Your roadmap, your customers, your data - treated like it
Putting real product work into an AI tool is a trust decision, not just a feature decision. Idam AI is built so the answer holds up: your data is used only the way you direct, kept isolated to your workspace, and handled to the standards your engineering lead will ask about.
Three rules that don't bend
PM work is full of sensitive material - customer names in interview notes, unreleased roadmaps, churn numbers. Idam AI handles all of it under the same non-negotiables, on every plan, from day one of the trial.
Your data works only for you
What you put into your workspace is used the way you direct and nowhere else. Nothing crosses into another customer's workspace, and nothing you share trains models that serve anyone but you.
PII handled before you have to think about it
Customer names, emails, and phone numbers ride along in tickets and transcripts whether you want them to or not. Personal data shared with your agent is encrypted automatically, then masked or stripped depending on what the task actually needs.
Your company's rules, enforced in the workspace
Every company has topics that stay internal - unannounced deals, legal matters, codenames. Idam AI's policy controls let off-limits topics and keywords be flagged and filtered so your agents respect the same boundaries your team does.
Built on many models, so you depend on none
Idam AI runs on a blend of frontier, open-weight, and specialist models working together behind one agent. That architecture is a quality decision and a reliability decision at the same time.
One agent, several brains
A single PM request involves very different kinds of work: understanding what you meant, deciding what to do, and writing something worth shipping. Each step goes to the model that does it best - you never have to know or care which.
The right model for each job
Different models genuinely excel at different things. Idam AI routes each step - parsing your message, weighing a decision, writing the output - to the model class best suited for it, instead of forcing one model to do everything.
You inherit every upgrade
The model landscape moves monthly. Because Idam AI sits above the providers, your agents pick up the best available models as they ship - no migrations, no settings to chase, no "which model should I pick" homework.
No single point of dependence
A multi-provider architecture means your workflow is not chained to one vendor's roadmap, pricing, or status page. The constellation is built so work continues smoothly even when any one provider has a rough day.
The four questions your eng lead will ask - answered
You found the tool, but connecting Jira or Slack usually needs a nod from engineering or IT. These are the answers they are looking for - in their language, ready to forward.
“Where does our data actually go?”
Into your workspace and nowhere else. It is isolated per customer, encrypted at rest, and used only to do the work you ask for. No cross-customer access, ever.
“Does it train on our stuff?”
No. Your documents, feedback, and context never train models that serve other customers. What your agents learn about your product stays yours.
“What about customer PII in tickets and transcripts?”
It is detected and encrypted automatically, then masked or stripped based on what the task needs. The PM gets the insight; the identity does not travel further than required.
“What if a model provider has an incident?”
Idam AI runs on multiple model providers by design, so the platform is not tied to one vendor's uptime. Provider choice is our problem to manage, not your team's.
Trust questions, answered plainly
Where trust meets the rest of the platform
Guardrails
Trust covers how your data is handled; guardrails cover how your agents behave - scoped, reviewable, and accountable on every task.
Learn more →Memory
Agents remember your preferences and decisions - and you can see, edit, or delete every item they hold. Control extends to what they learn.
Learn more →Integrations
Jira, Slack, Notion, and analytics connect under the same data rules described here - grounding without giving anything up.
Learn more →Evals
Reliability is measured, not assumed. Continuous evaluation keeps agent quality honest as models and prompts evolve underneath.
Learn more →