Bugs
Reproducible defects with limited context, isolated in one system.
Bounded.AI-Factory · SDLC harness
Agreed work, through your checks, to a merge request.
AI-Factory is a server-side SDLC harness. It runs agreed bugs and compact tech-debt tasks through a defined route — requirements, design where needed, implementation, checks, internal review — and lands a merge request in your Git. Your engineers keep acceptance and merge. Your infra. Your tracker. Your model endpoint.
- Runs in your infrastructure
- LLM-agnostic
- Multi-repo out of the box
- Your Jira · your GitLab
02 · The bottleneck
Code generation isn't the bottleneck
- 01
AI copilots make developers faster
They speed up writing code — one fragment of one technical task.
- 02
But the whole chain lies between a need and a release
Analysis, decomposition, code, QA, review, delivery. That chain still runs on people — and stalls on them.
- 03
AI-Factory runs the agreed route
An agreed task goes in. A reviewed merge request comes out. Your engineers accept or reject it.
The bottleneck isn't code generation — it's moving an agreed task through the checks your team already requires.
03 · How it works
From your tracker to a merge request
Works inside your existing process — your tracker, your Git, your infrastructure. Nothing to replace, nothing to migrate.
01 →Intake
Kniffco picks up a task from your Jira.
02 →Requirements
Analyses, clarifies, decomposes.
03 →Design
Solution design against your actual codebase.
04 →Build
Code and tests written, checks executed, in-scope defects fixed inside the route.
05 →Review
Internal code review by a separate agent, with review gates.
06Merge request
A reviewed MR lands in your GitLab. Your team stays the final gate.
Inside your networkYour repository, the agents, build, test and QA runs, the merge request.
The only thing that leavesTask context, sent to the LLM endpoint you nominate — your tenant, your contract, your region.
04 · Suitable work, controlled route
Put repeatable engineering work on a defined SDLC path.
The fit is work your team already knows how to specify: bugs everyone works around, small tasks that never fit a sprint, and tech debt with a runnable check. It sits because the team is busy shipping features, not because the path is unclear.
AI-Factory is built for that class of work, inside one repository.
Compact tasks
1–2 day changes in one service, with a clear check path.
Scoped.MVPs & prototypes
Only when scoped as agreed tasks with acceptance criteria, not open-ended discovery.
Only if scoped.Tech debt
Small, verifiable paydown in one repo. Not a platform rewrite.
Verifiable.Not everything belongs on this route: architectural trade-offs, changes that fan out across half your services, and bugs nobody can reproduce stay with your engineers.
05 · Outcomes
What changes
Without Kniffco
40%
of engineering time goes to routine work
10%
lost to idle time between steps
—
cycle time capped by who's available
With Kniffco — observed on our delivery pipeline
95–150 min
per full task cycle — intake to merge request
Parallel
execution — scale delivery without hiring
Scoped pilot
3–4 weeks, one repository, one team, written acceptance criteria.
06 · A different class of tool
Where Kniffco sits in the AI landscape
AI development tools are not one category. Each class solves a different problem:
Copilots & IDE assistants
Make an individual developer faster. The effect is capped by that developer's time and attention.
App builders
Turn a prompt into a working prototype — great for starting from zero, not built for your existing codebase and process.
Autonomous AI developers
Take a technical task and return code — a single executor, and its reasoning runs in the vendor's cloud no matter where you deploy it.
Kniffco — AI-Factory, an SDLC harness
A multi-stage harness on your infrastructure: agreed task, then required analysis and design as needed, implementation and checks, internal review gates, merge request. Your Jira, your GitLab, your model endpoint, your accept and merge authority. Not an IDE copilot. Not a prompt-to-app builder. Not pay-per-MR ticket labor.
Copilots make developers faster. AI-Factory moves agreed work through the checks you already require.
07 · Start with a pilot
See it run on your repo
Start with the AI-Factory pilot. About 3–4 weeks, one repository, one team. Agreed bugs and compact tech-debt tasks with written acceptance criteria. Your engineer reviews, and your team keeps merge authority.