our platform.

The Transformation: From Passive Dashboards to Active Intelligence

The Old Way
  • Fragmented data & conflicting dashboards
  • Wasted hours reconciling the truth
  • Reactive, historical reports
  • Arguing over data definitions
The DataviCloud Way
  • A proactive, intelligent action layer
    Editing takes me forever.
    Editing takes me forever.
    Editing takes me forever.
  • A single, versioned truth with field-level confidence scoring
    I miss uploads trying to finish videos.
    I miss uploads trying to finish videos.
    I miss uploads trying to finish videos.
  • Proactive AI-agents that flag risks & drive immediate action
    I hate editing. I just want to record.
    I hate editing. I just want to record.
    I hate editing. I just want to record.
  • A unified data dictionary that aligns everyone
    My videos don’t look pro enough.
    My videos don’t look pro enough.
    My videos don’t look pro enough.

A Platform to Enable Business Users

A Platform to Enable Business Users

End the Data Debates for Good

Define 'MQL' once in our central Data Dictionary and Metric Layer. Your business users see one consistent, trusted number everywhere—ending the "my dashboard vs. your dashboard" arguments for good.

Deliver Insights They Can Trust

Stop reports your team second-guesses. Our Data Hygiene module with Confidence Scores delivers reliable insights. Users act with confidence on data that is clean, de-duplicated, and trustworthy.

Drive Users to Action

Empower teams to act, not look. Our Reverse ETL and AI Agents push alerts and actions into their tools. You become the enabler, deploying custom apps that solve problems, not just dashboards.

ARCHITECTURE

ARCHITECTURE

ARCHITECTURE

ARCHITECTURE

An Open Architecture for Action

An Open Architecture for Action

An Open Architecture for Action

An Open Architecture for Action

DataviCloud isn't another black-box dashboard. It's an end-to-end GTM data platform that acts as the connective tissue for your entire stack. We provide a single, logical architecture to take you from fragmented data to measurable outcomes—whether you use our pre-built solutions or build your own.

The DataviCloud Architecture:

The DataviCloud Architecture:

The DataviCloud Architecture:

The DataviCloud Architecture:

Ingest:

Ingest:

Connect and centralize data from 400+ sources (CRM, product, billing, external signals).

Connect and centralize data from 400+ sources (CRM, product, billing, external signals).

Clean & Model:

Clean & Model:

Consolidate all data into a unified, time-versioned ID graph. Our hygiene engine applies field-level confidence scores and quarantines bad data at the door.

Consolidate all data into a unified, time-versioned ID graph. Our hygiene engine applies field-level confidence scores and quarantines bad data at the door.

Enrich & Define:

Enrich & Define:

Add behavioral signals and centrally define all business logic in the Data Dictionary and Metric Layer. Define 'MQL' once, and it's true everywhere.

Add behavioral signals and centrally define all business logic in the Data Dictionary and Metric Layer. Define 'MQL' once, and it's true everywhere.

Analyze & Expose:

Analyze & Expose:

Surface insights using our pre-built tools (like Qurie and AutoML) or access the clean, modeled data directly to build your own custom analytics.

Surface insights using our pre-built tools (like Qurie and AutoML) or access the clean, modeled data directly to build your own custom analytics.

Act & Extend:

Act & Extend:

Deploy our 10+ pre-built AI Agents, build custom apps (like LEO) on our foundational platform, or push data to any tool via Reverse ETL.

Deploy our 10+ pre-built AI Agents, build custom apps (like LEO) on our foundational platform, or push data to any tool via Reverse ETL.

Learn:

Learn:

Measure the outcome of every action (both pre-built and custom) to create a closed feedback loop, update playbooks, and drive compounding accuracy.

Measure the outcome of every action (both pre-built and custom) to create a closed feedback loop, update playbooks, and drive compounding accuracy.

* Every field carries a confidence score. Every metric has lineage. Every action is tracked. This is what a true, extensible GTM operating system looks like, giving you complete trust in your data.

MODULES

MODULES

MODULES

MODULES

The Building Blocks of Your GTM OS for Smarter Revenue Growth.

The Building Blocks of Your GTM OS for Smarter Revenue Growth.

Our platform isn't a collection of siloed tools; it's a layered architecture. Each module acts as a building block, creating a single, extensible system that takes your data from raw ingestion to intelligent action.


LAYER 1: THE DATA FOUNDATION (INGEST & CLEAN)

LAYER 1: THE DATA FOUNDATION (INGEST & CLEAN)

Integrations

400+ Sources, One Foundation

Connect your entire GTM stack in minutes. Salesforce, HubSpot, Gong, Snowflake, Slack—DataviCloud ingests it all. This isn't just about connecting tools; it's about building a single, cross-platform foundation.

Why it matters:

Why it matters:

You're not locked into one CRM. You get to consolidate 400+ sources to build a truly unified customer view, solving the root cause of tool sprawl and data silos.

CRM: Salesforce, HubSpot

Product: Mixpanel, Amplitude, Product Telemetry/Usage

Data: Snowflake, BigQuery

Communication: Slack, Outreach, Gong, Avoma

External Signals: Job changes, tech stack, intent data

External Signals: Job changes, tech stack, intent

Data Hygiene

Trust is a First-Class Object

DataviCloud fixes the GTM "trust deficit" at the source. We consolidate and clean data from all 400+ sources into a single, cross-validated foundation. Bad data is quarantined before it corrupts your stack.

Why it matters:

Why it matters:

For one customer, we cut duplicates by 30% in the first month. Your forecasts become accurate, and you waste zero time on manual data cleaning.

Trust Scoring: Every field gets confidence based on source freshness and validation.

Versioned Truth: Time-stamped history so you always know what the system believed and when.

Quality Gates: Schema contracts block bad data at the point of ingestion.

LAYER 2: THE CORE LOGIC (MODEL & DEFINE)

LAYER 2: THE CORE LOGIC (MODEL & DEFINE)

Data Modelling

One Model to Rule Them All

This is the core of our platform. DataviCloud maps all your GTM sources—CRM, product, billing, external signals—to one opinionated common data model and unifies them into a time-versioned ID graph.

Why it matters:

Why it matters:

This unified graph is the stable foundation you use to build custom apps (like LEO) and reliable agents. It resolves identities, maintains historical accuracy, and ensures every team works from the same truth.

Resolves identities using deterministic keys and probabilistic matching.

Always maintains historical accuracy with type-two slowly changing dimensions.

The semantic layer clearly centrally defines KPIs to eliminate conflicting logic.

Data Dictionary

One Truth, One Voice

The Metric and semantic layer that centrally defines every KPI—MQL, SQL, pipeline coverage, PQL—so everyone in your organization speaks the same language. This is the "API" for your business logic.

Why it matters:

Why it matters:

You define 'MQL' once, and it's true everywhere. No more arguing in review meetings. Every metric has lineage, so you can click on any KPI and see exactly which sources and columns feed it.

Centrally defines critical KPIs in one place, ensuring company-wide alignment and clarity.

Tracks metric lineage for full transparency, making data auditing straightforward and reliable.

Provides guardrails to prevent teams from breaking definitions, maintaining data integrity.

LAYER 3: THE INTELLIGENCE (ANALYZE & LEARN)

LAYER 3: THE INTELLIGENCE (ANALYZE & LEARN)

AutoML

AI That Learns from Every Move

This is your compounding accuracy engine. DataviCloud's AutoML engine spots hidden patterns, surfaces what's working, and automatically updates playbook weights based on measured outcomes.

Why it matters:

Why it matters:

Every action your team or agents take feeds back into the system, making your custom and pre-built recommendations smarter over time.

Creates refined feature views from raw GTM signals.

Closes the loop by tracking action outcomes and updating playbooks.

Drives the Action Layer with next best actions, owners, and due dates.

Qurie (Conversational Analytics)

Qurie (Conversational

Analytics)

Qurie (Conversational

Analytics)

Your Pocket Analyst

Built on top of our clean data model, Qurie lets your business users self-serve. Ask plain-English questions—like "Why did the pipeline drop last week?"—and get instant, visualized answers.

Why it matters:

Why it matters:

You are freed from ad-hoc report requests. Qurie cuts sales review prep time in half, and over 50% of user interactions shift away from dashboards to conversational queries.

Self-configuring AI that leverages the underlying data model.

Business-native and context-aware.

Action-oriented—generates charts, reports, and workflows.

Revenue Intelligence Dashboards

Revenue Intelligence

Dashboards

Revenue Intelligence

Dashboards

Dashboards That Don't Lie

Because our dashboards are built on the same governed, time-versioned data model as Qurie and your AI Agents, they are finally reliable. Every number is backed by confidence scoring and full lineage.

Why it matters:

Why it matters:

You can finally trust your dashboards. Your team will spend less time in dashboards and more time acting on insights, driving faster, better business outcomes.

Built on a foundation where every field carries a confidence score.

Consistent truth that reduces argument time in reviews.

Designed to be an "answer," not a "project."

LAYER 4: THE ACTION (ACT & EXTEND)

LAYER 4: THE ACTION (ACT & EXTEND)

AI Agents

Agents That Work for You

We provide 10+ pre-built proactive agents (like Revenue Risk & Rescue, Lead Revival) that run quietly to flag risks and suggest next moves, ensuring your GTM teams act faster, turning insights into decisive, profitable actions immediately.

Why it matters:

Why it matters:

This is where you unlock revenue. Our agents catch at-risk deals and revive cold leads. You can also build, test, and deploy your own custom agents tailored to your business.

10+ pre-built agents running autonomously to drive action.

Platform for Custom Agents: Use our foundation to build your own.

Closed-loop feedback system that learns from every outcome.

Reverse ETL

Close the Loop from Insight to Action

This is your "Action API." The Reverse ETL layer pushes AI-driven recommendations, clean data, and workflows directly into your operational tools—CRM, Slack, Outreach—so insights don't just sit in a report.

Why it matters:

Why it matters:

Reverse ETL ensures your single source of truth flows back into your daily tools. AI-powered lead scores sync to Salesforce. This closes the loop and makes your clean data actionable.

Embeds agent recommendations directly into daily workflows.

Triggers immediate actions in tools your team already uses.

Tracks outcomes to improve recommendation accuracy over time.

Impact

Impact

Impact

Impact

From Architecture to Action: See the Platform at Work

From Architecture to Action: See the Platform at Work

See how technical leaders use DataviCloud's platform blocks to solve critical business problems and enable their teams, driving efficiency and measurable revenue growth.

Enabling a Clean Pipeline

The Business Problem:

An HR tech firm was running on a dirty pipeline. Duplicate records, conflicting data sources, and zero trust in their forecast accuracy.

The Platform Solution (How You Fix It):

Your RevOps Analyst uses DataviCloud to connect CRM, billing, and product data. They define custom uniqueness rules, allowing the Data Hygiene module to cross-validate, block bad rows, and quarantine duplicates at the source.

The Business Result:

DataviCloud cut duplicates by 30% in the first month. You've enabled a clean pipeline, and the sales team finally trusts the numbers in their forecast.

Enabling a Clean Pipeline

The Business Problem:

An HR tech firm was running on a dirty pipeline. Duplicate records, conflicting data sources, and zero trust in their forecast accuracy.

The Platform Solution (How You Fix It):

Your RevOps Analyst uses DataviCloud to connect CRM, billing, and product data. They define custom uniqueness rules, allowing the Data Hygiene module to cross-validate, block bad rows, and quarantine duplicates at the source.

The Business Result:

DataviCloud cut duplicates by 30% in the first month. You've enabled a clean pipeline, and the sales team finally trusts the numbers in their forecast.

Enabling a Clean Pipeline

The Business Problem:

An HR tech firm was running on a dirty pipeline. Duplicate records, conflicting data sources, and zero trust in their forecast accuracy.

The Platform Solution (How You Fix It):

Your RevOps Analyst uses DataviCloud to connect CRM, billing, and product data. They define custom uniqueness rules, allowing the Data Hygiene module to cross-validate, block bad rows, and quarantine duplicates at the source.

The Business Result:

DataviCloud cut duplicates by 30% in the first month. You've enabled a clean pipeline, and the sales team finally trusts the numbers in their forecast.

Enabling Proactive Deal Rescue

The Business Problem:

A Sales Manager is staring at a $200K deal that's gone quiet. Fewer product logins. Missed meetings. Radio silence from the champion.

The Platform Solution (How You Fix It):

Your GTM Engineer leverages the Time-Versioned ID Graph. They configure the "Revenue Risk & Rescue" Agent to watch engagement drops, which triggers a workflow: update forecast risk, create task, and push a Slack alert.

The Business Result:

The rep gets a clear, specific next action, delivered in real-time. The deal gets back on track, and you've successfully used the platform to prevent revenue leak.

Enabling Proactive Deal Rescue

The Business Problem:

A Sales Manager is staring at a $200K deal that's gone quiet. Fewer product logins. Missed meetings. Radio silence from the champion.

The Platform Solution (How You Fix It):

Your GTM Engineer leverages the Time-Versioned ID Graph. They configure the "Revenue Risk & Rescue" Agent to watch engagement drops, which triggers a workflow: update forecast risk, create task, and push a Slack alert.

The Business Result:

The rep gets a clear, specific next action, delivered in real-time. The deal gets back on track, and you've successfully used the platform to prevent revenue leak.

Enabling Proactive Deal Rescue

The Business Problem:

A Sales Manager is staring at a $200K deal that's gone quiet. Fewer product logins. Missed meetings. Radio silence from the champion.

The Platform Solution (How You Fix It):

Your GTM Engineer leverages the Time-Versioned ID Graph. They configure the "Revenue Risk & Rescue" Agent to watch engagement drops, which triggers a workflow: update forecast risk, create task, and push a Slack alert.

The Business Result:

The rep gets a clear, specific next action, delivered in real-time. The deal gets back on track, and you've successfully used the platform to prevent revenue leak.

Enabling Self-Serve Analytics

The Business Problem:

A CRO asks, "What's our pipeline coverage for Q3?" forcing your analyst team to pull a manual report.

The Platform Solution (How You Fix It):

Your RevOps team defines "Pipeline Coverage" once in the central Data Dictionary. Because Qurie is built on this governed semantic layer, when the CRO asks the question in plain English, they get an instant, accurate answer.

The Business Result:

You've eliminated ad-hoc report requests. Prep time for sales reviews is cut in half, and leaders get answers in seconds, not hours—all because you built the metric correctly once.

Enabling Self-Serve Analytics

The Business Problem:

A CRO asks, "What's our pipeline coverage for Q3?" forcing your analyst team to pull a manual report.

The Platform Solution (How You Fix It):

Your RevOps team defines "Pipeline Coverage" once in the central Data Dictionary. Because Qurie is built on this governed semantic layer, when the CRO asks the question in plain English, they get an instant, accurate answer.

The Business Result:

You've eliminated ad-hoc report requests. Prep time for sales reviews is cut in half, and leaders get answers in seconds, not hours—all because you built the metric correctly once.

Enabling Self-Serve Analytics

The Business Problem:

A CRO asks, "What's our pipeline coverage for Q3?" forcing your analyst team to pull a manual report.

The Platform Solution (How You Fix It):

Your RevOps team defines "Pipeline Coverage" once in the central Data Dictionary. Because Qurie is built on this governed semantic layer, when the CRO asks the question in plain English, they get an instant, accurate answer.

The Business Result:

You've eliminated ad-hoc report requests. Prep time for sales reviews is cut in half, and leaders get answers in seconds, not hours—all because you built the metric correctly once.

Enabling Custom App Development (Like LEO)
Enabling Custom App Development (Like LEO)

Enabling Custom App Development (Like LEO)

The Business Problem:

Your team needs a custom lead enrichment and multi-channel outreach app. Your options are buying another rigid tool (more tool sprawl) or a 6-month custom engineering project.

The Platform Solution (How You Fix It):

Your GTM Engineer uses the DataviCloud platform as a foundation. They leverage unified data and Reverse ETL to build a custom application (like LEO) that enriches leads and triggers outreach on a single, governed platform.

The Business Result:

You've solved a complex business need in a fraction of the time, without adding another tool to the stack. You used the platform to consolidate, customize, and deliver a new, high-value capability.

Enabling Custom App Development (Like LEO)

Enabling Custom App Development (Like LEO)

The Business Problem:

Your team needs a custom lead enrichment and multi-channel outreach app. Your options are buying another rigid tool (more tool sprawl) or a 6-month custom engineering project.

The Platform Solution (How You Fix It):

Your GTM Engineer uses the DataviCloud platform as a foundation. They leverage unified data and Reverse ETL to build a custom application (like LEO) that enriches leads and triggers outreach on a single, governed platform.

The Business Result:

You've solved a complex business need in a fraction of the time, without adding another tool to the stack. You used the platform to consolidate, customize, and deliver a new, high-value capability.

Enabling Custom App Development (Like LEO)

Enabling Custom App Development (Like LEO)

The Business Problem:

Your team needs a custom lead enrichment and multi-channel outreach app. Your options are buying another rigid tool (more tool sprawl) or a 6-month custom engineering project.

The Platform Solution (How You Fix It):

Your GTM Engineer uses the DataviCloud platform as a foundation. They leverage unified data and Reverse ETL to build a custom application (like LEO) that enriches leads and triggers outreach on a single, governed platform.

The Business Result:

You've solved a complex business need in a fraction of the time, without adding another tool to the stack. You used the platform to consolidate, customize, and deliver a new, high-value capability.

Strategy

Strategy

Strategy

Strategy

Don't Buy Rigid. Don't Build Raw.

Don't Buy Rigid. Don't Build Raw.

Don't Buy Rigid. Don't Build Raw.

You're trapped between two bad choices: rigid black boxes or raw DIY infrastructure. DataviCloud is the third, better option: an extensible, GTM-native platform that frees you.

vs. Point Solutions (The Black Box)

They're "bolt-on AI on messy data." Rigid, single-use tools that don't fix your architecture. DataviCloud unifies your data first, then deploys agents and apps on a trusted foundation, delivering reliable results and eliminating the need for constant reconciliation.

vs. Point Solutions (The Black Box)

They're "bolt-on AI on messy data." Rigid, single-use tools that don't fix your architecture. DataviCloud unifies your data first, then deploys agents and apps on a trusted foundation, delivering reliable results and eliminating the need for constant reconciliation.

vs. Point Solutions (The Black Box)

They're "bolt-on AI on messy data." Rigid, single-use tools that don't fix your architecture. DataviCloud unifies your data first, then deploys agents and apps on a trusted foundation, delivering reliable results and eliminating the need for constant reconciliation.

vs. Point Solutions (The Black Box)

They're "bolt-on AI on messy data." Rigid, single-use tools that don't fix your architecture. DataviCloud unifies your data first, then deploys agents and apps on a trusted foundation, delivering reliable results and eliminating the need for constant reconciliation.

vs. Legacy Platforms (Blank Slate)

This is raw infrastructure requiring massive engineering. DataviCloud is the platform in the middle. We provide the GTM-native data model, metric layer, and action layer out of the box, so you can build custom solutions in days, not quarters.

The Consolidation Narrative

Your team is juggling 38+ tools, and you are the one paying the "integration tax" with manual, brittle scripts. DataviCloud replaces that manual labor. It acts as the central connective tissue for your stack. We don't add another tool—we make your stack finally work as one.

FAQ

FAQ

FAQ

FAQ

Frequently Asked Questions

Frequently Asked Questions

Frequently Asked Questions

Frequently Asked Questions

Is DataviCloud a CDP, a BI Tool, or a Data Warehouse?

We are the GTM Data Operating System built on a true data platform. Unlike a raw data warehouse (Snowflake/BigQuery) or a generic BI tool, we provide the GTM-native data model, time-versioned ID Graph, and Metric Layer out-of-the-box. We act as the connective tissue that creates a single source of truth for all revenue data, enabling an immediate Action Layer on top of your existing tech stack.

Is DataviCloud a CDP, a BI Tool, or a Data Warehouse?

We are the GTM Data Operating System built on a true data platform. Unlike a raw data warehouse (Snowflake/BigQuery) or a generic BI tool, we provide the GTM-native data model, time-versioned ID Graph, and Metric Layer out-of-the-box. We act as the connective tissue that creates a single source of truth for all revenue data, enabling an immediate Action Layer on top of your existing tech stack.

Is DataviCloud a CDP, a BI Tool, or a Data Warehouse?

We are the GTM Data Operating System built on a true data platform. Unlike a raw data warehouse (Snowflake/BigQuery) or a generic BI tool, we provide the GTM-native data model, time-versioned ID Graph, and Metric Layer out-of-the-box. We act as the connective tissue that creates a single source of truth for all revenue data, enabling an immediate Action Layer on top of your existing tech stack.

Is DataviCloud a CDP, a BI Tool, or a Data Warehouse?

We are the GTM Data Operating System built on a true data platform. Unlike a raw data warehouse (Snowflake/BigQuery) or a generic BI tool, we provide the GTM-native data model, time-versioned ID Graph, and Metric Layer out-of-the-box. We act as the connective tissue that creates a single source of truth for all revenue data, enabling an immediate Action Layer on top of your existing tech stack.

What is the "Time-Versioned ID Graph" and why is it critical?

The Time-Versioned ID Graph is the core of our Data Modelling Module. It uses deterministic and probabilistic matching to resolve identities (Account/Contact/Lead) from disparate GTM systems (CRM, product usage, billing) into a single, unified record. Time-versioning maintains an immutable, historical record of every change, eliminating data drift and ensuring your AI Agents and historical reports are perfectly accurate. This is the foundation for trustworthy GTM insights.

What is the "Time-Versioned ID Graph" and why is it critical?

The Time-Versioned ID Graph is the core of our Data Modelling Module. It uses deterministic and probabilistic matching to resolve identities (Account/Contact/Lead) from disparate GTM systems (CRM, product usage, billing) into a single, unified record. Time-versioning maintains an immutable, historical record of every change, eliminating data drift and ensuring your AI Agents and historical reports are perfectly accurate. This is the foundation for trustworthy GTM insights.

What is the "Time-Versioned ID Graph" and why is it critical?

The Time-Versioned ID Graph is the core of our Data Modelling Module. It uses deterministic and probabilistic matching to resolve identities (Account/Contact/Lead) from disparate GTM systems (CRM, product usage, billing) into a single, unified record. Time-versioning maintains an immutable, historical record of every change, eliminating data drift and ensuring your AI Agents and historical reports are perfectly accurate. This is the foundation for trustworthy GTM insights.

What is the "Time-Versioned ID Graph" and why is it critical?

The Time-Versioned ID Graph is the core of our Data Modelling Module. It uses deterministic and probabilistic matching to resolve identities (Account/Contact/Lead) from disparate GTM systems (CRM, product usage, billing) into a single, unified record. Time-versioning maintains an immutable, historical record of every change, eliminating data drift and ensuring your AI Agents and historical reports are perfectly accurate. This is the foundation for trustworthy GTM insights.

How does your Data Hygiene module prevent "Garbage In, Garbage Out"?

DataviCloud fixes the GTM "trust deficit" at the source. Our Data Hygiene engine applies Quality Gates and Schema Contracts at the point of ingestion to block bad data. Crucially, every field in the ID Graph receives a Confidence Score based on freshness and cross-validation against trusted sources, ensuring your teams only act on vetted, reliable insights.

How does your Data Hygiene module prevent "Garbage In, Garbage Out"?

DataviCloud fixes the GTM "trust deficit" at the source. Our Data Hygiene engine applies Quality Gates and Schema Contracts at the point of ingestion to block bad data. Crucially, every field in the ID Graph receives a Confidence Score based on freshness and cross-validation against trusted sources, ensuring your teams only act on vetted, reliable insights.

How does your Data Hygiene module prevent "Garbage In, Garbage Out"?

DataviCloud fixes the GTM "trust deficit" at the source. Our Data Hygiene engine applies Quality Gates and Schema Contracts at the point of ingestion to block bad data. Crucially, every field in the ID Graph receives a Confidence Score based on freshness and cross-validation against trusted sources, ensuring your teams only act on vetted, reliable insights.

How does your Data Hygiene module prevent "Garbage In, Garbage Out"?

DataviCloud fixes the GTM "trust deficit" at the source. Our Data Hygiene engine applies Quality Gates and Schema Contracts at the point of ingestion to block bad data. Crucially, every field in the ID Graph receives a Confidence Score based on freshness and cross-validation against trusted sources, ensuring your teams only act on vetted, reliable insights.

How does DataviCloud handle data security and compliance?

Data security is paramount. We are SOC 2 Type 2 compliant. All data is encrypted both in transit and at rest. You maintain granular control over access. Our architecture supports global compliance standards (like GDPR/CCPA) by allowing you to manage deletion and preference requests against the unified, cross-platform identity layer.

How does DataviCloud handle data security and compliance?

Data security is paramount. We are SOC 2 Type 2 compliant. All data is encrypted both in transit and at rest. You maintain granular control over access. Our architecture supports global compliance standards (like GDPR/CCPA) by allowing you to manage deletion and preference requests against the unified, cross-platform identity layer.

How does DataviCloud handle data security and compliance?

Data security is paramount. We are SOC 2 Type 2 compliant. All data is encrypted both in transit and at rest. You maintain granular control over access. Our architecture supports global compliance standards (like GDPR/CCPA) by allowing you to manage deletion and preference requests against the unified, cross-platform identity layer.

How does DataviCloud handle data security and compliance?

Data security is paramount. We are SOC 2 Type 2 compliant. All data is encrypted both in transit and at rest. You maintain granular control over access. Our architecture supports global compliance standards (like GDPR/CCPA) by allowing you to manage deletion and preference requests against the unified, cross-platform identity layer.

How is your platform truly "extensible"? Can we build custom logic?

Yes, that's our core differentiator. We provide the foundational strength so you can stop writing brittle scripts and start building. You can:

1) Define all custom business logic in the central Metric Layer and Data Dictionary.

2) Leverage the clean, unified data via API to build and deploy proprietary AI Agents and Custom Applications (like LEO) directly on our platform. We provide the foundation; you build the competitive edge.

How is your platform truly "extensible"? Can we build custom logic?

Yes, that's our core differentiator. We provide the foundational strength so you can stop writing brittle scripts and start building. You can:

1) Define all custom business logic in the central Metric Layer and Data Dictionary.

2) Leverage the clean, unified data via API to build and deploy proprietary AI Agents and Custom Applications (like LEO) directly on our platform. We provide the foundation; you build the competitive edge.

How is your platform truly "extensible"? Can we build custom logic?

Yes, that's our core differentiator. We provide the foundational strength so you can stop writing brittle scripts and start building. You can:

1) Define all custom business logic in the central Metric Layer and Data Dictionary.

2) Leverage the clean, unified data via API to build and deploy proprietary AI Agents and Custom Applications (like LEO) directly on our platform. We provide the foundation; you build the competitive edge.

How is your platform truly "extensible"? Can we build custom logic?

Yes, that's our core differentiator. We provide the foundational strength so you can stop writing brittle scripts and start building. You can:

1) Define all custom business logic in the central Metric Layer and Data Dictionary.

2) Leverage the clean, unified data via API to build and deploy proprietary AI Agents and Custom Applications (like LEO) directly on our platform. We provide the foundation; you build the competitive edge.

Does DataviCloud require us to move our CRM or data warehouse?

No, we augment, not replace. We are designed to sit on top of your existing data infrastructure. We pull data from 400+ sources, perform the critical tasks of unification, hygiene, and modeling, and then push clean data, scores, and AI-driven actions back into your operational tools (CRM, Slack, Outreach) via the Reverse ETL module.

Does DataviCloud require us to move our CRM or data warehouse?

No, we augment, not replace. We are designed to sit on top of your existing data infrastructure. We pull data from 400+ sources, perform the critical tasks of unification, hygiene, and modeling, and then push clean data, scores, and AI-driven actions back into your operational tools (CRM, Slack, Outreach) via the Reverse ETL module.

Does DataviCloud require us to move our CRM or data warehouse?

No, we augment, not replace. We are designed to sit on top of your existing data infrastructure. We pull data from 400+ sources, perform the critical tasks of unification, hygiene, and modeling, and then push clean data, scores, and AI-driven actions back into your operational tools (CRM, Slack, Outreach) via the Reverse ETL module.

Does DataviCloud require us to move our CRM or data warehouse?

No, we augment, not replace. We are designed to sit on top of your existing data infrastructure. We pull data from 400+ sources, perform the critical tasks of unification, hygiene, and modeling, and then push clean data, scores, and AI-driven actions back into your operational tools (CRM, Slack, Outreach) via the Reverse ETL module.

We need to define 'MQL' differently across three regions. Can you support that?

Yes. Our Data Dictionary and Metric Layer allows you to centrally define and govern complex KPIs like MQL, PQL, and Pipeline Coverage. You can apply dimensional logic to these definitions, ensuring that 'MQL' is defined consistently, but can be sliced and filtered by attributes like Region, Product Line, or Vertical—ending the data debates across the organization.

We need to define 'MQL' differently across three regions. Can you support that?

Yes. Our Data Dictionary and Metric Layer allows you to centrally define and govern complex KPIs like MQL, PQL, and Pipeline Coverage. You can apply dimensional logic to these definitions, ensuring that 'MQL' is defined consistently, but can be sliced and filtered by attributes like Region, Product Line, or Vertical—ending the data debates across the organization.

We need to define 'MQL' differently across three regions. Can you support that?

Yes. Our Data Dictionary and Metric Layer allows you to centrally define and govern complex KPIs like MQL, PQL, and Pipeline Coverage. You can apply dimensional logic to these definitions, ensuring that 'MQL' is defined consistently, but can be sliced and filtered by attributes like Region, Product Line, or Vertical—ending the data debates across the organization.

We need to define 'MQL' differently across three regions. Can you support that?

Yes. Our Data Dictionary and Metric Layer allows you to centrally define and govern complex KPIs like MQL, PQL, and Pipeline Coverage. You can apply dimensional logic to these definitions, ensuring that 'MQL' is defined consistently, but can be sliced and filtered by attributes like Region, Product Line, or Vertical—ending the data debates across the organization.

How does your AI (Agents, AutoML) improve accuracy over time?

We operate a closed-loop feedback system. Every action—whether a deal risk alert from an AI Agent or a change in a lead score—is tracked for its measurable outcome. This outcome data (e.g., did the deal close or was the lead revived?) is fed back into the AutoML Module, which automatically updates the model weights and playbooks. This drives compounding accuracy and makes your entire GTM engine smarter with every action taken.

How does your AI (Agents, AutoML) improve accuracy over time?

We operate a closed-loop feedback system. Every action—whether a deal risk alert from an AI Agent or a change in a lead score—is tracked for its measurable outcome. This outcome data (e.g., did the deal close or was the lead revived?) is fed back into the AutoML Module, which automatically updates the model weights and playbooks. This drives compounding accuracy and makes your entire GTM engine smarter with every action taken.

How does your AI (Agents, AutoML) improve accuracy over time?

We operate a closed-loop feedback system. Every action—whether a deal risk alert from an AI Agent or a change in a lead score—is tracked for its measurable outcome. This outcome data (e.g., did the deal close or was the lead revived?) is fed back into the AutoML Module, which automatically updates the model weights and playbooks. This drives compounding accuracy and makes your entire GTM engine smarter with every action taken.

How does your AI (Agents, AutoML) improve accuracy over time?

We operate a closed-loop feedback system. Every action—whether a deal risk alert from an AI Agent or a change in a lead score—is tracked for its measurable outcome. This outcome data (e.g., did the deal close or was the lead revived?) is fed back into the AutoML Module, which automatically updates the model weights and playbooks. This drives compounding accuracy and makes your entire GTM engine smarter with every action taken.

What is the difference between your Revenue Intelligence Dashboards and a tool like Tableau/Looker?

The difference is the foundation. Generic BI dashboards are often built on raw, untrusted data, leading to conflicting reports. Our dashboards are built directly on the governed, time-versioned ID Graph and Metric Layer. Every number is consistent, trustworthy, and backed by a field-level Confidence Score, meaning they are finally an answer, not a project.

What is the difference between your Revenue Intelligence Dashboards and a tool like Tableau/Looker?

The difference is the foundation. Generic BI dashboards are often built on raw, untrusted data, leading to conflicting reports. Our dashboards are built directly on the governed, time-versioned ID Graph and Metric Layer. Every number is consistent, trustworthy, and backed by a field-level Confidence Score, meaning they are finally an answer, not a project.

What is the difference between your Revenue Intelligence Dashboards and a tool like Tableau/Looker?

The difference is the foundation. Generic BI dashboards are often built on raw, untrusted data, leading to conflicting reports. Our dashboards are built directly on the governed, time-versioned ID Graph and Metric Layer. Every number is consistent, trustworthy, and backed by a field-level Confidence Score, meaning they are finally an answer, not a project.

What is the difference between your Revenue Intelligence Dashboards and a tool like Tableau/Looker?

The difference is the foundation. Generic BI dashboards are often built on raw, untrusted data, leading to conflicting reports. Our dashboards are built directly on the governed, time-versioned ID Graph and Metric Layer. Every number is consistent, trustworthy, and backed by a field-level Confidence Score, meaning they are finally an answer, not a project.

How does DataviCloud stop us from being a RevOps/Data Bottleneck?

We free you in two ways:

1) Qurie (Conversational Analytics) allows business users to self-serve answers to complex questions ("Why did pipeline drop?") in plain English, eliminating ad-hoc report requests.

2) The platform's extensibility lets you build and deploy automated, proactive AI Agents that solve problems (like deal risk) autonomously, shifting your role from reactive reporter to strategic enabler.

How does DataviCloud stop us from being a RevOps/Data Bottleneck?

We free you in two ways:

1) Qurie (Conversational Analytics) allows business users to self-serve answers to complex questions ("Why did pipeline drop?") in plain English, eliminating ad-hoc report requests.

2) The platform's extensibility lets you build and deploy automated, proactive AI Agents that solve problems (like deal risk) autonomously, shifting your role from reactive reporter to strategic enabler.

How does DataviCloud stop us from being a RevOps/Data Bottleneck?

We free you in two ways:

1) Qurie (Conversational Analytics) allows business users to self-serve answers to complex questions ("Why did pipeline drop?") in plain English, eliminating ad-hoc report requests.

2) The platform's extensibility lets you build and deploy automated, proactive AI Agents that solve problems (like deal risk) autonomously, shifting your role from reactive reporter to strategic enabler.

How does DataviCloud stop us from being a RevOps/Data Bottleneck?

We free you in two ways:

1) Qurie (Conversational Analytics) allows business users to self-serve answers to complex questions ("Why did pipeline drop?") in plain English, eliminating ad-hoc report requests.

2) The platform's extensibility lets you build and deploy automated, proactive AI Agents that solve problems (like deal risk) autonomously, shifting your role from reactive reporter to strategic enabler.

CUSTOMER STORIES

CUSTOMER STORIES

CUSTOMER STORIES

CUSTOMER STORIES

  • Shashidhar
    Shashidhar
    Shashidhar

    Shashidhar,

    Product Marketing Leader, Keka

    Product Marketing Leader, Keka

    "DataviCloud helped us turn product usage into real revenue impact."

    Before using DataviCloud, it was challenging to identify top accounts based on product usage for our Product and Product Marketing teams to focus on the right adoption initiatives and pinpoint common pain points.


    Another challenge was that we didn't have a structured approach for account farming. Since partnering with DataviCloud, we've implemented a clear, usage-based scoring model that segments insights by industry, company size, and more.


    The farming framework we co-developed with their team has enabled us to grow key accounts with greater confidence and precision. It also had a direct and positive impact on our revenue growth. Product usage insights are a goldmine for any PMM. Huge thanks to the DataviCloud team for helping us unlock these and scale smarter."

  • Jing Z.,

    Data Lead at wati

    I feel like the Data team now has a seat at the Strategy table.”

    Before DataviCloud, we struggled with fragmented data and unclear metric definitions, which made it hard to get stakeholder buy-in.


    Now, we’ve got a single source of truth, which means stable historical data and well-aligned processes. It’s made a huge difference to how we answer key questions and support teams like Product, Marketing, and Finance.


    Plus, the integration with tools like HubSpot and our new self-serve options have been a game changer in terms of effort/efficiency.

  • Jing Z.,

    Data Lead at wati

    I feel like the Data team now has a seat at the Strategy table.”

    Before DataviCloud, we struggled with fragmented data and unclear metric definitions, which made it hard to get stakeholder buy-in.


    Now, we’ve got a single source of truth, which means stable historical data and well-aligned processes. It’s made a huge difference to how we answer key questions and support teams like Product, Marketing, and Finance.


    Plus, the integration with tools like HubSpot and our new self-serve options have been a game changer in terms of effort/efficiency.

  • jing

    Jing Z.,

    Data Lead at Wati

    “I feel like the Data team now has a seat at the Strategy table.”

    Before DataviCloud, we struggled with fragmented data and unclear metric definitions, which made it hard to get stakeholder buy-in.


    Now, we’ve got a single source of truth, which means stable historical data and well-aligned processes. It’s made a huge difference to how we answer key questions and support teams like Product, Marketing, and Finance.


    Plus, the integration with tools like HubSpot and our new self-serve options have been a game changer in terms of effort/efficiency.

Action

Ready to Activate Your Revenue?

Stop leaking revenue and guessing at your forecast. Talk to an expert today to identify which DataviCloud solutions can have the biggest, fastest impact on your GTM motion.

Action

Ready to Activate Your Revenue?

Stop leaking revenue and guessing at your forecast. Talk to an expert today to identify which DataviCloud solutions can have the biggest, fastest impact on your GTM motion.

Action

Ready to Activate Your Revenue?

Stop leaking revenue and guessing at your forecast. Talk to an expert today to identify which DataviCloud solutions can have the biggest, fastest impact on your GTM motion.

Action

Ready to Activate Your Revenue?

Stop leaking revenue and guessing at your forecast. Talk to an expert today to identify which DataviCloud solutions can have the biggest, fastest impact on your GTM motion.

See you in your inbox?

Ideas, frameworks and updates to help you get real intelligence out of your data.

GTM Operating System powering faster growth and smarter wins.

© 2025 Datavi Cloud, Inc. All right reserved

See you in your inbox?

Ideas, frameworks and updates to help you get real intelligence out of your data.

GTM Operating System powering faster growth and smarter wins.

© 2025 Datavi Cloud, Inc. All right reserved

See you in your inbox?

Ideas, frameworks and updates to help you get real intelligence out of your data.

GTM Operating System powering faster growth and smarter wins.

© 2025 Datavi Cloud, Inc. All right reserved

See you in your inbox?

Ideas, frameworks and updates to help you get real intelligence out of your data.

GTM Operating System powering faster growth and smarter wins.

© 2025 Datavi Cloud, Inc. All right reserved