Colrows
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We build data AI agents for your enterprise.

Analytics, reporting, migration, pipeline RCA — any agent that needs governed access to your data. Built and handed over in 8 weeks.

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01 · The frame

Your data AI agents. Governed by construction.

The model is fine. The context is not. We compile your enterprise's meaning, access rules and join logic into governed infrastructure, then build the data agents that run on it.

The infrastructure

A semantic execution layer your agents compile through.

A versioned, typed semantic graph. Multi-vector embeddings per concept. A compile-then-execute pipeline that turns intent into dialect-perfect SQL with the join path proven and the governance predicates already inside it. Every agent and workflow inherits governed data access by construction.

The delivery

Engineers who build and ship it with you.

Forward-deployed engineers embedded in your team for a fixed-scope engagement. They stand up the infrastructure, build your first AI agents and workflows against real data, train your team, and hand over everything: agents, semantic graph, audit trail and runbooks.

02 · Ways to work with us

Three offers. One rule.

Every agent we build compiles through the execution layer. Compile-time governance and a reproducible audit trail, by construction. Everything ends in your hands.

01 · DATA AI AGENT DEPLOYMENT

Analytics agents, reporting agents, migration agents — governed from day one.

We build the governed data infrastructure and the data agents that run on it. Analytics agents that answer business questions, reporting agents that compile dashboards, migration agents that move data between systems, pipeline-RCA agents that diagnose failures. Every agent compiles through the execution layer, so RBAC, ABAC and row and column predicates are enforced before the SQL runs.

You keep: the agents, the graph, the traces

02 · AGENTIC DATA WORKFLOW BUILD

Multi-step data workflows that chain agents across your datastores.

A reporting agent pulls from three warehouses, a reconciliation agent cross-checks, and an alerting agent flags the variance — all in one governed workflow. Each step compiles through the execution layer, so each step inherits the right governance scope and the audit trail chains end to end.

You keep: the workflows and their runbooks

03 · DATA AI OPERATING ROADMAP

The sequenced plan to put data AI agents across your enterprise.

CDO-level sequencing for regulated enterprises: which business questions first, which datastores to wire, which agent types to deploy in which quarter, and what governance has to exist before any agent touches data. The roadmap that turns a pilot into an enterprise-wide data AI operating model.

You keep: a sequenced plan you can fund

03 · The engagement

Map. Build. Ship. Handover.

Four steps, and the fourth is the one that matters. A deployment that needs us to stay is a deployment we got wrong.

01

Map

Your datastores, your access model, your first three business questions. We scope the AI operating roadmap before we build anything.

Days, not months
02

Build

The semantic graph, the governance layer and the first data agents, compiled against real data with your team watching. Fixed-scope pilot.

Fixed scope · creditable
03

Ship

Agents in production on your cloud. Compile-time governance enforced on every query. Workflows running, drift detection watching.

Your cloud · your keys
04

Handover

Your team owns the agents, the semantic graph, the audit trail and the runbooks. The engineer leaves. Everything stays.

Yours to keep
04 · Week by week

What the engineer does in week one.

Unglamorous and specific, which is the point. No slideware. Real schema, real agents, real governance from the first week.

WhenWhat the engineer doesWhat you get
Week 1

Reads your schema. Builds the first scoped subgraph.

Real tables, real access model. Runs the first governed query against real data by the end of the week.

A working query you did not write.

Compiled, dialect-perfect SQL you can read, with the join path proof attached.

Week 4

Governance layer live. First data agents compiled.

RBAC, ABAC and row and column predicates injected into every agent query. Multi-vector embeddings tuned to your vocabulary. The first analytics and reporting agents running against real data.

Data agents your CDO can defend.

Every agent answer traceable to the SQL that produced it and the rules in force when it ran.

Week 8

Data agents and workflows in production. Drift detection on.

Autonomous maintenance watching the graph. Your team trained on the agents, the governance model and the versioned semantic graph.

Your data AI stack in production. Your team running it.

Audit trail live. Point-in-time reproducible from day one.

Handover

The engineer leaves.

Agents, workflows, the versioned graph, the audit trail and the runbooks are yours. No retainer required to keep the lights on.

Everything stays.

Optional: keep an engineer on retainer for the next set of agents, the next datastore or the next persona.

05 · Deployed. Measured.

What deployments have produced.

Colrows is deployed across pharma, retail and finance. The numbers below are from those deployments and from our first-party research.

8× data adoption uplift Cipla · Pharma
>95% reduction in evaluation cycle time Confidential ARC · BFSI
40% less data-management overhead SSP Group · Retail
98.2% text-to-SQL accuracy through the compiler First-party benchmark
06 · Honest scope

What you keep. What we don't do.

We build data AI agents and the governed infrastructure they run on. We are not a general-purpose consultancy.

After handover, you own

  • The data AI agents and workflows: in production, governed, with the audit trail attached.
  • The semantic graph: versioned, typed, multi-scope, readable by your team.
  • Every join path proof and every persona scope, as configuration, not tribal knowledge.
  • The audit trail: every agent query, the SQL it compiled to, and the rules in force when it ran. Point-in-time reproducible.
  • Runbooks for every agent type, the datastore connections, drift detection and the release cadence.
  • A team that has run the data agents and the infrastructure in production with the engineer beside them.

What we do not do

  • Build or migrate your warehouse. Colrows sits above Snowflake, Databricks, BigQuery and Redshift; it does not replace them.
  • Run your BI or your dashboards. Agents and the layer feed them; they do not own them.
  • Fine-tune models. The context is what we fix. The model is yours to choose, including bring-your-own on-premise.
  • Build agents that write their own SQL. Every agent compiles through the execution layer or we do not build it.
  • Stay indefinitely. Handover is a step in the plan with a date on it.
07 · Questions

Questions a CDO asks before signing.

Not answered here? Send us a message.

Is Colrows a product or a consultancy?

A product, plus the engineers who put it and your data AI agents into production. Colrows is a semantic execution layer that compiles enterprise intent into governed, deterministic SQL. Forward-deployed engineers build the governed infrastructure, build the data agents and workflows that run on it — analytics, reporting, migration, pipeline RCA — and hand everything over. Every engagement ends with the agents, the semantic graph, the audit trail and the runbooks in your hands, and the engineer leaving.

Who owns the semantic graph after handover?

You do. The semantic graph is versioned and typed, and it lives in your deployment. Definitions, join path proofs, persona scopes and governance predicates are all readable and yours to change. Nothing about the layer depends on a Colrows engineer staying on.

Do we have to keep a forward-deployed engineer after go-live?

No. Handover is a defined step, not an open-ended retainer. Autonomous maintenance and drift detection keep the graph current, and your team is trained to run it. Some customers keep an engineer on retainer for the next datastore or the next set of personas. That is optional.

Is the pilot fixed-price?

Yes. The pilot is fixed in scope and creditable against the platform subscription if you go to production. Colrows meters the natural-language requests your people and applications submit, never tokens, seats or CPUs, so the production figure is one committed annual number. Details are on the pricing page.

What kinds of data agents does Colrows build?

Any agent that needs governed access to your data. Analytics agents that answer business questions, reporting agents that compile dashboards, data-migration agents that move data between systems, and pipeline-RCA agents that diagnose failures. Every agent compiles its data access through the execution layer, so it inherits compile-time governance, dialect-perfect SQL and a point-in-time reproducible audit trail by construction. We do not build agents that write their own SQL.

Where does the engineer work, and where does the data stay?

Colrows runs inside your own cloud environment, on AWS, Google Cloud or Microsoft Azure, or on-premise. The data never leaves your perimeter. The engineer works with your team, on your systems, under your access controls, and only the question and the answer cross the boundary.

Data AI agents in production. Yours to keep.

Tell us the first three business questions you need data agents to answer and which datastores they live in. We will come back with a fixed-scope pilot.

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