LangGrant

Questions on your data? Get explainable answers across multiple databases—plus reusable Data Plans.

Data Plans automatically build and evolve your semantic layer, making AI reasoning across enterprise databases reusable, explainable, and governed.

Business Users

Multi-database reasoning, reused by people and AI

• Followups answered immediately — no waiting
• Plans show their work – shared, reviewable & aligned across teams
• Don’t pay the AI twice. Use AI only for plan changes
• Plans span multiple databases and auto-adapt as schemas evolve
• View token usage before using the model, against your token budget See business examples

Recognized by Gartner for Machine Learning, Data & Analytics

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AI Data Teams

Version, stage, and ship data plans — like code

• Versioned artifacts — diff, review, roll back
• Promote test → staging → approved with sign-off
• Run on demand or by API — reproducible every time
• Review and approve SQL & Python steps generated by the model Explore the architecture Already building AI data systems?

Snowflake · SQL Server · Oracle · PostgreSQL · BigQuery · Redshift · Azure SQL · Amazon Aurora & RDS · Databricks · Hive · MySql · Salesforce

The three C’s

Reasoning you keep pays off three ways.

Competitive advantage

Build organizational intelligence

Every Data Plan adds to a governed, growing library of how your business actually reasons about its data. That compounding intelligence becomes an asset competitors can’t copy — not a thread that disappears when the chat closes.

Collaboration

Reason together, not alone

AI nudges everyone toward working in isolation — each person “loop-engineering” their own throwaway prompts. A Data Plan gives humans a shared object to review, correct, and build on together, so reasoning is aligned across the team instead of locked in private chats.

Cost reduction

Reuse reasoning, don’t pay AI twice

Plans run once and are reused by people and AI alike. Follow-ups pay only for the incremental change — not a full re-analysis every time, cutting the compute, tokens, and analyst hours spent regenerating work you already have.

Why Data Plans

Answers are the wrong unit of analysis.

AI systems are built to give you answers. But an answer is a dead end — a disposable output. You can’t see the steps behind it, reuse them, or hand them to someone else.

A Data Plan keeps the steps used to compute the answer, persisted so they can be reused, reviewed, or run again by anyone — human and AI.

And it lets teams collaborate on a shared set of steps — the way you never could on an answer.

The shift

Analysis is not an answer. It’s a reusable object.

A Data Plan captures how the analysis is produced — the exact steps behind the answer — so it can be reused, reviewed, and run again by anyone, human or AI.

What changes

No re-running AI for every question

No hidden logic buried in prompts or SQL

No disposable, throwaway analysis

Instead

One Data Plan — reused, governed, and improved by the whole team.

The new unit of analysis

Not answers Not dashboards Not queries

Data Plans are the unit of reusable, AI-generated analysis.

For business users

Ask, get the answer, dig deeper — then keep the plan.

Example questions

What teams actually ask

Cross-system, ad-hoc, not in any dashboard.

• ?Why is gross margin down this quarter — walk me through every driver.
• ?Is any single product over-discounted, and what is it costing us?
• ?Where does our billing report disagree with the settlement report, and by how much?
• ?Revenue variance vs. the same quarter last year, by region, product and discount tier.
• ?Which customers drove the change — and which are predicted to next quarter?

01

Ask in plain language

No dashboard, no semantic model, no engineering ticket. Type the question the way you’d say it — across as many databases as the answer needs.

02

Get the answer — with the why

LangGrant answers from your live data and shows how it got there: the sources, the steps, and the numbers behind each one. No black box.

03

Dig deeper, instantly

“Which product?” “Just the EMEA region?” “Versus last year?” Each follow-up builds on the last answer — you keep the thread.

04

Keep the plan — run it next quarter

The first answer is saved as a Data Plan. Run it again on demand, and it adapts automatically as your data changes — so you never rebuild the same question.

Trust the answer, because you can see the work.

See how it was reached

Every step is laid out in plain terms, with the figures behind it — the proof, not just the conclusion.

The approved numbers

Plans use the metric definitions your organization has agreed on, so “net revenue” means the same thing every time.

The same answer, on demand

Re-run a plan and get a current, consistent answer — useful for the monthly close, the board deck, or a quick gut-check.

The same pattern, across the business.

Sales

• Which deals slipped this quarter, by region and stage, and what changed?
• Why did this segment’s close rate drop from Q2 to Q3?
• Reconcile CRM bookings with revenue recognition — flag what doesn’t tie.

Customer Success

• Which accounts have new escalations, by ARR tier and product?
• Why are tickets up this week, and which categories are the outliers?
• What’s driving NPS variance by customer segment?

Operations

• Why did this metric spike, and which change came first?
• What’s different about this week’s pattern vs. last week’s?
• Which accounts saw the largest change in the last 30 days?

See it answer your questionBring a real database and a real question — we’ll answer it live.

For AI data teams

The engineers building agents, MCP tools and pipelines that answer data questions.

Ship data plans through the same pipeline you ship code.

You already version, review and promote code through stages before it reaches production. LangGrant gives the data plans your models propose the same pipeline — versioned artifacts, sign-off gates, and reproducible runs — so AI-generated data work becomes an asset your team owns and operates, not output that piles up.

How each plan earns its way to production.

Every stage above is backed by real lifecycle steps your team operates — the same way source code became a managed asset through DevOps. The Data Plan is the authoritative, versioned artifact that moves through review, validation and approval.

01 · Context

Metadata & context

Schemas, relationships, statistics, definitions, lineage and security attributes, collected continuously across your sources.

02 · Plan

AI planning engine

The model constructs a Data Plan that is mostly configuration that is expressable in JSON. Code is generated only if needed

03 · Version control

Versioned repository

Every plan is a versioned artifact — diff revisions, roll back, search and reuse. A growing library of trusted data logic your team owns.

04 · Review

Review & sign-off

Engineers, analysts and governance teams inspect and sign off on the plan — the gate that promotes it from test to staging.

05 · Validate

Policy validation

Every plan is checked against access rules, PII protection, approved sources and compliance — reject, modify, or route for approval.

06 · Execute

Run on demand or by API

Run an approved plan on demand, on a schedule, or via API — reproducible every time. The same plan recompiles for new engines as platforms evolve.

07 · Audit

Audit & lifecycle

Requests, plans, review decisions, validations, executions and edits are all recorded — a complete audit trail.

08 · Trusted

Trusted Data Plan

After review, validation and approval, a proposal becomes a Trusted Data Plan — reused across teams and improved over time.

The pattern is the point. Just as Infrastructure-as-Code and GitOps turned scripts into version-controlled, reviewable config, AI Data DevOps gives your team a pipeline to version, review, promote and run the data plans your models propose — a discipline you own end to end.

Already building this? LangGrant fits in.

LangGrant is a control plane, not another agent. It doesn’t replace the AI work you’ve started — it gives that work an artifact you can govern, reuse and trust.

“We already have an AI agent / product.”

Keep it. Your agent calls LangGrant’s tools and emits a Data Plan instead of disposable code. LangGrant governs, versions and reuses what your agents produce — not a competing agent.

“We’re building a semantic model, then migrating to a warehouse to query it.”

That’s a long project. LangGrant builds semantics automatically with each question and runs on your databases as-is — no migration required to get governed answers now. When the warehouse is ready, the same plans recompile to run there.

“We’re already building an MCP server and tools.”

Good — that’s exactly where LangGrant plugs in. Your MCP tools produce a versioned, reviewable Data Plan your pipeline can promote through stages — so the output of your MCP work is an asset you can govern, run by API and reuse.

“We’re building an ‘ask the database’ tool that generates SQL.”

That tool gives you an answer. LangGrant gives you a versioned plan you can review, promote through stages, and re-run by API — the SQL becomes a compiled artifact inside the plan, so your team manages the pipeline, not one-off queries.

See a Data Plan on your dataPoint it at a real database and watch a question become a versioned, promotable plan.

From the team behind Windocks

Backed by analysts. Trusted by global enterprises.

LangGrant is the new product from the team that built Windocks. We have earned Gartner recognition for database CI/CD and for ML, data and analytics, and we have shipped into regulated industries from healthcare to insurance to global retail.

Analyst recognition

Named by Gartner for Database CI/CD

Windocks is named in Gartner research for database continuous integration and deployment. That same rigor in handling production data is built into LangGrant.

Named by Gartner for ML, Data & Analytics

Windocks is cited in Gartner research on machine learning, data and analytics — the same data foundation LangGrant uses to answer questions directly from production systems.

The AI-ready myth

Your data is already AI-ready.

There are two camps. One says you must first get your data “AI ready” — consolidate it into a warehouse, model it by hand, then begin. We think that is backwards: point AI at the databases you already run, starting today.

What you’re told

Get your data AI ready first.

• Consolidate Oracle, SQL Server and Postgres into Snowflake or Databricks
• Hand-build a semantic model — then keep it in sync forever
• Budget quarters of migration and duplicate storage
• Start the AI work after all of that

What actually works

Use AI on the databases you have.

• Connect the databases you already run — Snowflake and Databricks included
• The semantic layer builds itself as Data Plans run
• Nothing copied or staged first — the data stays where it is
• First Data Plan today, not next quarter

No data movement

Plans reach into the databases you already run and join only the tables they need — live. Nothing is copied or staged into a warehouse first.

Plugs into your MCP tools

LangGrant’s tools bind your model to emit config. Existing MCP servers and agents call the same interface and get a Data Plan back.

Bring your own model

Use Claude, OpenAI or Gemini. The Data Plan is the authoritative artifact, so the model and execution engine can change without rewriting your logic.

We run on Snowflake and Databricks too. We just don’t make you move there first.

Connect the databases you already run

Snowflake

SQL Server

Oracle

PostgreSQL

Amazon Redshift

Azure SQL

Amazon Aurora

Amazon RDS

Oracle OCI

Google BigQuery

Apache Hive

Databricks

MySQL

Salesforce

… and more

Bring your own model

Claude

OpenAI

Google Gemini

See a Data Plan built on your data.

Bring a real database and a real question. In one session we’ll answer it from your data as a governed, reusable Data Plan — explainable for the people asking, and config the people governing can review.

• ✓Get the answer — and the why — from the databases you already run.
• ✓See one plan span multiple databases, joining only the tables it needs.
• ✓Re-run it free; follow-ups are charged only for the incremental change.

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