We Help Engineering Organisations Become AI Native
We build software factories with your engineers, train them on the new way of working, and modernise the legacy code that holds them back.
Trusted by engineering teams at
How We Work
Vision & Outcome
We align on goals, constraints, and success criteria before any investment
Assessment
We analyse your systems, data, and architecture for AI readiness
Roadmap
A gap analysis and prioritized roadmap, written at our cost
Delivery
We build with your engineers, document everything, and train your people to run it
You see what you get and what it costs before committing further.
We use this framework to pinpoint
Exactly which stage your company is on
Not sure where you stand? Find your stage in 4 minutes →
Discovery
Getting AI into the company: access, then coordination
How you recognise it
- No formal adoption yet.
- Engineers experiment individually, on personal accounts.
- Leadership is aware of it without acting on it.
What holds you here
Organisational permission. The stage ends when leadership sees enough to fund a pilot.
Typical next move
Get a funded pilot
Stage 0 ends when leadership sees enough to fund a formal pilot. Make the case with a scoped team, a time box and the evidence you will bring back.
How you recognise it
- A pilot group, usually 5–10% of engineers, has formal access.
- The most motivated engineers learn quickly.
- The rest of the organisation has no reliable signal about whether any of it is working.
What holds you here
The missing signal. The stage ends when the pilot produces credible evidence on adoption, satisfaction and output.
Typical next move
Make the pilot produce evidence
Take a baseline before the pilot starts, then report adoption, satisfaction and output against it. Without that signal the rest of the organisation cannot tell whether it is working.
How you recognise it
- Most engineers have access and use AI at least sometimes.
- Everyone uses it their own way, with different tools, prompts and conventions on the same codebase.
- Output rises, quality varies, and the dashboards look good — which is why the plateau stays invisible from the top.
What holds you here
Coordination. The stage ends with shared standards, structured education, and a pilot team that redesigns its workflow first.
Typical next move
Decide tools and guardrails before expanding
Pick the agent harness, the security model, the context standard and the review process, then encode the standards in linters, hooks and CI policy so they apply to every change.
How do we adopt AI without it going sideways?
Is this you?
Discipline
Making AI work for the team: review, context, security, ownership, ops
How you recognise it
- Standards are enforced through tooling, linting and CI, and most developers have moved from autocomplete to agentic work.
- Output arrives faster than senior engineers can carefully read it, so reviewers skim, trust the tests, or trust the author.
- Test coverage rises while the team’s actual confidence in the code falls.
What holds you here
Review, because output now arrives faster than senior engineers can carefully read it. The stage ends when review has been redesigned and the test suite is trusted.
Typical next move
Redesign review for AI-generated code
Write down how AI-generated code is reviewed differently, and enforce it in tooling: PR size limits, required checks, and an author who has read every diff they ship.
How you recognise it
- Several agents per developer is normal, running in sandboxed environments.
- Context files grow past the point where the model can make good use of them.
- Developers approve pull requests they could not have written themselves.
- An agent can hold the developer’s credentials, the team’s data and network access at the same time — the lethal trifecta.
What holds you here
Context quality, security and ownership. The stage ends with scoped agent permissions and clear ownership rules.
Typical next move
Break the lethal trifecta
Scope credentials to the task, keep production secrets out of reach and restrict network egress, so no agent combines private data, untrusted content and a way to send data out.
How you recognise it
- AI runs beyond the IDE: CI/CD, code review automation, infrastructure as code, on-call and security scanning.
- Engineering stops being the slowest stage, and the product becomes the bottleneck.
- Specifications detailed enough for human-speed work do not carry enough information for agents.
What holds you here
Scale and product flow, because engineering stops being the slowest stage. The stage ends with factory infrastructure.
Typical next move
Build the agent platform as a product
Give teams context, tools, environments, permissions and validation as self-service, so every agent task runs in its own isolated environment. This is the factory infrastructure Stage 5 ends with.
How do we hold quality as agents write more?
Already past this?Factory
Making AI work at scale: validation, governance
How you recognise it
- Agents produce most of the code, and humans focus on review and approval.
- Agents cannot reliably check their own work, so the validation pipeline sets the pace for everything.
What holds you here
Validation at machine speed, because agents cannot reliably check their own work, so the validation pipeline sets the pace for everything.
Typical next move
Build validation agents can’t game
Agents cannot reliably check their own work. Keep end-to-end scenarios outside the codebase the agents can see, and never let the agent that wrote the code decide whether it is acceptable.
How you recognise it
- Humans specify, and agents implement.
- Validation carries most of the weight that code review used to carry.
What holds you here
The frontier. Too few teams have reached this stage yet to say what they hit next.
Typical next move
Build validation agents can’t game
Agents cannot reliably check their own work. Keep end-to-end scenarios outside the codebase the agents can see, and never let the agent that wrote the code decide whether it is acceptable.
How do we validate code safely at machine speed?
Further up the ladder?Frontier
Undefined; few teams are here, practices still forming
How you recognise it
- Unknown so far. Practices are still forming, and few teams are here.
What holds you here
Unknown. We haven’t seen any teams go past the dark factory yet.
Typical next move
A placeholder, not a prediction
Stage 7 itself may turn out to be temporary, so we keep 8+ on the map as a placeholder rather than a prediction.
What does the next operating model look like?
Find out where you stand
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Our Core Services
AI Native Transformation
An end to end programme to become AI native across the organisation.
Learn More
Results we've delivered
A roadmap to reduce cloud costs and carbon emissions
40%
Potential reduction in carbon emissions
20%
Projected cloud cost savings
100%
Zombie workloads identified
AI training turned 1.5 stagnant years into a 3-day launch
400%
Increase in agentic coding usage
50% → 0%
Developer AI-hesitancy, before and after
+1,030%
Top performer's PR throughput
From fragmented systems to one AI Native data platform
3×
Fewer cloud platforms to manage
60%
Faster data pipeline processing
1 platform
Single source of truth for data & AI
A roadmap to reduce cloud costs and carbon emissions
40%
Potential reduction in carbon emissions
20%
Projected cloud cost savings
100%
Zombie workloads identified
AI training turned 1.5 stagnant years into a 3-day launch
400%
Increase in agentic coding usage
50% → 0%
Developer AI-hesitancy, before and after
+1,030%
Top performer's PR throughput
Want Similar Results?
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Trusted Globally
From European enterprises to organisations across North America, the Middle East, and Asia, our team has built platforms worldwide for two decades.
Success Validated By Clients
re-cinq made me rethink how to use AI in scaled Organisation.
Bjorn Brauel
CTO - Odevo
One person from the team doing the rewrite created a mobile app in 3 days. That's now live in both stores. Previously, no progress had been made for 1.5 years.
Tomasz Maj
Head of Product Ops & Development - Odevo
re:cinq took our fragmented tech stack — three cloud providers, mixed databases, legacy systems — and turned it into a modern platform we can scale.
Markus Brendel
COO - seabo
The re:5 platform helped Ole Larsen, a Danish logistics company, to reduce and report emissions from its 200-truck fleet.
Søren Printz Larsen
CEO - Ole Larsen
re:cinq's KubeCon talk sparked our green tech initiative. Their expertise led to recommendations that could cut our carbon emissions by 40%.
Sietse Bruinsma
Manager Digital Platforms & Cybersecurity - Vandebron
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Common Questions
Most companies stall after the first 10% of engineers adopt AI. Review and CI can't keep up with the volume, the backlog dries up, and adoption stops spreading past the early users. That's one of the stages we work in.