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AIDLC helps organizations bring AI into their delivery process in a controlled, context-aware and measurable way.
We design and implement AI workflows around the real context of each project: codebases, documentation, Jira issues, architecture decisions, coding standards, business rules, onboarding materials, operational runbooks or customer-specific knowledge bases.
The goal is to make AI useful across the delivery lifecycle, from planning and modernization to development, QA, DevOps, review and operations.
from a 90-day pilot
PRs / code changes checked against project-specific rules
test scenarios generated or reviewed with AI support
deployment / release checks validated against defined standards
to build knowledge base
We work with each customer to identify where AI can create the most value, then design the right context layer, workflows and governance model around that need.
This can mean supporting legacy modernization, improving QA and review processes, helping DevOps teams validate operational changes, or making engineering knowledge reusable across teams.
Any company developing software that wants to execute a controlled AI rollout. Especially organizations going through major transformation, operating in regulated sectors, or running large engineering teams.
AIDLC is not a fixed tool or a generic prompt library. It is adapted to the customer's delivery process, available knowledge sources and business constraints.
Engineering teams of 50+. Smaller teams? We have a focused starter package - ask us about it.
Copilot, Cursor, and similar tools know general coding. They do not know your ADRs, your deprecation list, your target architecture, or your business rules. Your team pastes the same context into prompts every single day.
Fix AIDLC moves this context to a permanent, versioned foundation that flows automatically into every phase.
Is the generated code compliant with company standards? Who is checking? In regulated sectors, this question cannot go unanswered. Anyone who says it can is either not in a regulated sector or has not shipped yet.
Fix AIDLC auto-checks every AI output against your written policies, blocks violations, cites the rule, and leaves an audit trail.
Architecture decisions, service dependencies, onboarding knowledge - all locked in senior engineers' heads. When they leave, a new team member or your AI makes the same mistake from scratch. Again.
Fix AIDLC pulls this knowledge into a queryable, living foundation. Critical decisions, validated learnings, reusable standards and approved context updates are captured in the system.
Individual tools work. Enterprise rollout fails. Every team writes its own prompts. Standards are not kept. Nobody can measure what is actually working. You have a hundred AI experiments and no system.
Fix AIDLC's meta-loop layer observes the pipeline, reports patterns to humans, and the system gets better over time - not just deployed and forgotten.
Confluence, source repos, ADRs, target architecture, business rules, and past decisions are connected via read-only connectors / integrations, MCP where applicable.
Define, Refine, Build, Review, Ship, Operate, Learn - AI agents (skills) fed with your company context are integrated at each phase. Each phase output becomes the next phase input.
Every AI output is automatically checked against your written rules: ADRs, security policies, coding standards, deprecation lists. If there is a violation, it is blocked, and the rule reference is shown. Audit trail is created automatically.
The pipeline watches itself. Which rule was violated most? Which AI output was rejected? Where was context missing? Reported weekly to humans. Humans decide. System updates. It is not AI fixing AI - it is AI informing humans.
AIDLC is especially valuable in legacy modernization programs where teams need to understand large codebases, dependencies, architectural constraints and business rules before making transformation decisions.
It can support modernization assessment, risk identification, dependency discovery, target-state alignment, backlog preparation and Proof of Value planning.
This helps teams move from unclear legacy complexity to a more evidence-based modernization roadmap.
AIDLC can help QA teams generate, review and improve test scenarios using acceptance criteria, product rules, historical defects and platform-specific expectations.
AIDLC can support DevOps teams by checking deployment plans, runbooks, configuration changes and operational risks against defined delivery standards.
AIDLC can help engineering teams apply architecture decisions, coding standards, deprecation rules and security policies more consistently across delivery workflows.
No sales pitch. Just a look at your architecture to see where it might explode.