AI-native engineering transformation

The problem with unmanaged AI coding

An engineer can install a coding assistant and become faster at a local task. The company still needs to know which context the tool can access, how a generated change was planned, whether the tests cover the intended behavior, and who approved the release.

Without a shared workflow, each team creates its own rules. Code review gets harder because generated diffs can be large and plausible. Sensitive repository context may reach an unapproved provider. Local speed improves while production risk moves downstream to reviewers, QA, and platform teams.

A single AI SDLC across coding tools

We design one route from product intent to production. Architecture decisions become explicit inputs. Tickets carry acceptance criteria and test expectations. Agents can prepare plans, implement changes, run checks, and assemble review context, but the workflow controls what they can read and what they can change.

Engineers can use different approved tools. The company keeps one set of repository permissions, quality gates, audit records, and release rules. This makes AI coding a team capability instead of a collection of personal shortcuts.

The engineering workflow we put in place

1. Product and architecture decisions are written before code generation starts.
2. The agent receives only the repository context and tools needed for the task.
3. Tests, static analysis, security checks, and policy checks run as part of the same workflow.
4. Reviewers receive the plan, changed behavior, evidence from tests, and known limits.
5. Production outcomes feed back into prompts, checks, and team guidance.

Legacy modernization factory

The same system can support a legacy rewrite. We first map the current behavior, interfaces, data contracts, and operational constraints. AI-assisted delivery then helps the team document the old system, build characterization tests, move bounded parts to the target architecture, and compare new behavior with the existing baseline.

The modernization work leaves behind more than new code. The client team keeps the governed AI development workflow for future maintenance and product work.

What we measure

We track lead time from an accepted ticket to production, review effort, test quality, escaped defects, rollback rate, adoption, and the share of generated work that passes the agreed gates without rework. Faster output only counts when production quality remains controlled.

Our engineering success story describes a unified AI SDLC used by more than 260 engineers at an ecommerce marketplace.