Quick start
How to choose a product, determine the perimeter and assemble the first AI-controlled circuit.
1. Start with a task
Don’t start with a full portfolio. Select a surface that will directly solves a user problem:
| Problem | Entry point |
|---|---|
| Route calls to models | IAM.Router |
| Check AI traffic and apply DLP | IAM.Secure |
| Create and launch agents | IAM Agent or IAM Marketplace |
| Work with documents and sources | IAM.Docs |
| Add a consultant to the site | IAM.Bot |
| Handle calls and speech | IAM.Voice + IAM.Speech |
| Describe architecture as code | IAM.Solution / AGL |
| Save personal memory locally | IAM.Mind |
2. Fix the perimeter
For each thread, determine where the data, model, and decision log reside:
- local — execution and memory on the owner’s device;
- on-prem / perimeter — services within the selected contour;
- dedicated — separate installation or tenant boundary;
- cloud — managed public surface of the product.
The production status does not mean that any deployment profile is available without
preparation. The product page lists dependencies and operational separately.
borders.
3. Create a chain of command
The minimum managed chain looks like this:
User / application
→ IAM.Identity context
→ IAM.Secure inspection
→ IAM.Router decision
→ approved model or tool
→ audit receipt
IAM.Core becomes necessary when multiple products need to detect capabilities of each other, transfer scoped context and coordinate agent actions via MCP/API.
4. Open the desired surface
There are four types of links on each product page:
- Landing — public description and connection request;
- Application - a working interface, sometimes requiring an account;
- API — external endpoint, if it is part of a public contract;
- Portfolio - return to the general card
iamgroup.ru.
5. Check the integration
Before production use, confirm:
- tenant and identity scope;
- model route and fallback;
- DLP/retention policy;
- timeouts, retry and idempotency;
- audit/observability;
- fault tolerance of downstream services;
- staging regression for the target scenario.