Sigma platform
Sigma. One AI-native operating layer for your enterprise.
One operating layer. Goals, context, intelligence, workflows, controls, system access, and the interfaces people actually use — running as one continuous execution environment.
The stack
Seven layers. One execution environment.
Control plane
Governance & security
Wraps every layer below it. One place decides who and what may read which data and take which action — RBAC, a mandatory human approval gate, and an audit record that cannot be edited. The same rules apply to every agent, every channel, every deployment.
Interaction surface
Channels
Where people meet the system: CLI, email, Slack and Teams. Requests come in and approvals go back out where the work already happens. Nobody learns a new tool.
Execution runtime
Vertical AI agents
Durable, checkpointed agent loops that survive restarts and produce the same result on every run. Templated where the path is known, model-driven where it is not.
Semantics
Canonical data model
One shared vocabulary for the business. An agent asks for invoice or customer and gets the same thing whichever system it came from. Onboarding a new source is a mapping, not a rebuild.
Context layer
Context
Policies, prior decisions and reference material, retrieved per task and scoped to the requester. Agents work from what your business actually knows, not from what a model remembers.
Model gateway
Models
Model-agnostic by design. Claude, OpenAI, open-weights or a fine-tune behind one interface. Each task routes to the model that fits it; a new frontier model drops in without a rebuild.
Data plane
Data connectivity
Internal and external. Read-only adapters into Snowflake, PostgreSQL and SQL Server, plus outbound connectors to the vendors and services you transact with. Your golden source stays authoritative.
Capabilities
What you get out of the box.
Beyond the layers themselves — the operational tooling every production deployment needs, built once and reused.
Pre-built agents
A library of production agents for the workflows enterprises share — reconciliation, onboarding, KYC refresh, exception triage, reporting. Each arrives already wired to the canonical model and the approval gate, so a deployment starts from a working system and is configured to your policies rather than written from nothing.
Ask
Natural-language questions answered straight from the warehouse. Every query is role-scoped, validated and read-only, so an answer never exceeds the asker's permissions.
Evals & observability
An evaluation harness, drift and quality monitoring, alerting and dashboards — with retraining loops that run on the evidence rather than on a schedule.
Routing & cost control
Each task goes to the model that fits it, with spend attributed per team and per workflow. You see what AI costs before finance does.
Governance & security
Your data stays yours.
Sigma reads your data. It does not take it, copy it out, or learn from it on anyone else's behalf. Everything an agent does is scoped, logged, and reviewable.
Access controls
Every person and every agent gets the narrowest set of permissions that still lets them do the job. Each request is checked against those permissions and written to a log, so you can always see who looked at what, and when.
Agent governance
Agents can read your data but cannot change it. Anything that touches the outside world — sending, filing, paying, committing — stops at a human approval gate first. Every decision is written to a record that cannot be edited afterwards.
Data sovereignty
Your data and your encryption keys stay with you. Nothing is copied out of your environment, and nothing is ever used to train a model for another client. Encrypted at rest and in transit.
Deployment options
Run Sigma in our cloud, in your cloud, in your own data center, or in a fully isolated environment with no outbound connection. Your infrastructure, your call.