Capability 03 · Reusable intelligence
Stop paying the LLM twice. Every answer is a reusable Data Plan.
LangGrant turns each LLM result into a structured JSON Data Plan — readable by humans, editable in the UI, callable by agents, and runnable again without spending new tokens. The system gets more accurate and more efficient the more your team uses it.
Recognized and deployed
Built by Windocks. Cited by Gartner. Running in enterprises across regulated industries.
LangGrant is built on the Windocks platform, used in production for database delivery and AI-ready data across healthcare, insurance, pharma, and global retail.
Cited for Database CI/CD
Gartner cites Windocks in research on database continuous integration and deployment — the discipline of versioning, persisting, and reusing data artifacts that LangGrant brings to LLM outputs.
Production deployments across regulated industries
The customers below run Windocks technology in production for database delivery and AI-ready data — the foundation LangGrant builds on for reusable, governed Data Plans.
How reuse works
Three properties of the Data Plan artifact.
AI projects that emit SQL leave their teams with throwaway artifacts: unreadable to the business, unreliably modified by the model, and re-billed on every repeat. LangGrant emits something better — and then prefers it on every following query.
01 · UI you can read, not code
Every answer becomes a Data Plan your business team can read, review, and change.
The Data Plan appears in the LangGrant UI as a sequence of labeled steps. Each step shows what it does in plain language, with its inputs and outputs laid out in a table you can scan in seconds. Anyone on the team can read it, sign off on it, edit a step, or delete one — no SQL, no JSON viewer, no developer in the loop.
- Each step named in plain English — describing what it does, not how it’s coded
- Edit or delete any step directly from its row — no code, no admin
- Add new steps from a button row alongside the existing ones
- Same screen for analyst, auditor, and finance lead — no separate review tool
02 · Reuse before re-billing
Existing Data Plans are checked first, so similar questions don’t burn new tokens.
When a new question comes in, LangGrant searches saved Data Plans for a match before calling the LLM. If one exists, it runs directly — same answer, near-zero new token spend. If none exists, the new Data Plan is persisted for next time.
- Match on intent and sources, not just literal text
- Run reused Data Plans in milliseconds, not seconds
- Edit a Data Plan once; every future caller gets the improvement
- Callable by humans in the UI and by autonomous agents over MCP
03 · Compounding context library
Every Data Plan enriches the context for the next question. Accuracy goes up with usage.
The Data Plan library is not just a cache — it’s the source of high-quality, usage-validated context that LangGrant feeds to the LLM when an entirely new question arrives. The more your team uses it, the more concise and accurate the context becomes.
- Data Plans annotate the schema with how it’s actually used
- Joins, aggregations, and naming conventions are reinforced by real queries
- Each reviewed Data Plan tightens the context the LLM sees on related questions
- Quality control is a UI review, not a model retraining cycle
Get started
Bring a recurring question. Leave with a Data Plan your team can rerun.
Pick a question your team or your business stakeholders ask every month — variance, reconciliation, cohort analysis. In a working session we’ll produce the Data Plan, walk through the JSON with you, and show how the next month’s run skips the LLM entirely.
- Generate a real Data Plan from a real question
- Review and edit the JSON in the UI
- Call the same Data Plan from your existing agents via MCP