Grounded, citable answers from your own data, for every AI your business runs.
The problem
What a business knows lives in dozens of systems. No model can reason over data it can't reach.
When context is missing, models fill the gap with something plausible. Plausible isn't good enough for a board deck or a budget.
An answer without a source can't be checked, defended or audited, so it never makes it into a real decision.
The solution
How it works
Text, numbers, dates and identifiers, all fully indexed. ID lookups take a direct key-value path, so agents pivot record to record instantly.
Every index and field carries its type, capabilities and a plain-language description. The AI learns what the data is, not just what columns are called.
Agents query through MCP across many indexes in one call. Every hit is tagged with its source, and the AI answers only from what comes back.
Why CameoDB is different
No field is left out of the index. Any attribute can be filtered, sorted or searched.
Type, capabilities and a plain-language meaning ride with every field, so an agent builds the right query the first time.
They read what the data is, then ask for exactly what they need.
Built for agents
{ "id": "A-1042", "name": "…", "owner": "…",
"address": { … }, "notes": "…long free text…",
"history": [ { … }, { … }, { … } ],
"contacts": [ … ], "attachments": [ … ],
"audit_log": [ … 400 more lines … ] }
… 16 more records like this …
{ "id": "A-1042",
"renewal_date": "2026-11-30",
"arr": 240000,
"_index_source": "crm_accounts" }
… 16 more records, same 4 fields
The complete data stays in the index. The agent pulls the rest by id only when it needs it. Illustrative sizes.
Built in, not bolted on
Loader and structure detection ship in the platform.
Your own data becomes a working knowledge base, live.
Your team adds new data sources without us.
See it work
agent › search_across_indexes( "renewal risk next 90 days", [crm_accounts, support_tickets, contracts]) cameodb‹ 17 hits · each tagged with _index_source agent › answer Three enterprise accounts show renewal risk. Two have open P1 tickets older than 30 days, and one contract lapses before its usage review. ↳ crm_accounts#A-1042 · support_tickets#T-88913 · contracts#C-311
Scales with your data
Add new indexes on the same server as new sources and use cases arrive.
Data volume outgrows a server? Add servers. One distributed query, still focused.
No ceiling on volume. Answers stay as small and precise as the question.
Use cases
Which accounts are most at risk this quarter, and why?
What drove the variance between plan and actuals in Q3?
Which files share this signer certificate, and which domains did they reach?
What changed before the outage began?
Show every contract with a non-standard liability clause.
Summarize everything we know about this account.
Why it's trustworthy
How we engage
Map sources and the decisions AI should support. Agree the first use cases.
Ingestion, schemas, field descriptions and MCP wiring to the tools teams already use.
Use cases go live with your people, who learn to ask better questions and trust the answers.
New sources, new agents, new decisions. The layer grows with the business.
Business model
A first working session turns the client's own data into a business knowledge base.
They get the platform and tooling to add every new data source themselves.
Enterprise subscription for support, upgrades and maintenance. Industry standard.
Consulting on request for specific and complex use cases.
Let's talk
Tell us which decisions you want your data behind.
We'll show you the layer that gets it there.