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AI-DLC
Company-Aware
AI Workflows
For Your Delivery
Lifecycle
AI-DLC brings AI into your delivery process in a controlled, context-aware and measurable way.
Agents work across planning, modernisation, development, QA, DevOps, review and operations,
and a named person still makes every call that matters.
AWS Premier Tier Services Partner Delivering since 2016 200+ customers 80+ engineers
Build AI into your delivery
We design AI workflows around the real context of each project: codebases, documentation, Jira issues, architecture decisions, coding standards, business rules, onboarding material, runbooks and customer knowledge bases. Not a generic prompt library, and not a tool you deploy and forget. The context becomes a permanent, versioned foundation that flows automatically into every phase.
From a 90-day pilot
50
PRs and code changes checked against project-specific rules
75
test scenarios generated or reviewed with AI support
15
deployment and release checks validated against defined standards
1-2wk
to build the knowledge base
A Consulting-Led Framework For Putting AI Into The Delivery Lifecycle
AI-DLC stands for AI-Driven Delivery Lifecycle. It is not a fixed tool or a generic prompt library. We work with each customer to find where AI creates the most value, then design the context layer, the workflows and the governance model around that need. That can mean supporting legacy modernisation, improving QA and review, helping DevOps validate operational changes, or making engineering knowledge reusable across teams.
Built for
What it is not
Best suited for
Things Breaking
Your AI Rollout
Right Now
Your AI tools do not know your company.
Copilot, Cursor and the rest 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.
Our Solution :
We move that context into a permanent, versioned foundation that flows automatically into every phase, so it is answered once rather than re-typed forever.
AI output is unaudited and possibly non-compliant.
Is the generated code compliant with company standards, and who is checking? In a regulated sector that question cannot go unanswered.
Our Solution :
Every AI output is checked automatically against your written policies. A violation is blocked, the rule is cited, and an audit trail is written without anyone having to remember to do it.
Institutional knowledge lives in a few people's heads.
Architecture decisions, service dependencies and onboarding knowledge sit with senior engineers. When they leave, a new hire or the AI makes the same mistake from scratch. Peer-reviewed research puts developer time spent just understanding existing code at around 58%.
Our Solution :
That knowledge is pulled into a queryable, living foundation: critical decisions, validated learnings, reusable standards and approved context updates, captured in the system.
AI adoption stalls after the pilot.
Individual tools work, enterprise rollout fails. Every team writes its own prompts, standards drift, and nobody can measure what is actually working.
Our Solution :
A meta-loop layer watches the pipeline, reports patterns to humans, and improves over time. The system gets better, not just deployed and forgotten.
Four steps
One System That Does Not Fight You
Our engagement follows AWS Migration Acceleration Program phases, extended with modernization sprints.
Enterprise Context Foundation
Confluence, source repos, ADRs, target architecture, business rules and past decisions are connected through read-only connectors and integrations, MCP where it applies. Nothing is written back to your systems by default.
AI Agents Embedded In The Pipeline
Define, Refine, Build, Review, Ship, Operate, Learn. AI agents fed with your company context are integrated at each phase, and each phase output becomes the next phase input.
A Compliance Layer Audits Every Output
Each AI output is checked automatically against your written rules: ADRs, security policies, coding standards, deprecation lists. A violation is blocked and the rule reference is shown. The audit trail is created for you.
A Meta-Loop Improves The System
Which rule was violated most, which output was rejected, where context was missing. Reported weekly to humans, humans decide, the system updates. This is AI informing humans, not AI fixing AI.
Why AIDLC?
Faster Delivery
Conventions and dependencies are already known to the AI, so
review cycles shorten and repeated corrections drop. Less time
explaining context that should already be in the system.
Built-In Compliance
Every AI output is audited against company policy. In a regulated
sector the audit trail is generated without anyone remembering
to do it.
Institutional Memory
Decisions made, trade-offs considered and lessons learned are
written into the system. The knowledge does not leave when
the engineer does.
Faster Onboarding
A new hire, or the AI, does not learn your architecture and
standards from scratch. Day one looks a lot less like a
treasure hunt.
A pipeline That Improves
The meta-loop means the system adapts to your language and
process over time. It learns, and it tells you what it learned.
Humans Stay In Control
AI suggests and audits, it does not decide. Critical decisions
always go to human review. The loop always ends with a
person making
the call.
Cost You Can See Per Outcome
Because the delivery platform is metered, cost can be expressed
per capability. On our reference modernisation engagement the
agentic build of one capability ran at roughly $39 of model spend.
Seven Years Of This before AI,
And an AWS Premier Partnership
The method worked before we pointed AI at it, which is why the
AI part is safe. The assessment framework we wrote for our
customers was adopted by AWS as the mandatory template for
partners in its modernisation programme.
Where It Applies
Software Modernization
(Brownfield)
Your legacy platform is holding the business back
We measure what it does, rebuild it in slices, and prove each slice behaves identically before it goes live. This is the line we have delivered end to end.
Software Development
(Greenfield)
You are building something new and it needs to last
Spec first, tests first, gated from the first commit. AI speed without prototype quality.
Internalize SaaS with AI
A SaaS costs more every year and you use a fraction of it
We capture the part you actually use, rebuild that, and switch only once the behaviour matches.
SDLC Transformation
Your teams have AI tools and no method
We install the pipeline into your own repositories and prove it on your own next features. When we leave, it is yours.
Shall We Have a Call?
Just a look at your architecture and your delivery process to see where AI would help and where it would quietly break things. You leave with a view of where to start, not a quote.
FAQ
Does AI-DLC replace our existing tools like Jira, Confluence and GitHub?
No. It complements them rather than replacing them. It integrates read-only and does not write back to your data by default. Any update to the knowledge base or context goes through a controlled, approved workflow.
Does AI write the production code, and who stays in control?
Agents do the volume and suggest. People decide. Critical decisions always go to human review, no agent merges on its own, and on regulated or high-consequence flows the review is 100%, never sampled.
We operate in a regulated sector. Is AI-DLC suitable?
It was designed with this in mind. The compliance layer generates audit trails automatically, the human-in-the-loop approach is preserved throughout, and no critical decision passes without human approval. Banking, healthcare and insurance are exactly the version this was built for.
Can we use our own LLM, on-premise or a private model?
Yes. AI-DLC is model-agnostic by design. AWS Bedrock, Azure OpenAI and on-premise models are all supported. If your data cannot leave your environment, it does not have to.
How long does it take to get started?
A pilot setup is typically running within four to six weeks. The first phase focuses on one or two selected lifecycle stages, and scope expands from there based on what you learn. No need to commit to the whole lifecycle upfront.
Is AI-DLC suitable for smaller teams?
It creates the most value in engineering teams of fifty or more. For smaller teams there is a more focused starter package, and it is worth a conversation, because sometimes a small team has bigger compliance problems than a large one.
How often does the meta-loop run?
By default it produces a weekly summary, and it can be set to daily or sprint-based reporting. The summary goes to humans, humans decide what to update, and the system does not self-modify without approval.
What if our people resist this?
They are not being replaced. The method reserves the decision seats for humans by design: specification ownership, architecture calls, merge authority and final acceptance.