AI quality
Review output examples, then tune prompts, retrieval, model choices, tools, structure, and fallback behavior.
Support for products we built
After launch, we can keep improving the AI products we know. Real usage gives us better examples, sharper priorities, and a clear reason for every change.
This service is for existing Adamant clients. Products built by another team begin with a separate assessment.

What happens after release
The agreed product reaches its first real users.
We fix defects tied to the delivered scope during the included four-week period.
User feedback, output examples, analytics, and logs reveal what matters next.
We tune the AI, remove workflow friction, and release focused changes.
A defined job
Review output examples, then tune prompts, retrieval, model choices, tools, structure, and fallback behavior.
Diagnose failures across application logic, data, infrastructure, permissions, integrations, and scheduled work.
Release small UX, reporting, admin, and workflow changes without turning support into an undefined new product build.
Product evolution, not a maintenance metric
Across roughly 18 months and two major versions, the coaching product developed into a multilingual text and voice experience with memory, accountability, HR reporting, and guardrails.
The project shows why staying close to a product matters. It is not a claim that ongoing support alone caused its user outcomes.
Product evolution
Leelou AI
RELEASE 01
RELEASE 02
Priorities, availability, response expectations, and release rhythm are defined for the engagement. Larger product areas are scoped separately so ownership stays clear.
Every build includes four weeks of bug fixing for defects tied to the delivered scope. Ongoing tuning, maintenance, and new improvements are agreed separately.
Not through the standard ongoing service. An outside product first needs a separate assessment so we can understand the foundation, risks, and appropriate route forward.