The First Agent-Native
Data Platform
Ingest, validate, transform, store, retrieve, and analyze your data — whether you're an AI agent talking through MCP, a developer, or a data analyst. One platform for all.
keyFields on the pipeline.
Type what you want.
The Assistant builds it.
Datris ships with an Assistant Agent inside the platform UI. Tell it what data you want. It asks a few sharp scoping questions, picks the right source and destination, generates the fetcher, requests credentials securely, runs the job, confirms the rows landed, and lets you query the result — usually in seconds, with no hand-written config.
- Clarifies scope before building — picks the right source, destination, and cadence with you
- Generates and runs the fetcher (tap) for external APIs and files
- Requests credentials through a secure form — never in chat history
- Polls job status and confirms rows actually landed before saying "done"
- Flags things you didn't ask about — upsert vs append, fair-use policies, schema drift
- Answers natural-language questions over the data once it's in
- "I'm looking for corporate earnings data."
- "Ingest these PDFs into a vector store for RAG."
- "Refresh treasury yields from FRED nightly and let me query trends."
Intelligence at every stage
Every step of your data pipeline is enhanced with AI. From ingestion to delivery, Datris makes data engineering accessible through natural language.
Push and pull — one platform, two interfaces
AI agents and humans ingest data through the pipeline, store it across databases and vector stores, and retrieve it back — via MCP or API.
Full RAG pipeline built in
Extract, chunk, embed, and upsert documents into any major vector database. Build retrieval-augmented generation workflows without leaving your pipeline.
Your AI agents are
first-class pipeline operators
Datris ships with a native MCP server. Claude, Cursor, OpenClaw, and any MCP-compatible AI agent can register pipelines, trigger jobs, search your data, and monitor pipelines — all through natural conversation.
- Register pipelines and configure schemas
- Upload data for processing
- Trigger and monitor pipeline jobs
- Profile data and get AI insights
- Semantic search across vector databases
- Query PostgreSQL and MongoDB directly
Speaks every data language
Ingest structured data, unstructured documents, and archives. Output to vector stores, structured stores, or optimized columnar formats.
| Format | Input | Default Destination |
|---|---|---|
| CSV | SQL DB | |
| JSON | NoSQL DB | |
| XML | NoSQL DB | |
| Excel (.xlsx) | SQL DB | |
| Parquet | SQL DB | |
| ORC | SQL DB | |
| Vector DB | ||
| Word (.docx) | Vector DB | |
| PowerPoint (.pptx) | Vector DB | |
| HTML | Vector DB | |
| Email (.eml) | Vector DB | |
| EPUB | Vector DB | |
| Archives (.zip, .tar) | Unpacked, routed | |
| Plain Text | Vector DB |
Destinations are fully configurable. Route any format to any target — SQL databases, NoSQL stores, vector databases, REST endpoints, Kafka topics, or ActiveMQ queues.
Your choice of AI model
Use cloud AI from Anthropic or OpenAI, or keep everything local with Ollama. No vendor lock-in — switch providers without changing your pipeline config.
How Datris compares
The only platform combining MCP-native agent access, AI-powered data quality and transformation, multi-destination pipelines, and RAG — in a single open-source package.
| Capability | Datris | Airbyte | Fivetran | dbt | Prefect | Dagster | NiFi | Meltano |
|---|---|---|---|---|---|---|---|---|
| MCP Server (native) | 30+ tools | |||||||
| AI Data Quality | ||||||||
| AI Transformation | ||||||||
| Data Ingestion | ||||||||
| Orchestration | Config-driven | ~ Limited | ~ Limited | |||||
| Vector DB / RAG | 5 DBs | |||||||
| Open Source | AGPL-3.0 | Core | Core | |||||
| No-Code | JSON config | ~ UI | UI | SQL | Python | Python | Visual | CLI/YAML |
| Self-Hosted |
Send us a message
Questions, feedback, or just want to chat — we'd love to hear from you.