LakeMind
Natural-language analytics over governed lakehouse systems.
Business questions still queue behind data workflows.
LakeMind is built for organizations where data exists, but answers still require SQL skill, dashboard backlog, or a data engineer in the loop.
IT becomes the query interface
Ad-hoc questions wait hours or days because business users cannot safely query governed data themselves.
Dashboards stop at known questions
Traditional BI works for prepared views, but spontaneous questions still fall back to manual analysis.
Sources stay fragmented
Operational databases, exports, and business systems need one governed analytical path before agents can answer reliably.
Generic AI lacks business context
LLMs need semantic profiles, policy boundaries, and validation loops to avoid confident but incorrect data answers.
A governed analytical console, not a prompt box.
The demo follows the real product journey: connect a source, sync tables, define semantics, ask in chat, inspect SQL, publish dashboards, and benchmark trust.
Four layers keep the agent grounded.
The system separates data movement, lakehouse storage, agent reasoning, and user-facing workflow so each boundary can be tested and governed independently.
Enterprise sources
01Relational and operational systems are synchronized into a shared analytical foundation instead of queried ad hoc.
PostgreSQLMySQLERPCRMLakehouse backbone
02Object storage, Iceberg metadata, Polaris cataloging, and Trino provide one SQL gateway over governed tables.
MinIOIcebergPolarisTrinoAgentic workflow
03LangGraph agents plan, retrieve context, generate Trino SQL, review errors, and recommend visualization.
LangGraphWeaviateRAGOperator surfaces
04Chat, SQL editor, Studio, Jobs, Dashboards, and Benchmarks expose the lifecycle without hiding the underlying artifacts.
ChatSQLDashboardBenchmark
Semantic profiles
Table definitions, business terms, metric expressions, and examples ground natural-language questions before SQL is generated.
Policy before model
Keycloak identity and OPA authorization decide access. The AI does not get to invent its own permission model.
Operational runtime
Jobs, notebooks, sync status, logs, and benchmark traces make the system observable when answers are wrong or delayed.
Governed query loop
Each answer passes through a visible chain of intent, context, execution, visualization, and review.
- 01
Planner
Reads intent and selects the right semantic profile.
- 02
Context
Two-stage RAG enriches the prompt with business knowledge.
- 03
Text-to-SQL
Generates and runs read-only Trino SQL.
- 04
Chart
Chooses a visualization that matches the result shape.
- 05
Reviewer
Validates quality before the answer reaches the user.
Data Journey
The core product surfaces span ingestion, preparation, analysis, and quality review.
Ingest
Connect and sync sources
Connections
Connect a source database and sync its tables into the lakehouse via CDC.
Prepare
Catalog, build, and schedule
Catalog
Browse synced tables and define the semantic profiles that ground answers.
Studio
Notebook workspaces prepare, validate, and publish analysis-ready tables.
Jobs
Schedule recurring ETL runs and inspect live execution logs.
Analyze
Ask, query, and visualize
Analyst
Ask in natural language and receive SQL, a chart, and a written answer.
SQL Editor
Write and run Trino SQL directly against the governed lakehouse.
Dashboards
Pin useful charts into a live, draggable grid for repeated review.
Trust
Evaluate answer quality
Benchmarks
Run question sets, score each answer, and track pass rate over time.
Built on a governed platform
LakeMind keeps AI behavior downstream of data architecture: open tables, one query path, semantic context, and policy-controlled access.
Governed lakehouse
Open table format with a single query engine over object storage.
Multi-agent AI
A LangGraph pipeline grounded by two-stage RAG over curated semantics.
Enterprise security
SSO identity and per-query authorization are decided by policy, not by the model.
The system optimizes for governed answers, not magic.
LakeMind accepts platform complexity so that generated answers remain inspectable, repeatable, and policy-aware.
- 01
Semantic governance
Optimizes
Optimized for shared business definitions and fewer wrong-intent answers.
Accepted cost
Requires teams to maintain semantic profiles instead of expecting a raw schema to explain itself.
- 02
Read-only analytical runtime
Optimizes
Optimized for safe querying, charting, and explanation over governed tables.
Accepted cost
It does not replace upstream modeling, data contracts, or operational write workflows.
- 03
Full-stack data foundation
Optimizes
Optimized for identity, policy, notebooks, jobs, catalogs, and benchmarks in one environment.
Accepted cost
More infrastructure must be operated than a thin chat interface over one database.
A working private product surface with visible trust loops.
The current system is active and demoable. Claims are kept at product-surface and architecture level rather than framed as a finished enterprise BI replacement.
End-to-end demo exists
The public-safe demo shows sync, catalog browsing, semantic profile setup, chat, SQL, dashboards, and benchmarks.
Governance is part of the product
Identity, catalog isolation, scoped credentials, OPA policy checks, and benchmark traces are first-class parts of the workflow.
Next evidence frontier
The next step is production evidence: benchmark history, failure analysis, and operational metrics across real datasets.