We audit, train and transform your organization for AI, starting with Product and Engineering, where agents already ship real work. Then we take the operating model to every team.
Teams
Pick a team to see where it stands. Taller bars mean a higher level.
More tools do not fix that. An operating model does.
Most companies sit in the first two: people use AI on their own, or every day without shared rules. The value comes at the third, when rules, context and checks are shared, so work ships with a named owner.
People try AI tools on their own.
Teams use AI every day, each in its own way.
Shared limits, skills and automated checks apply across teams.
Every team runs on the same operating model, and a named person answers for each result.
Six questions, one for each layer. Pick the answer that sounds most like you today.
Answer to see your stage
An AI operating model is how a company decides what agents may touch, what they know, how they work and who approves the result. Every piece of AI work passes these six stations, from request to shipped.
What each agent may see and change, written down and approved by a person.
Access updates the day a new data source arrives.
The platform enforces permissions and logs every action.
The knowledge each task needs, kept in one place someone maintains.
A decision updates the shared source the day it is made.
Docs change in the same review as the code.
How your best people work, written down so every agent works that way.
The brief skill improves from what reviewers flag.
Skills are tested on real tasks before they change.
Work starts from a request and runs in its own space, many tasks at once.
Several drafts run at once from one launch plan.
Tasks start from issues and run in parallel.
Checks every piece of work passes before a person looks at it.
A failed check sends the draft back to the agent.
A failed gate sends the change back to the agent.
A clear path from request to shipped, with a named person approving.
Every team ships this way, with an owner for each result.
Shipped work is reported per team every month.
We make a company AI-native in three steps: an audit that says which team to start with and what to change first, training for the people who run it, and six weeks inside your teams.
For leaders whose teams use AI every day but cannot yet say what it has changed.
For the people who run the model after we leave: product and engineering leads, and the managers who set the rules.
See the cohortThey run the model after we leave.
We work inside your product and engineering teams for six weeks, on a launch you already have planned. Each week your people own more of it. By the end they run it without us.
Book a scoping call| Week | Kaas | Your team |
|---|---|---|
| 1 | Leads | Watches |
| 2 | Leads | Pairs |
| Your first launch planned with the model | ||
| 3 | Pairs | Pairs |
| 4 | Reviews | Leads |
| 5 | Advises | Leads |
| 6 | Advises | Runs it |
| Your team ships it; your approver signs off | ||
Each step moves the next team forward. Pick one to hold it.
| Team | Audit | Train | Transform | Scale |
|---|---|---|---|---|
| Engineering | Assisted | Assisted | Governed | AI-native |
| Product | Experimenting | Assisted | Governed | AI-native |
| Design | Experimenting | Experimenting | Assisted | Governed |
| Marketing | Not started | Experimenting | Experimenting | Governed |
| Operations | Not started | Not started | Experimenting | Assisted |
| Finance | Not started | Not started | Not started | Assisted |
What changes once the operating model runs in every team.
Every team runs on the same limits, context and skills, instead of a different setup in each.
You decide where agents act alone and where people approve.
What reaches a reviewer has already passed automated gates.
A named person stays accountable for every change that ships.
Senior engineering judgment, applied to how your teams use AI.
KaaS.team is run by Paramanantham Harrison, a former Head of Software Engineering who has built production software for more than 14 years. At Jobbatical, Param led a function of 13 across product engineering, data science and site reliability. Param was also the key architect of the pivot that became the core business behind its €11.6M Series A.
The Software Factory runs on Param's own codebases before it reaches yours. Recent work includes an agentic case manager with human review designed in, and the engineering behind an ISO 27001 certification. You work with Param directly, from the first call to a working factory on your codebase. No sales handoff, account manager or agency layers.
No. We start with product and engineering, where agents already ship real work, then take the same operating model to other teams.
No. The build happens in isolated branches and environments, alongside your team's normal delivery.
No. We cover the system around AI rather than recommending a vendor.
Read access to your repository, test scripts and CI configuration is enough for the audit.
A 30-minute call about your teams and where they are today. No sales handoff.
We talk through your teams and where you are today.
You get a fixed quote for the work.
We start with your first team.
Open Param's calendar and choose any free slot. You get an invite with a video link.