Agentic Data Platform Capabilities
Every request your team gets becomes another item in a queue that never clears. Celvari changes what one request costs: describe a data product in plain language and get back a finished one — built, tested, governed, and deployed in your own environment. The agents do the work. Your team makes the one decision that matters.
Eight steps from a sentence to a governed data product.
Every data product travels the same path. Each step is automated and recorded — and three hard gates sit in the route: quality before code, passing tests before progress, and one human approval before deploy.
Intent
Describe what you need. The planning agent asks questions, finds the source tables, and writes a clear, versioned specification.
DQ Gate
Before any code is written, a strict quality gate runs. Bad data stops here — not three weeks later.
Generation
Agents generate production-grade transformations on your database engine, correcting themselves on failure.
Testing
Every model is tested — schema, logic, edge cases. Tests must pass before the product can move forward.
Approval
A person sees the intent, logic, tests, and quality findings together and makes one decision.
Production
Deploys into your environment — cloud, on-prem, or air-gapped. Data never leaves your database.
Lineage
Every column is traced and documented while the work happens. Governance as a result.
Consumption
Query in plain English, or connect to BI tools and APIs. Just ask your data questions.
The actual moments.
The real screens where your own knowledge becomes governed metadata and a plain-language request becomes a living, governed, queryable data product — knowledge ingestion, intent, the quality gate, human approval, lineage, edit-by-chat, and the live cost-and-time analytics.
Clever prompts can’t run your data platform. A coordinated system of agents can.
A single model improvising your pipelines is a liability. Celvari runs a system of purpose-built agents, each owning one part of the build — with a reasoning harness that lets every one plan, check its own work, and recover from failure. Plan → Execute → Validate → Reflect, on every task, logged and replayable, so you can see exactly what was done and why.
Conversational authoring
Turn plain-language intent into a structured spec, edit deployed products through the same chat, and query finished products in natural language.
Build and validation
The production line: generate transformations, write and run tests, enforce the DQ gate, deploy to production — self-correcting on failure.
Discovery and profiling
Classify every column and flag PII and sensitive data automatically — so sensitive fields are caught and governed before they ever enter a product. Plus column-grain lineage and schema resolved against your own catalog.
Domain building
Stand up a whole data domain in one pass — every table classified, every column tagged, and column-level lineage discovered across the domain, so an entire area of your estate becomes governed and queryable at once.
Knowledge and glossary
Extract your business terms from your documents, reconcile conflicting definitions for a steward, and link them to the actual columns.
Semantic memory
RAG-grounded over your documents and schemas, so generation and profiling reason from your reality — not a generic template.
Operational support
An in-app assistant that knows the product and your workflow state, so users navigate without leaving the app.
Legacy platforms are adding an AI agent that searches their catalog. Celvari’s agents build it.
Every agent works the way a careful engineer does.
No agent fires a single shot and hopes. Each one runs the same disciplined loop on every task — planning before acting, validating after, and correcting itself when something is wrong. It is what makes autonomous data work safe enough to trust.
Plan
The agent breaks the task into steps, decides what it needs, and writes a plan before touching your data.
Execute
It carries out the plan — generating, transforming, or profiling — on your engine, in place.
Validate
It checks its own output against the goal: tests, quality rules, and expected results.
Reflect
If something failed, it reasons about why, revises the plan, and tries again — instead of passing the error downstream.
Plan → Execute → Validate → Reflect — on every task, logged and replayable, so you can see exactly what was done and why.
The right model for each agent — not one model for everything.
Different jobs need different intelligence. A reasoning planner and a fast, cheap classifier shouldn’t run on the same model. Celvari lets you assign a model to each agent independently — and tune every agent’s setup — so you get the right capability at the right cost, everywhere.
Models are switchable per agent — cloud, private, or local — alongside the rest of each agent’s configuration. Your agents, your setup.
You keep control without becoming the bottleneck.
You should not have to choose between moving fast and keeping control of your data. Most platforms make you ship your data to where the processing lives. Celvari does the opposite — your data stays inside your own database, behind your own perimeter, and the work runs in place.
Where intelligence lives
The agents, orchestration, reasoning, and catalog. It plans the work and governs it. It never holds your production data.
Where your data stays
Your database, your environment, your engine. Transformations execute here, in place. Nothing is copied out to be processed elsewhere.
Only metadata, generated SQL, and prompts cross the line. Sensitive data never leaves the boundary it’s supposed to stay inside.
OPEN FOUNDATIONS
- Model-neutral — any AI provider
- Catalog-neutral — open catalog you own
- Engine-neutral — runs on your database
DEPLOYS ANYWHERE
- Fully on-premises
- Hybrid / Full cloud
- Fully air-gapped
EVERY DECISION TRACEABLE
- Per-decision AI tracing
- Every approval logged
- Full lineage, end to end
Built on tools your engineers already trust.
You have been locked into a data platform before. Celvari is not a closed system with a chat window on top — it orchestrates open, proven components and publishes everything it builds to an open catalog you own. Walk away whenever you want; you keep all of it.
| Layer | Today | Direction |
|---|---|---|
| Catalog | Open catalog on an open standard — you own it | Catalog-neutral; more catalogs over time |
| Transformation | Data Pipeline on your database | - |
| Source databases | 13 database and data lakes supported engines | More engines on the roadmap |
| AI models | Open-weight - OpenAI - Anthropic - Google - or any compatible endpoint — switchable per agent | Model-neutral, always |
| Identity | SAML 2.0 / OIDC SSO — Okta | — |
| Orchestration | Temporal — durable | — |
| AI observability | Langfuse and OpenTelemetry-compatible tracing | More LLM-governance tools over time |
Every AI decision is traceable.
Each agent step — the prompt, the model, the reasoning, the result — is captured and replayable. When you need to know why an agent did something, the answer is recorded, not reconstructed.
Plug into your LLM governance stack.
Celvari emits traces in an open, standard format, so it connects to the tools you already use to monitor and govern AI — Langfuse today, and more as your LLM-governance stack grows. Your AI oversight doesn’t stop at our boundary.
Move fast and keep control. Both, from day one.
Role-based access
Admin, Operator, and Viewer roles, enforced across the platform.
Enterprise SSO
SAML 2.0 and OIDC, with automatic user creation and role mapping from your identity provider.
Encrypted credentials
API keys and connection secrets are encrypted at rest. Every connection change is tested before it goes live.
Complete audit trail
Every agent decision is logged with its reason. Every approval is recorded with the reviewer’s name and comments. Every deployed column is traced to its source.
One platform, the whole lifecycle.
- Conversational creation
- Self-correcting generation
- Automated testing
- Edit-by-chat
- Production deployment
- DQ hard gate
- Column-grain lineage
- Human approval
- Full audit trail
- RBAC + SSO
- Knowledge base + RAG
- Auto glossary
- Schema-aware ingestion
- Domain builder
- Semantic profiling
- Natural-language query
- Charts
- BI / API access
- Data-product-as-source
- Per-decision observability
- Agent analytics
- Live workflow tracking
- Event-driven automation
- Model-neutral
- Catalog-neutral
- Engine-neutral
- On-prem → air-gapped
Celvari doesn’t assist your team. It expands what your team can do.
The hard questions, answered.
What makes the agent workflows autonomous — and what keeps autonomy from becoming unpredictable?
Every agent runs the same reasoning harness on each task — Plan → Execute → Validate → Reflect: it plans before touching your data, checks its own output against the goal, and reasons about failures to revise and retry instead of passing the error downstream. The autonomy is bounded by construction: each agent is scoped to one job, transformations run as deterministic, versioned SQL so a run is reproducible rather than improvised, no agent can skip the quality or testing gates, and the pipeline halts for one human approval before deploy. Every step — the prompt, model, reasoning, and result — is logged and replayable. That harness is the difference between an LLM improvising and an engineering discipline you can leave running across all eight steps.
We have already invested in ETL/ELT tooling. Does Celvari replace it, or coexist?
Coexist. Celvari runs its transformations as SQL on the same warehouse your pipelines already feed, so it sits alongside your stack instead of ripping it out. Keep your ingestion and existing pipelines exactly as they are; point Celvari at the same data and let it take on the part that is actually slow — turning a request into a governed, tested, deployed data product in minutes. The pragmatic path most teams take: use Celvari's speed to unblock your AI initiatives now, on top of what you already run, then migrate more of the backlog onto it over time as it proves out. It is an on-ramp, not a forklift.
How do you stop an agent from shipping wrong — or destructive — logic?
Controls compound, so a single bad step can't reach production. The quality gate catches bad data before generation, every model must pass its tests to progress, transformations execute as deterministic, versioned SQL on your engine so runs are reproducible rather than improvised, and each agent runs a Plan → Execute → Validate → Reflect loop that catches and fixes its own failures. Deployment still requires a human approval.
Where does our data live, how is it protected in transit, and is it ever used to train a model?
Your data stays in your environment and is never used to train any model. Celvari splits a control plane — the agents, orchestration, and catalog that plan and govern the work — from the data plane, your database, where transformations actually run. Only metadata, generated SQL, and prompts cross that boundary, over encrypted transport; production data never does, and stays under your database's own encryption and access controls. Connection credentials and secrets live in your secret store, not inside Celvari.
Your agents reason over our business knowledge. Where does that knowledge base live, and who can access it?
The knowledge base the agents draw on — your glossary, business definitions, and the semantic memory grounded over your documents and schemas — is built from your material and lives in the catalog you own, inside your environment. It is retrieval context at inference time (RAG over your reality), not training data: nothing in it tunes a model, and it never leaves your boundary. Access is governed by your identity provider and the same role-based controls as the rest of the platform, so people and agents see only what they are entitled to.
Can it run on-premises or fully air-gapped?
Yes — on-premises, in your cloud, hybrid, or completely air-gapped, with no outbound dependency when paired with a private or local model. Because the work executes in place on your engine, deployment mode is a configuration choice, not a different product.
Won't an AI-built pipeline be a black box I can't audit?
The opposite — auditability is a by-product of how it works. Every agent step (the prompt, model, reasoning, and result) is captured and replayable, every approval is recorded with the reviewer and their comments, and every deployed column is traced to its source. Traces emit in an open, OpenTelemetry-compatible format, so they plug into the LLM-governance tools you already run.
What stops us from being locked in?
Nothing about Celvari is built to be hard to leave. Your data products, lineage, glossary, and domains live in an open catalog you own, on your database, with your choice of model and engine. Walk away tomorrow and you keep all of it.