The software factory is real. Building one alone is brutally hard.
The backlog clears without hiring, and your engineers run the line, not every ticket on it. We bring the blueprints and the training, build it with your team, and leave you owning it. No lock-in.
The assessment takes 3 minutes. You get your team's level and a peer benchmark by email.
Trusted by engineering leaders in healthcare, cybersecurity, and enterprise software. Featured on Fraunhofer IEM and the AI Agents Podcast.
The tools moved the needle. Now make it a system.
- Everyone's got a license. Two devs are flying, the rest still use it like fancy autocomplete.
- Every dev works with AI their own way. Output quality depends on who wrote it.
- You see movement, but you can't say what's driving it or what to scale.
- And the setups that work live on personal laptops, not in your repos.
One process, measured centrally, owned by the company. That's the software factory.
What 2026 actually looks like
90%
of developers regularly use an AI tool at work
JetBrains AI Pulse, January 2026, 10,000+ developers
64%
of teams report 25%+ velocity gains from AI. The gains are real.
Jellyfish, State of Engineering Management 2026
2.3 vs 1.4
PRs merged per week: daily AI users vs. the rest of the same org
DX, Q4 2025 Impact Report
10%
of orgs have strong enablement and high adoption. The rest run on individual effort.
Jellyfish, State of Engineering Management 2026
A machine that runs your SDLC, end to end.
Plan, build, test, review, ship: one loop, run by agents behind your quality gates. You feed it intent and judge the output. Every fix becomes a rule, so the same defect can't come back.
Agentic coding in the IDE speeds up implementation. But a faster middle strains both ends: reviews pile up, the product team can't feed stories fast enough, and quality slips. A factory runs the whole line as one flow, from intent to ship.
Engineers feed the loop with intent and judge the outcomes, instead of managing every line of code. They set the direction, tune the guardrails, and decide what ships.
Quality gates between every stage
Six levels of AI-assisted engineering.
Like driving automation, AI-assisted software development has defined levels. Knowing your level is the first step. Knowing you can't skip levels is the second.
Manual coding
You write everything yourself.
Human oversight
100%
Conversational prompting
Copy-paste from a chat window.
Human oversight
~95%
Inline suggestions
Most teams are hereAutocomplete-style completions. Where most teams are stuck.
Human oversight
~80%
Contextual pair programmer
Full repo context, in the IDE.
Human oversight
~65%
Agentic engineering
Scoped ticket in, reviewed PR out. Where the measured gains live.
Human oversight
~50%
Your Software Factory
We take you hereAutonomous AI software delivery. Vision and specs only.
Human oversight
<20%
Two hard truths
You can't skip levels.
Each level builds on the discipline of the one before: coding standards, CI/CD, review culture, test coverage. Level 4 on a Level 1 codebase produces expensive chaos.
Every level up is more organizational than technical.
Level 1 needs a legal sign-off. Level 3 needs shared standards. Level 5 needs new roles and new incentives. The technical effort shrinks per level. The organizational effort grows.
"We save hours each sprint with dependable story refinements and test plans, without adding complexity to our workflow. Codeligence has been reliable and easy to work with."
The first call is about your team, not our offer.
The two problems CTOs call us about.
Usually it's one. Sometimes both.
Problem one
A few devs are flying. The rest never took off.
Two or three devs ship like never before. The rest tried it, got burned once, and went back to typing. Your seniors still call it autocomplete.
We train the whole team on your codebase, seniors and skeptics included, until agents are just how they work. Your seniors become the architects of the factory, not its victims.
Problem two
The gains are real. The process is anyone's guess.
Same task, same model: one dev needs five minutes, another forty. Ten private workflows, no shared steps. And you're tracking seats, not outcomes.
We turn your best devs' workflow into the team's workflow: written down, versioned in your repos, one set of quality gates. Baselined and measured, so you know what to scale. That's the software factory, and it's yours.
Not sure which one describes your team? The 3-minute maturity quiz tells you.
From first call to a team that ships differently.
No packages to compare. We look at your team first, then talk scope.
Take the maturity quiz
3 minutes
Eight questions about how your developers actually work with AI. You see what level your team is on, and how that compares to your peers.
Scoping call
30 minutes
You tell us how your team works with AI today. We tell you what we'd do: how many sessions, which roles, in what order. Price comes once the structure is clear, not before.
We work with your team
Days to weeks
Training, coaching, and hands-on setup on your own repo. Real stories from your backlog go through the new setup before we leave, and your leadership gets a final report.
Keep it sharp
Optional
Models and tools change every few months. Book a recurring session, weekly, monthly, or quarterly, and we keep tuning what your team built.
The first call is about your team, not our offer.
What engineering leaders say.
"We were using Claude Code but treating each session as a one-off, with no shared conventions across the team. Now we approach it as an orchestration model, not a coding assistant. The goal shifted from AI helps us code faster to AI handles implementation while we supervise architecture and outcomes."
Bryan Scown
Director of Software Engineering, Valant
"We save hours each sprint with dependable story refinements and test plans, without adding complexity to our workflow. Codeligence has been reliable and easy to work with."
Adam Lundqvist
CEO - Cybersecurity Company
"Working with Codeligence was completely uncomplicated. We were able to evaluate and start using AI technology quickly and with minimal effort. Getting started was effortless."
Tobias D. Kern
CEO - Digital Agency
The thinking behind the method.
Founder Juri Kuehn on why AI adoption stalls at autocomplete, and what it takes to get an engineering organization to agentic delivery.
AI Agents Podcast
EP85 - Building AI Enterprises with Codeligence
with Demetri Panici
Fraunhofer IEM
Prof. Dumitrescu Tech Talk #43 - KI im Software Engineering
with Prof. Dr.-Ing. Roman Dumitrescu
Practitioners, not slide decks.
We ship with agents daily
30 years of shipping software. Our methods come from our own production work with AI agents, not from a consulting framework.
Measured, in writing
We baseline first, write the measurement protocol into the contract, and give you before and after numbers from your own repos in the final report.
You own it, no dependency
We build the factory with your team, not to lock you in. It lives in your repos and your team runs it when we leave.
Vendor-neutral on tools
Claude Code, Codex, Copilot. We set up what fits your stack and measure what matters. We never sell tool training.
Find out where your team stands today.
Get Your AI Maturity ScoreWhy do 95% of AI pilots fail?
Research reports with cited sources on AI adoption, developer productivity, and what separates measurable gains from adoption theater.
Find out what level your team is on.
Eight questions, three minutes. You get a scored level, a peer benchmark, and the one move that takes your team to the next level.