AI writes code in seconds. Reviewing it takes days.
Coding agents did not speed up delivery. They moved the bottleneck into your pull request queue. We set up your repo so each change is proven before a human looks at it, so reviewers read a one-page receipt instead of a long diff.
Every engagement is run by the founder, a former Head of Software Engineering.
See how a team’s code goes from agent to shipped
A walkthrough of how each change is checked, reviewed and rolled out, and how the rules get better after every run.
Want this mapped onto your own repo?
Book a scoping callGenerating code is free. Verifying it is expensive.
Three messages from one standup. Each is a gap around the agent that a KaaS engine closes.
- Fixed by KaaS Review
Senior engineer
The same two of us read every agent pull request. The queue is longer than the sprint.
- Fixed by KaaS Verify
Tech lead
Every check is green and checkout is broken. The agent wrote the tests too.
- Fixed by KaaS Review
Engineering manager
Reviewers approve in a minute because the diff is too long to read.
Code is for machines to run.
Receipts are for humans to review.
When an agent gets it wrong, fix the spec or the rule, never the pull request.
How KaaS runs inside your repository.
Every change takes the same five steps, from the ticket to the receipt a reviewer reads.
- The ticket becomes a spec.KaaS Spec sets the acceptance checks before any code is written.
- The agent keeps the rules in mind.KaaS Context pins your architecture and rules, so a long run does not forget them.
- The run happens in a box.KaaS Sandbox isolates the agent and wipes the box when it finishes.
- Hidden tests try to break it.KaaS Verify rejects a change on tests the agent never saw.
- The diff collapses into a receipt.KaaS Review shows hidden tests passed and boundaries held.
Built for teams at any scale
The problem looks different at 10, 80 and 400 engineers. The first step is the same: the 2-week Diagnostic.
Startups, 1 to 15 engineers
The founder reviews agent pull requests late at night, and there is no QA team.
Scaleups, 15 to 100 engineers
Headcount doubled, the pull request queue is 30 deep and staging keeps breaking.
Enterprise, 100+ engineers
Agent tools are scattered, model spend sits on many cards and there is no audit trail.
A system that learns from every failure.
Failed runs and human overrides become updated repository rules. A rule that raises cost or causes regressions on your past tasks is blocked before it ships.
Month 1
- Updated
Month 2
- Fixed
- Updated
Month 3
- Updated
- Kept
Next
Factory paused. No new rule updates from us. Every rule and check above stays in your repo and keeps running in your CI.
Built by an engineering leader who rejects invasive software.
Nothing runs on developer laptops. Specs, rules and checks are version-controlled files in your repo, built on standard open tools.
Every rule, test harness and verification workflow stays in your repository and keeps running in your CI.
- Specs and rules as files in your Git repoKaaS engineer with your Tech leadTech lead
- Verification workflows in your CIKaaS engineer with your Senior engineersSenior engineers
- Checks on standard open toolsKaaS engineer with your Platform leadPlatform lead
- The delivery reportKaaS engineer with your Engineering managerEngineering manager
“You work with the person who does the work. No handoff, no juniors, no account manager.”
Paramanantham HarrisonFounder, KaaS.team
- Former Head of Software Engineering
- 14 years building production software
- Architect of the pivot behind an €11.6M Series A
Start small. Prove it on your repo. Then roll it out.
Three engagements, each standing on its own. Written scope and a fixed quote before anything starts.
Step 1
2-week Diagnostic Sprint
A read-only look at your git and PR history. Its fee counts toward the next step.
Step 2
12-week Factory Transformation
The engines built in your repos, with an exit check each phase, and your team trained to run them.
Questions teams ask before they book
Who is this for?
Teams whose engineers already use AI coding agents and whose reviews have become the bottleneck. You want the setup in your own repo and CI, and someone senior will own the change after we leave.
Who is this not for?
Teams that want a tool picked and resold to them, or a strategy deck in place of changes in the repo. If nobody on your side writes code with agents yet, start with training.
Do we have to use Claude?
Training and rollouts are built on Claude Code. The engines work with whichever coding agents your teams already pay for.
Where do the KaaS engines run?
In your own repo and CI by default. Your code, config and data stay there, and you keep everything when we finish.
Who does the work?
The founder, a senior engineer, does every engagement. There is no handoff to juniors and no account manager.
How do you price?
Fixed scope, with from-prices on the pricing page. After the scoping call you get a written scope and a quote before anything starts.
What if we only want training?
That is a common start. Many teams take a workshop first and decide on the rest later, or never.
What will you report?
Where you actually land, including where nothing improved. A report with no red rows is not one leadership should trust.
Stop reviewing AI syntax. Start verifying production software.
A 30 minute call with the founder about where work is stuck.
- Spec
- Build
- ReviewWaits here
- Release
- Where work is stuck
- Spec, build, review or release: wherever changes wait longest.
- What exists today
- The AI tools your teams use and the rules around them, if any.
- What the next quarter needs
- The launch or deadline the work has to hit.
- 1
Call
We find where the work waits and whether we can help.
- 2
Fixed scope
You get a written scope and a fixed quote before anything starts.
- 3
Kickoff
Work starts with your product and engineering leads.
If we're not the right fit, we'll say so.
Pick a time
Open the calendar and choose any free slot. You get an invite with a video link.
