Seconds to ideate. Weeks to ship.

A method, and an orchestration layer that sits between AI coding agents and your production code.

• An assessment turns your codebase into a ranked list of findings. Each finding becomes a task on a sprint board.
• A task goes to a person, to an AI agent, or to an AI agent whose diff a person approves first.
• AI agents work in a locked-down sandbox, on a copy of the code, and run the real build and tests.
• The sandbox's output is treated as untrusted. The change is checked again outside it, and only then is a draft pull request opened.
• A person reviews and merges every one. A fresh scan after each sprint shows what improved.

Those tools write code fast, and you should keep using them. The slow part is everything that code has to touch. On an old core with thin tests, nobody can say what a change broke. So it joins the queue for regression testing, sign-off and a release window.

AK Evolve does the work those tools need done first.

• Tests pin down what the system does today.
• We cut clean boundaries between systems.
• Each AI agent works within limits on what it may touch.

Inside that, we use the same models you would.

AI analysis agents run inside your own AWS account, so your code doesn't leave it. They score every service on complexity, coupling, testability, change risk and AI readiness. Coupling counts shared data as well as calls between services. You get:

• tech debt in dollars, per sprint and per service;
• a verdict for every service: ready now, needs foundation work first, or retire;
• a map of every service-to-service call and shared database;
• a modernization plan ordered by business value, change risk and AI readiness;
• the business case, with savings at 12, 24 and 36 months.

We start with characterization tests: tests that record what the system does today, right or wrong. Mutation testing then checks those tests would catch a break. We change the code on purpose and see whether a test fails. After that, every change has to keep those tests passing, unless you decide the behavior should change.

With the strangler fig pattern. The new service is built beside the old one, behind a routing layer. Traffic moves across one slice at a time: an endpoint, or a group of customers. The old path stays until the new one has carried real traffic without incident. If something goes wrong, the route goes back. Nothing switches all at once, so no other work has to pause.

It is usually the real knot. The assessment maps every table that more than one system reads or writes. Each system gets a boundary first, with a contracted interface in front of the shared tables, so the others stop reaching into them. Schema changes are planned and run by engineers, never by AI agents, because they can't be undone.

The way your teams build with AI, in your own AWS account. It does six jobs.

• Ground: a domain model your experts sign off.
• Orchestrate: the method, the AI agents and the tools they use.
• Control: the harness, with an identity and a policy for every AI agent.
• Prove: evals that gate every change.
• Run: canary releases, with rollback.
• Improve: experiments that keep only what beats today.

It's a way of working backed by today's tools. New tools can replace old ones without changing how the work gets done.

The harness is everything around the AI model that makes it safe and useful: its instructions, tools, memory, permissions, checks and logs. We build it before any AI agent runs, so fast code can't leave unchecked. AI agents that build software and AI agents that act on your systems run in separate environments, with separate keys.

Tests check code that should give the same answer every time. Evals check AI, which doesn't. We build a golden set from your real cases and your experts' answers. We also keep a sealed holdout that no AI agent or prompt author ever sees. Every critical case has to pass on every run, not on average. Evals gate every change in CI and keep running on live traffic.

AI-DLC, the AI-Driven Development Lifecycle, an open method from AWS. Your experts and ours write the spec together, and the work is cut into small units. AI agents write the code. Tests and a named person gate every change. Each piece is small enough to check before the next begins.

The leading models, such as Claude, running on Amazon Bedrock inside your AWS account and under your rules. The model for each task is chosen by eval on your own work: quality, latency and cost per task. Because the evals are yours, swapping a model later is a comparison that takes days, not a rebuild.

Both. Moving a workload to AWS as it is (rehost), or with small changes (replatform), is a migration. Rebuilding it so it is easy to change (refactor) is modernization, and that is where the knot comes undone. The assessment says which path each system should take, including the ones to retire. We plan the migration and the modernization together, so nothing is moved twice.

MAP is AWS's program for funding migrations and modernizations. It runs in three phases: assess, mobilize, and migrate and modernize. For workloads that qualify, AWS can put credits or funding toward each phase. Our assessment fits the first, the proof of concept and the foundations fit the second, and the AK Evolve work fits the third. AWS decides what it funds. We hold AWS's Migration and Modernization Competency, and we put the case together with you.

Yes. AWS Transform is Amazon's agentic AI for modernization, and we use it for the bulk conversions. AK Evolve is the work around it.

• The assessment decides what goes through it, and in what order.
• Characterization tests prove the converted code behaves like the old code.
• Our AI agents and engineers take the rest: business rules, the seams between systems and the shared databases.
• We manage the switch-over while the business runs, and your engineers approve every change.

We bring in AWS's newest modernization tools as they arrive, such as AWS Transform's continuous modernization.

Yes, we're a Databricks consulting partner. When the knot is an old data warehouse and the jobs that feed it, AK Evolve moves it onto Databricks. Lakebridge profiles the old warehouse, converts the SQL and reconciles the numbers against the old system. The pipelines are rebuilt on Lakeflow. Access rules and lineage live in Unity Catalog, so every table has an owner and a history. AI agents on that data can then be built with Agent Bricks.

Lakebridge reconciles the new tables against the old warehouse, table by table. The old and the new run side by side until the numbers match. Only then is a job switched off in the old warehouse.

Yes. Databricks runs on AWS. Applications and AI agents stay on AWS services such as Amazon Bedrock. The data, its pipelines and its access rules sit on Databricks. A pipeline change goes through the same checks as an application change.

The method works on any cloud: the assessment, the tests, the switch-over a slice at a time. Our ready-built parts run on AWS today. So on Azure or Google Cloud more is built by hand. The plan and the price say so up front.

No. The AI agents that read your code and the AI agents your engineers lead all run inside your own AWS account, and everything we set up stays there.

One of your engineers, every time. Each change has a named owner, and nothing ships until they say yes. No AI agent approves or merges its own work.

They can't merge their own pull requests, read secrets, edit database migrations, change the production deploy, or reach a repository they weren't given. Those limits are enforced by the orchestration layer around the AI, not by asking the AI to behave. A critical finding from the security scan blocks the merge, and no approval overrides it.

It can happen, so no single check is trusted alone.

• Tests are written first and have to pass.
• A security scan runs on every change, and an AI reviewer flags what tests can't see.
• One of your engineers approves it.
• Changes go out in small pieces, to a few users first.

So a mistake stays small and is rolled back. Then it becomes a new test.

Static analysis (SAST), secret detection and dependency scanning run in CI on every change. Amazon Inspector checks the dependencies and the images the code ships in, and AWS Signer signs what is released. An AI reviewer adds a second look, and a person approves the change before it ships.

Each AI agent has its own identity, least-privilege IAM permissions and short-lived credentials. Policy is deny by default, written as code, and enforced where the AI agent calls a tool. Untrusted content is kept apart, so no single AI agent holds private data, untrusted input and a way out together. We red-team against the OWASP Top 10 for LLM applications and for agentic applications. Every attack that works becomes a regression test.

It's designed to. You get a full record of every change: who asked for it, what changed, which checks it passed and who approved it. The record can be exported for your auditors, and each control is mapped to the standards you name. That is a mapping, not a certificate.

Yes. You keep your change board. Every change reaches it with its evidence attached: the tests it passed, the checks it ran and who approved it. That's usually what lets a board pre-approve small, routine changes and spend its time on the big ones.

• A free 90-minute workshop on your own systems.
• A fixed-fee assessment, two to three weeks.
• A proof of concept: one real system taken through the whole method. You see it work on your own code before you commit to more.
• AK Evolve then unties the core in pieces of 8 to 12 weeks. Systems that don't depend on each other go at the same time. Each has a go or no-go before it goes live.
• AK AI Factory rolls out to your teams alongside.

Yes. The assessment's dependency map shows which systems can be untied independently. Those run side by side, each with its own team and its own go or no-go gate. AK AI Factory runs alongside from the first piece, and a data warehouse move can run as its own track. Two systems that share a database wait until the boundary between them is in place.

The workshop is free. The assessment is a fixed fee. Each piece of work after it has a fixed scope and a fixed price, agreed before it starts. Where a workload qualifies, AWS's MAP funding can cover part of it.

We measure where you start in the first weeks, and again at the end. We use the four DORA measures of software delivery: how often you deploy, how long a change takes to reach production, how many changes fail, and how fast you recover. The dashboard stays with you.

That the money you've put into AI starts reaching customers. You get a fixed scope for each piece of work, a business case at 12, 24 and 36 months, and a dashboard that shows how fast you ship.

If your core already changes easily and you release every week, you don't need the AK Evolve half. If you want advice or training rather than changed systems, a hands-on Build with AI session fits better. And if a big-bang rewrite is the plan, we're not the team.

They lead the work with us. From the first week they pair with FDEs on real changes, so the practices are learned by doing them. By the end, each of them leads a small crew of AI agents. Before we step back, your team runs a drill without us. They deploy a change, restore from a backup and take the on-call rota. Your team owns everything we built together.

No. Everything runs in your own AWS account, and you keep:

• the rebuilt services and all the code;
• the AI agents and their harness, with full source: tool servers, policies, tests and eval sets;
• the CI pipelines, with the tests and security scans;
• specs, architecture decision records and runbooks;
• the DORA dashboard and the record of every change.

We keep only the generic patterns we learned. Never your data or your business logic.

Big rewrites tend to fail in three ways.

• Everything switches at once, so one fault stops the business.
• Nobody wrote down what the old system did, so the new one misses rules people relied on.
• The work runs for a year before anyone sees a result.

We design against each. Traffic moves a slice at a time, with the old path kept. Characterization tests record today's behavior before anything changes. And each piece takes 8 to 12 weeks, with a go or no-go before it goes live.