Ask your database anything.
DataSpeak turns a question into SQL against your own database, shows you exactly what it ran, and keeps every correction your team approves as grounding for the next question.
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Someone in ops asks
“What was revenue by region last quarter?”
DataSpeak first wrote SUM(o.total) as revenue. An analyst flagged that this counts refunded orders, and corrected it to SUM(o.total minus o.refunded). The approved correction was written to the dataspeak_knowledge collection, so later revenue questions on this schema start with it applied.
Illustrative schema and figures. The correction cycle is real.
Uptime
Avg response
Queries processed
Active teams
What makes DataSpeak different.
Other tools that write SQL start from zero on every question. This one retrieves what your people have already settled, then writes the query.
Self-improving AI
Every query and every judgment feeds a vector knowledge base, so accuracy climbs with use instead of staying flat.
Enterprise security
Fernet encryption for API keys and database credentials. Nothing sensitive is stored as plain text.
Universal database support
PostgreSQL, MySQL and SQLite, with automatic schema discovery on connect.
Real-time feedback loop
Users rate results. Correct answers strengthen the pattern; wrong ones open a review workflow.
Vector knowledge store
Qdrant-backed semantic search over query patterns, table instructions and domain knowledge.
Granular permissions
Five role tiers from Owner to User, assigned per instance so access follows the database.
The self-improving feedback loop.
Every query and every judgment makes the next answer better. A single thumbs-down never edits the model on its own — a person signs off first.
Natural language query
Someone asks a question in plain English.
AI SQL generation
A LangGraph pipeline writes SQL against the discovered schema.
Execute and display
Results come back with the SQL, the table and the answer.
User feedback
Thumbs up or down, with an optional reason.
Vector store learning
The judgment is embedded into Qdrant for later retrieval.
Continuous improvement
Approved corrections ground the next similar question.
Your data is encrypted and protected.
Connection strings, database passwords and LLM keys are encrypted at rest with Fernet — AES-128 in CBC mode — never stored as plain text. Sessions run on JWTs with refresh tokens.
API key encryption
Fernet symmetric encryption for every LLM API key.
Password encryption
Database credentials encrypted at rest.
JWT authentication
Token-based auth with refresh tokens.
Role-based access
Instance-level permission control.
All sensitive values encrypted with Fernet (AES-128, CBC).
Encryption specs
See results exactly how you need them.
A plain-English answer, the SQL that produced it, and the rows themselves — side by side, so the analyst and the operator can both trust it.
Show me the top 5 customers by revenue this month
Here are the top customers by revenue for this month.
| name | revenue |
|---|---|
| Acme Corp | $125,430 |
| TechStart Inc | $98,210 |
| Global Systems | $87,540 |
An answer in words
Written for whoever asked, not for whoever maintains the schema.
The SQL that ran
Shown by default, never hidden — so it can be checked and corrected.
The rows themselves
Formatted tables with the tables touched and the time taken.
What ran, in numbers
Row count, the tables touched, and how long it took — so a result is never a black box.
Copy the SQL
One click hands the exact query to anyone who wants to run it, tune it, or drop it straight into a report.
Find out where your schema needs a note.
Accuracy is broken down per table, so a column people keep misreading shows up as a number rather than as a complaint. Write one table instruction, and every later question about it starts from that.
Accuracy tracked per table
See which tables the AI already answers well, not just one aggregate score.
Flagged where accuracy slips
A table that answers worse than the rest is usually one nobody has documented.
Response times per instance
Find the connection that has started to drag before anyone reports it.
Accuracy by table
subscriptions and refunds answer worse than the rest. Both were corrected for the same reason — add a table instruction and the pattern stops recurring.
Synthetic example dataManage all your databases in one place.
Connect PostgreSQL, MySQL and SQLite instances, each with its own access control and connection monitoring.
Production DB
PostgreSQL
Analytics Warehouse
MySQL
Test Database
SQLite
Role-based access
Owner, Admin, Editor, Analyst, User.
Real-time status
Live connection monitoring with health checks.
Schema viewer
Browse tables, columns and relationships.
Powered by Qdrant vector search.
DataSpeak does not just generate SQL — it remembers. Every successful query, every correction and every judgment is embedded and retrievable.
Semantic retrieval
Find similar past queries using embeddings.
Table instructions
Custom notes per table for better queries.
Approved corrections
Learn from reviewed negative feedback.
Gap analysis
Compare a query against what is already known.
Vector collections
QdrantSuccessful queries and their patterns
Table-specific instructions
Every judgment, embedded
Embeddings: Google gemini-embedding-001 via LangChain.
Vector counts are illustrativeCollaborative query improvement.
Users give instant feedback. Maintainers review and approve corrections. Nothing reaches the model without a human signing off.
Positive feedback
Strengthens patterns that already work.
Stored in the vector knowledge base
Retrieved for similar questions
Improves future SQL generation
Negative feedback
Opens the review workflow.
Requires maintainer review
Approved corrections guide the AI
Prevents the same mistake twice
Review workflow
Powerful features that make your data accessible to everyone.
Ask in plain English
See exactly what ran
Tables with the SQL beside them
Improves with every query
Postgres, MySQL, SQLite
Encrypted credentials, JWT auth
Granular role-based access
Usage insights and metrics
Answers while you wait
Semantic knowledge base
Continuous improvement
Many databases, one place
Ready to transform how your team accesses data?
Connect a database and ask it something badly. The first answer might be wrong. Correct it, and watch the second one arrive already knowing.