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.
Your board wants the 5x. Another tool won't get you there.
- Adoption is universal. The gains are rare.
- Cycle time hasn't moved.
- No ROI you can defend to the board.
- Every gain is rented, and leaves when the tool does.
The real path to the 5x: a software factory you own.
The 2026 numbers behind the gap
90%
of developers regularly use an AI tool at work
JetBrains AI Pulse, January 2026, 10,000+ developers
~8%
median gain in delivery throughput, even as AI use rose 65%
DX, AI and Engineering Velocity 2026, 400+ orgs
54%
of engineering orgs don't track AI-specific metrics
Jellyfish, State of Engineering Management 2026
81%
of organizations report no bottom-line gains from AI
McKinsey, State of Organizations 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."
Embedded in your team. You own the factory, no lock-in. Every engagement is scoped in a call.
Two paths to your software factory.
Same destination. Pick by where your team stands today.
Factory Accelerator
Workshop · days to weeks
You already have an AI task force and want to accelerate adoption.
We close the knowledge gaps through training and set up or optimize your software factory together with your team: training, assessment, implementation, and application sessions with your product managers, Scrum Masters, developers, QA engineers, and support.
You leave with a factory proven on real stories from your own backlog. Your AI task force tracks the KPIs and drives adoption.
Good for: Teams already open to change, or already building their own factory.
Scope: Days to weeks, depending on team size and project complexity. We scope your case on a call.
Factory Transformation
Embedded FDE · months
You've introduced AI, adoption is individual, and you're ready to build the factory as an organizational asset.
We join your team and work on your product. Training sessions, 1:1 coaching, and building the software factory together with your engineers while upgrading their skills: changing habits, building a new mindset, adopting a new way of producing software.
You leave with a true software development transformation, carried by full change management. Your team truly owns the factory, and the change lasts.
Good for: Teams that want to sustainably upgrade how they build software, own the asset, and stay in control of their AI.
Scope: Months. We stay until the rough edges are gone.
Both paths end the same way: your team owns the factory. A board-ready final report is included: where your team was, where it is now, what it owns, and the baseline your KPIs are tracked against.
Not sure which path? The 3-minute maturity quiz tells you.
After either path: an optional Operating Partner keeps your factory sharp as models and tools shift.
Stop renting AI productivity. Own the machine that makes it.
Transform how your team builds software. Keep the factory.
You rolled out Copilot, Cursor, Claude. Adoption is up, the metrics aren't. We embed a forward-deployed engineer who builds a custom AI software factory inside your SDLC, on your repos, and ships your real backlog through it as proof.
We build it hands-on with your engineers, so they own it, not just inherit it. When we leave, the factory stays, your team runs it, and you don't pay us per seat to keep it.
What we build, and you keep
- Your own software factory wired to your repos
- A real assembly line for code: intent scoped into a plan, agents that build it, tests that update themselves, and an independent reviewer that signs off before anything ships
- Your architecture and conventions captured as skills and memory that your whole team feeds and improves
- Quality gates and guardrails for style, security, and correctness at every step
- Visibility into what the machine ships and what it spends
- Real backlog stories shipped through it, so the proof and the ROI arrive together
What you own when we leave
- 1 The configuration that makes the factory yours: context, profiles, evals, guardrails
- 2 All of it living in your repos, versioned and reviewed by your engineers
- 3 The right to change it, extend it, and keep running it long after we leave
- 4 Your context and workflows as a company asset, held by the organization instead of scattered on individual laptops, so the knowledge stays when people leave
- 5 Your team running it before we leave, no sharp cutover
What you're paying for.
You already carry the budget for one senior or AI-specialized engineer. This costs a fraction of that one hire over a year. The difference: that engineer leaves with one feature shipped. We leave a factory that multiplies the whole team, your team trained to run it, and your first real features already shipped through it. You're buying the machine, not the hours.
See the Factory TransformationEmbedded in your team. You own the factory, no lock-in. Every engagement is scoped in a call.
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.