The same columns and cards
The same assignees, statuses and comments - no parallel AI interface.
Managed AI-SDLC · for CTOs with a small team
The team you have, shipping 2-3× faster.
You approve the result, not the pull requests. Every task runs through a controlled loop and stops at your decision - nothing ships without a human yes.
For a CTO with a small team who needs the roadmap to move - without doubling headcount or turning yourself into an AI babysitter.
Book a call instead See how the loop works
The assessment: we review your repo, your process, and one real task from your backlog, then show where the loop cuts delivery time - with an estimate. Free, no slides.
3×shorter time from task to decision
2 - 3×smaller standing team
95%target test coverage with TDD
every stageleaves documentation up to date
Results apply to typical flows after the loop is configured and depend on the starting maturity of the process.
Two modes of one system
One control loop. Different levels of automation.
In AI support mode a person drives the card and calls AI for a specific stage. In AI autopilot mode status and confirmations pick the next independent role. Both modes use one board, one repository and one set of rules - and both end in a human decision.
Familiar interface
For the team it is ordinary Kanban
A person does not need a new AI console. The task moves through the same columns and cards, and AI roles write into it like colleagues. The next role reads the card and the exact artifacts the way an independent participant would.
AI roles write into tasks
AI comments look like messages from ordinary participants, not a hidden shared chat.
The same repositories and CI
Links point to the same Git and CI. One task ties tracker and repository together.
Verifiability
How AI software development becomes verifiable
One end-to-end path with no internal detail. Every transition has a basis to move on. On an error the work returns to a new corrective cycle, and prior confirmations are kept.
- 1ContextTask and definition of done are fixed.
- 2TDD / REDTests describe the behavior and fail before implementation.
- 3ImplementationCode within bounds, tests turned green.
- 4Independent reviewResult reproduced by a separate role.
- 5CI and reviewBuild and automated checks passed.
- 6Business and securityBusiness rules and access policy satisfied.
- 7Human decisionBasis collected, a human moves it to Done.
Leadership view
What leadership gets
For the CTO
- speed is separated from the right to accept a change
- traceability is kept from task to final check
- AI authority is bounded, and the impact of an error is localized
- you see the state of delivery, not agent activity
For the IT lead
- every stage has a clear input and output
- a product defect is separated from an infrastructure failure and missing context
- checks reproduce on the exact version
- notes return the work to the right stage
How it compares
Not another hire. Not an agency. Not you at 2am.
The same roadmap, four ways to move it. Only one keeps the decision with you and the delivery off your plate.
| In-house hire | Agency / offshore | You + Copilot | AI Agent Labs | |
|---|---|---|---|---|
| Time to first shipped feature | Months to hire, then ramp | Weeks, limited visibility | Your nights and weekends | Days, on a running loop |
| Who runs it day to day | You manage and review | A PM you have to chase | You, between founder duties | The loop runs, you approve |
| Tests and independent review | Depends who you hire | Varies by vendor | Whatever you have time for | TDD and independent review built in |
| What ships without you | Nothing moves without your review | You find out at the demo | You are the bottleneck | Nothing ships without a human yes |
| Cost shape | A full senior salary, loaded | Per-sprint, often opaque | Free, until it costs the roadmap | Fixed monthly, scale up or down |
| If it is not working | Hard to unwind a hire | Locked into a contract | Still all on you | Configured into your process, cancel anytime |
Proof
Two people, three months - a full platform, tested, secured, documented
A creator marketing management platform built from zero under the managed loop - the kind of scope that normally needs a full squad with its own QA and DevOps.
Case 01
- Team
- 1 senior full-stack engineer + a part-time analyst
- Not on the team
- no separate QA, no DevOps
- Timeline
- 3 months to a shipped system
What shipped
1,800+ automated tests
built with the code across the system, not added before release
Production CI/CD
automated build, checks and independent review
Security hardening
security controls built into the delivery, not bolted on at the end
Living documentation
kept in the repository and current with the build
The loop covered the roles a two-person team cannot staff - independent review, test discipline, CI/CD and security - so a small team shipped what usually needs a full one.
Why AI Agent Labs
Senior builders behind the loop - not a body shop
Our team has delivered for large enterprises in finance, insurance, mining and energy - and we love working with small teams. You get senior people who build, with no layers between you and them.
- 100+
- projects
- 15+
- specialists
- 10+
- years
- 461M
- broadcast views on a real-time platform we architected
Mike Sadofyev
CEO and Founder
Ex-PwC, Accenture, and UniCredit. PhD in Optimization and Game Theory, MBA, PMP. 100+ enterprise AI projects across mining, energy, finance, and insurance.
Mike on LinkedIn15+ senior specialists: data scientists, ML engineers, and domain experts. Everyone codes. No layers between you and the builders.
- MSMikeCTO
- MVMaxTechnology and AI
- SKSashaStrategy, Corporate Finance and M+A
- RURuslanHead of Business and Systems Analysis
- MOMariiaCOO
- GSGeorgyCMO
- YTYuryArt Director and Design Lead
- AKAlexDelivery Lead and SME, Metal and Mining + Oil and Gas
- IVIvanInfrastructure and DevOps Lead
- ARArtemHead of QA
- IVIvanSME, Master Data and Industry
- PPPavelSME, Procurement and Supply Chain
- NSNickSME, IFRS and 1C
Delivery, in production
98% of claims automated, 40+ FTEs redeployed, seconds per claim.
200 to 4,000 documents per month in six months.
3 days to 15 minutes, 946 records at about 90% accuracy, 70% auto-filtered.
See all delivery cases
These systems ran in production for our team's enterprise work - we hold product teams to the same standard. Anonymized for confidentiality, metrics verified.
“Three days per tender became fifteen minutes - the team stopped drowning in line items and started deciding.”
Our team has built and delivered at
PwCAccentureEYKPMGUniCreditAngloAmericanAkerBPXBRL International
Industries and companies our team has worked with across prior roles.
Selected by
Alchemist AcceleratorFounder University
Security and control
Safety comes from the loop, not a promise
Two questions every team asks: can the AI break something, and can our code leak. Both are answered by how the loop is built - bounded authority and a private perimeter - not by trusting the model.
The AI cannot break your project
- AI acts only inside the limits set for a stage - it has no authority beyond them
- every irreversible step is a human action - only a person moves a card to Done
- no role approves its own work - developer, tester and reviewer run under separate accounts
- on any deviation the work returns to a corrective cycle and prior confirmations are kept
- the repository holds the exact result, so every change is traceable and reversible
Your code and data stay in your perimeter
- a fully private, on-prem deployment runs the whole loop inside your own infrastructure
- your code, data and models stay in your contour - the private loop needs nothing to leave it
- no hidden shared memory between agents - context lives in the card and the repository you control
- you choose the models and where they run, up to self-hosted open-weight in a private deployment
- access, roles and audit follow your existing security policies
Compatibility
Compatibility with your current stack
Below are compatibility logos, not clients or partners. The solution is model-agnostic and tracker-agnostic and runs on top of the company's current systems.
Trackers and Kanban
Models and AI agents
Deployment options
A loop that fits your data requirements
On top of a cloud stack
The loop deploys over your existing cloud models, tracker and CI/CD.
Hybrid loop
Some stages in your infrastructure, some in external services under your policies.
Fully private loop
Models, agents and repositories inside the company's infrastructure, with no external APIs.
The exact configuration depends on APIs, access policies, data requirements and the maturity of the current SDLC.
Transition program
Transition program with AI Agent Labs
- 1
Diagnostics and transition route
Assessment of the SDLC and DevOps, constraints, risks and baseline metrics. A phased rollout plan.
- 2
AI-native engineering environment
Models, agents, repositories, CI/CD and tracker, including a fully private deployment.
- 3
Processes and operating model
Roles, areas of responsibility, control points and team structure.
- 4
Unlocking senior specialists
A shift from manual execution to architecture, task definition, quality and growing AI teams on the company's real flows.
- 5
Control and measurable effect
Quality and security rules, metrics for speed, cost and adoption, and regular improvement of the loop from data.
Start with a single direction or assemble the full transformation program.
Pilot
How to start
- 1
Pick one repeatable software delivery flow
- 2
Fix the baseline metrics and mandatory checks
- 3
Start with AI support
- 4
Move stable stages to AI autopilot
- 5
Keep the final decision with a human
Free loop assessment
What you get before you commit anything
One flow, one real task from your backlog, one number. No prototype to babysit, no access to production.
- 1
We look at the real work
A short call plus read-only access to one repo and one delivery flow. We pull a real task from your backlog, not a demo.
- 2
We measure the baseline
Cycle time, rework, and review load on that flow as it runs today - the number the loop has to beat.
- 3
You get a written plan
Which stages move to autopilot, the target metric, and the human gate on Done. Yours to keep either way.
The metric goes in the contract
The baseline we agree on becomes the KPI you accept against. You sign off on results measured against that number, not on pull requests. If a stage does not move it, it is not Done.
FAQ
Frequently asked questions
Do we need to replace Jira, Plane or GitLab?
No. The loop runs on top of your tracker and repository. It is tracker-agnostic and fits into the same columns, cards and merge requests your team uses today.
Can it be deployed fully private?
Yes. Cloud, hybrid and fully private loops in your own infrastructure are available. In a fully private deployment your code, data and models stay in your contour. The exact configuration depends on access policies and data requirements.
Can the AI delete code or ship on its own?
No. The AI works only inside the limits set for a stage and stops at ready for human decision. Every irreversible step - merge, release, moving a card to Done - is a human action. No role approves its own work, and the repository keeps every change traceable and reversible.
How is this different from plugging in Codex or Claude Code?
A standalone coding agent performs a step. Managed AI-SDLC sets the boundaries for that step, adds independent review, TDD, CI/CD and traceability, and stops before the human decision. It is a control loop, not a single agent.
Who is responsible for an AI error?
AI authority is bounded by the set limits, and the impact of an error is localized to the stage. On a deviation the work returns to a corrective cycle, prior confirmations are kept, and a human makes the final decision.
Can we start with AI support only?
Yes. In AI support mode a person drives the card and calls AI for a specific stage. Stable stages are later moved to AI autopilot on the same board and under the same rules.
Does AI close the task automatically?
No. AI takes the work to the point of a decision and stops at ready for human decision. A human moves the task to Done.
How is the effect measured?
Against the flow's baseline metrics and mandatory checks fixed before the start: time from task to decision, test coverage, stage stability. Automation prepares the decision, but Done stays with a human.