A multi-tenant CRM platform
Bringing an established sales product up to AI speed — without losing control of the codebase.
A mature CRM — contacts, deals and pipeline, workflow automation, multi-tenant company management, an Outlook add-in — wanted to ship AI-assisted features faster while keeping every change reviewable and auditable.
The challenge
Years of accumulated product surface: dozens of interlocking features across a React SPA and its backend services.
Pressure to move at AI speed, but no appetite for AI writing code that reaches production unreviewed.
Improvement ideas and technical debt were scattered across TODOs, docs and people’s heads — never in one place.
What agI3R did
The whole product was inventoried automatically — its features mapped and its architecture summarized — in minutes, not a discovery sprint.
Improvement ideas and suspected gaps were harvested straight from the codebase — the “wire this to a real model”, “consolidate the backend”, “add rate limiting” notes teams forget for months.
Each proposed change arrived reviewed by independent checks — provenance, tests, acceptance-criteria coverage — and applied only after a human approved it.
The platform picked up the product’s own conventions from its code and held new AI-written work to them automatically.
The governed workflow
Every piece of work moved through the same human-controlled lifecycle — fast where it could be, gated where it mattered.
A structured interview turns a rough request into a reviewed spec, grounded in what the CRM already does — so new work extends rather than duplicates.
The coding agent proposes changes against the approved spec; nothing merges without passing the gate.
Reviewers see the diff alongside independent test and security evidence, and route changes back to the agent.
Approved work ships through a governed release with a permanent, tamper-evident record of who approved it.
“The team moved at the speed of the agent, but every line still passed the same human gate they trusted before.”
Illustrative case study based on real onboarding patterns. Anonymized; figures are representative, not customer-specific.