Ask anything.
Get answers across your current production databases.
LangGrant connects powerful LLMs to your current production databases, including questions that span multiple systems, without data movement, semantic modeling, or waiting on engineering. Every query runs under column-level access control, and answers become reusable Agent Workflows your team can rerun. Works on production or a standby replica.
Supported databases
Built for enterprise SQL.
Context replaces modeling, pipelines, and training.
LangGrant delivers the right database context to an LLM at scale, controls what the model and your agents see, and turns every query into reusable intelligence that compounds.
Accurate answers across large, fragmented databases.
Patent-pending context chunking, context caching, and distributed scaling let LLMs work on schemas that exceed their context windows, across one database or many.
- Single or multi-database queries
- Automatic schema updates
- Infers missing relationships and complex joins
Govern what the LLM sees and what users get back.
Role-based access at the column level, policy-aligned context, and visibility into token usage, so AI on production data stays within enterprise boundaries.
- Column-level access (incl. PII)
- User and agent role alignment
- Token usage limits and visibility
Every answer becomes a reusable Agent Workflow. Stop paying the LLM twice.
The first-pass result is persisted as a structured JSON Agent Workflow, not opaque SQL. Similar questions run the saved Workflow directly, with no new tokens and no waiting. Every query also adds to the context library, so accuracy compounds with usage.
- Massive LLM cost savings on repeat questions
- Auditable, human-editable JSON, not SQL
- Each query enriches future answers
- Callable by humans and autonomous agents
Supported models
Works with the latest LLMs.
Backed by analysts. Trusted by global enterprises.
LangGrant is the new product from the team that built Windocks. We’ve earned Gartner recognition for database CI/CD and ML, data and analytics, and we’ve shipped into regulated industries from healthcare to insurance to global retail.
Named by Gartner for Database CI/CD
Our team is named in Gartner research for database CI/CD, through Windocks, the product we’ve been shipping for years. That same data-handling rigor is in LangGrant.
Named by Gartner for ML, Data & Analytics
We’re cited in Gartner research on ML, data and analytics. LangGrant is how that experience shows up for finance and AI/ML teams asking questions of production data.
The technology stack behind LangGrant: virtualization, test data, DevOps, migrations.
A working example: the FP&A questions that fire-drill the close, answered in seconds.
FP&A teams own variance, reconciliation, and “why is this number different” questions that span ERP, billing, and CRM. Today they wait on engineering or stitch answers from three reports. With LangGrant they just ask, and the first answer becomes a persisted Agent Workflow the team and their agents can run again, edit, and audit.
Finance asks in plain language
No dashboards, no semantic model, no engineering ticket. Any of the questions on the left is typed directly, and autonomous agents in your finance workflows can call the same interface.
LangGrant delivers tuned context to your LLM
The right schema slices, relationships, and access-controlled columns from ERP + billing + CRM are assembled and sent to your chosen model. Data stays in your databases.
A persisted Agent Workflow is generated and reviewed
Instead of opaque SQL, the model emits a structured JSON Agent Workflow that captures exactly how the answer was computed. It is persisted to your workspace, readable by finance, editable by humans, and far more reliably modifiable by an LLM than SQL.
// Variance vs. prior forecast · persisted Agent Workflow { "name": "Q-forecast variance, by component", "sources": ["erp.forecast", "erp.actuals", "billing.invoices"], "joins": [{ "on": "period, region", "type": "inner" }], "compute": [ { "id": "variance", "expr": "actuals - forecast" }, { "id": "contribution", "expr": "variance / SUM(variance)" } ], "access": { "role": "fpa_analyst", "pii": false }, "output": ["period", "region", "variance", "contribution"] }
Reuse next month, without re-paying the LLM
Next month’s variance question runs the saved Agent Workflow directly. No new token spend on a question you’ve already asked. Every saved Workflow also becomes part of the context library, so the system gets more accurate the more your team uses it.
No data movement. No modeling. No upfront work.
Most AI-on-data programs spend months moving data into a warehouse, naming columns, and tying questions to metrics. LangGrant delivers context directly to the LLM from the databases you already run.
| Capability | Traditional Stack | LangGrant |
|---|---|---|
| Time to first business answer | Months | Days |
| Data movement required | Yes, into warehouse | No, works on current production (or standby) |
| Semantic modeling & column mapping | Manual, ongoing | Not required |
| New question, new metric | Engineering ticket | Just ask |
| Joins across multiple databases | ETL pipelines first | Inferred and executed |
| Access control for AI | Bolted on | Column-level, role-aware |
| System improvement over time | Manual rework | Agent Workflows + context library compound |
| Repeat questions | Pay the LLM every time | Run the persisted Agent Workflow, no new tokens |
| Usable by autonomous agents | Custom integration per agent | Same governed interface as humans |
See LangGrant on your data in a working session.
Bring a real database and a real question. We’ll show you answers from your current production data (or a standby replica), the Agent Workflow that produced them, and how access control governs every step.
- ✓Connect to one of your existing databases. No copies, no exports.
- ✓Ask the question your business is currently waiting on.
- ✓Leave with a reviewed, reusable Agent Workflow your team and agents can run again.
Request a demo or download
A LangGrant engineer will respond within one business day.
- ✓Bring a real production question; we’ll show governed answers in seconds.
- ✓See the Agent Workflow that produced them, end to end.
- ✓Get the binary if you want to evaluate Windocks first.
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