Capability 01 · Scalable context delivery
Scale across schemas, databases, users, and agents.
LangGrant scales in every direction that matters in production: schemas with hundreds of tables that exceed any model’s context window, joins spanning multiple databases of different engines, and thousands of concurrent users and autonomous agents hitting the same governed interface.
Analyst recognition
Named by Gartner for Machine Learning, Data & Analytics.
The team behind LangGrant — Windocks — is cited in Gartner research on the data foundation enterprises use for AI and analytics workloads.
Gartner research
Recognized for ML, Data & Analytics
Gartner cites Windocks in research covering the data infrastructure layer that powers machine learning and analytics in regulated enterprises. LangGrant is built on that same foundation, extended to deliver scaled context directly to LLMs.
How it scales
Three directions of scale, addressed head-on.
Schema size, the number and variety of databases joined for a single answer, and the load of users and autonomous agents calling in concurrently — LangGrant is engineered for each.
01 · Context chunking
A patent-pending chunking algorithm keeps accuracy high when the LLM only sees fragments.
Production schemas routinely exceed model context windows. LangGrant chunks schema and metadata so the model is fed precisely the slice that matches the question, with the surrounding structure needed to interpret it correctly.
- Handles hundreds of tables and thousands of columns per database
- Cryptic column and table names are translated into LLM-readable descriptions
- Context caching for hot schemas, distributed scaling for the rest
- Accuracy compounds: every query enriches the context library for the next
02 · Cross-database joins
Joins across databases — the problem warehouses were built to solve, solved without a warehouse.
An entire industry exists because cross-system joins are hard: foreign keys are missing, the same entity has different IDs in different systems, types don’t line up. LangGrant handles each case directly against the production databases — no ETL, no warehouse round-trip, no copy of the data into a vector store.
Exact-key
Primary / foreign keys where they exist, including composite keys across systems.
orders.customer_id = customers.idLookup table
Three-way joins via a mapping table when source and target use different identifier systems.
crm.acct ↔ map.crm_to_erp ↔ erp.accountFormula on column
Derived keys: substring, concatenation, date-bucket, type-coerced expressions on joined columns.
LEFT(account_id, 8) = legacy_acctFuzzy / Levenshtein
Name-similarity joins when “Acme Corp” lives elsewhere as “Acme Corporation.” Configurable threshold and tie-breaking.
name ≈ name (distance ≤ 2)Type-mismatch handling — string-versus-numeric IDs, date-format and timezone differences, locale variants — is applied automatically before the join runs.
03 · Concurrent users and agents
Many humans, many agents, one governed context layer.
A single LangGrant deployment serves analysts in the UI and autonomous agents over MCP from the same context layer. Hot-schema caches are shared across callers, live schema changes are detected and refreshed in place, and accuracy compounds across the whole user population.
- Distributed scaling for high concurrent query load
- Hot-schema context caches shared across every caller
- Automatic detection of schema changes — caches refresh in place, not on a schedule
- MCP server: your LangGrant agent or any MCP-compatible agent stack calls the same interface as humans
- Accuracy improves with usage — each answered query enriches the context library for everyone
Get started
Bring the question your warehouse can’t answer fast enough.
A schema that’s too big, two databases that don’t share keys, or an agent stack that needs the same governed interface humans use. In a working session we’ll connect LangGrant to your environment and answer it live.
- Schemas with hundreds of tables welcome
- Cross-database joins, no ETL required
- Concurrent users and agents over one MCP interface