Challenge
Cases AI development framework for the Fix Price PIM team How KT.Team prepared an AI-assisted delivery implementation program for the Fix Price PIM team: from requirements to pre-sale with human-in-the-loop control. 2026-05-21 Discuss the project Key takeaways How KT.Team prepared an AI-assisted delivery implementation program for the Fix Price PIM team: from requirements to pre-sale with human-in-the-loop control.
The Fix Price IT director asked not for a standalone AI review but for a complete program: what the pipeline from a business requirement to pre-prod looks like, where AI agents operate within it, what roles the agents have, how context, rules, prompts, skills and plans are formed, where a human controls the result and how much the rollout costs.
The scope covered discovery and design of the AI loop, a fast core of context + rules + agent pre-review, an MVP of a single process, expansion into analytics and development, QA/regression support, operations, metrics, training and scaling. After validation, the proposal scope was narrowed to a PIM team of about 20 people: KT backend/core, Toolkits frontend and the internal Fix Price team.
Solution
Task The Fix Price IT director asked for not a standalone AI review, but a complete program: what the pipeline looks like from business requirement to pre-sale, where AI agents work in it, what roles the agents play, how context, rules, prompts, skills, and plans are formed, where humans control the outcome, and how much implementation costs. KT.Team framed the project as a change to the PIM production environment, not as the adoption of a trendy tool.
AI development framework for an enterprise team Solution The scope included discovery and design of the AI framework, a fast core of context + rules + agent pre-review, an MVP for one process, expansion into analytics and development, QA/regression support, operations, metrics, training, and scaling. The documents also explicitly noted a limitation: this is not a turnkey out-of-the-box package and not a promise of a production end-to-end AI SDLC, but a managed implementation program with phases, risks, metrics, and phased expansion.
Project boundaries After validation, the scope of work was narrowed to a PIM team of about 20 people: KT backend/core, Toolkits frontend, and the internal Fix Price team. The result was an AI SDLC program structure for an enterprise team, a risk map, a baseline plan for TTM/error metrics, and the commercial implementation framework.
Project scope
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