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For the reader who checks the claims

How a question becomes a governed answer, where it runs, and the mechanism the whole three-audience model rests on.

Config-driven ETL

New sources onboard from configuration, not code — POS, loyalty, ERP, DMS, handheld — in days. This is what “live in weeks” rests on.

Multi-tenancy

The Advanced Analytics Platform is multi-tenant by design: every tenant works against their own permissioned view — their products, outlets and numbers, baked in. Without this, letting outside parties in is a policy; with it, it is a mechanism.

Multi-agent decomposition

A commercial question is decomposed — scope resolution against the governed model, metric retrieval, comparison, narrative — and each step is constrained to governed definitions. The answer carries its scope because the scope was resolved first, not inferred afterwards.

MCP integration

Platform capabilities — Report Builder among them — are exposed to Caisy as MCP tools. A conversation can build a report; the toolset extends without retraining anything.

Retrieval over your own data

Answers are computed from the customer’s own harmonised data at query time — not from anything memorised in a model. No customer data is used to train models.

Model-agnostic, within your Bedrock estate

Models are swappable within your own cloud environment — on AWS, that means Bedrock — so you get current engines without chasing whatever launched last week, and without data leaving your account.

Cloud-agnostic, your account

Deployed in your own cloud environment — AWS preferred, and the deployment we know best — alongside the existing warehouse or lake. Databricks and warehouse estates are complemented, not replaced. Data residency follows your account.

Workflow automation

Extensible workflow automation connects answers to actions — alerts, triggered actions, distribution of results — configured, not coded.

Permissions the AI cannot get wrong

Caisy never chooses what you are allowed to see. Every user queries a view with their permissions already baked in, and the platform inserts the entitlements itself — deterministically, on every query. The AI answers the question; it never gets to decide the scope.
✕ TRUSTING THE MODEL — THE RISKY WAY User question LLM picks the filters permissions left to a prompt All the data one bad guess is a leak ✓ THE DATASAPIENS WAY — PERMISSIONS BAKED IN User question any path — Caisy, reports Platform inserts the permissions deterministic — never the LLM Permissioned view entitlements baked into the view itself, per user Only entitled rows exist
The unauthorised rows are not filtered out of the answer — they are never in the view the query runs against.
There is no wrong view for the AI to pick.

Security posture

Enterprise-grade AI. Secured, certified, yours.

Every model runs inside an audited cloud perimeter, on your own data — certified, compliant, and fully under your control.
ISO 27001
ISO 42001
SOC 2 Type 2
GDPR-aligned
Your data, your tenant
Role-based access control
Microsoft Entra ID SSO
Deployed in your own cloud
No customer data used to train models
Talk to the team