Why it stalls (in a software company it's almost always this)
You already have good engineers and the AI tools. If speed doesn't rise —or AI doesn't get into your product— it's rarely a talent or a licences problem.
AI accelerates the wrong stretch
Writing code goes faster; deciding what to build and verifying that it works does not. The bottleneck moves, it doesn't disappear — and the debt grows.
Your product's AI stays a demo
RAG and agents work in the demo and break with the real case, because they don't know your product or have reproducible quality criteria. They never reach production.
The cost eats the margin
Without cost per useful task under control, the AI bill rises while the value delivered doesn't scale. And nobody knows what it will cost to maintain a year from now.
What changes when the method is right
Your team delivers faster, without debt — speed with specification and verification, not "prompt and ship".
AI gets into your product and holds up — RAG and agents in governed production, not in the eternal backlog.
A cost you can defend before your committee — cost per useful task measured, not a quarterly surprise.
And it stays in your team — the method and the context are yours; you don't depend on whichever vendor.
Not theory: product companies that have already done it
The same method —Spec-Driven Development, context engineering and human verification— applied to software and digital-product companies.
From constantly missing deadlines to 1 predictable weekly release in a B2B SaaS.
Plannet: a cross-platform social MVP, 100% cloud-native, ready to validate the market.
Hacktua: an app with 380+ pieces of content and a personalised profiling algorithm over sensitive data.
How we do it
We don't reinvent a method per sector. We gather how your product and your business really work and turn it into the context your AI uses, with human verification at every step and the cost measured from day one.
We call it context engineering. The technical detail, here: context engineering vs. prompt engineering, dual-track Scrum accelerated with AI and how to integrate AI into your SaaS product without mortgaging the roadmap.
Guides for software and SaaS companies
Everything we've learned taking AI to production at product companies — committee decisions, not theory.
AI agent in your product: build or partner?
The €50-300k decision: build the team in-house, buy, or bring in a partner that transfers.
Integrate AI into your product without mortgaging the roadmap
RAG and agents that work in the demo and break with the real case. How to take them to production without stopping delivery.
How to measure whether your product's AI works
The only two metrics that matter —verified quality and cost per useful task— and how to instrument them.
Claude vs Cursor vs Copilot for enterprises
Which to choose for your team — and why the tool is the easy 20%; the method decides the ROI.
Dual-track Scrum accelerated with AI
Accelerate the team's delivery without piling up debt or losing sprints to the transformation.
Context engineering vs. prompt engineering
Fine-tuning the prompt has a ceiling; building the context does not. What separates the demo from production.
Where we start
Depending on where it pinches today: accelerating the team that builds, or taking AI to your product.
Frequently asked questions
How does AI apply to a software or SaaS company?
On two fronts. Inside: accelerating your development team to deliver faster without piling up debt (method, not just licences). And in your product: putting AI —RAG, agents, LLM features— in so that it reaches production with cost and quality under control. In both cases the point is not the tool, it's the method that makes AI understand your business and be operable.
Why does my product team have AI copilots and not go any faster?
Because AI accelerates writing code, which is rarely the bottleneck. The jam is in deciding what to build and verifying that it works. Without a method that orders both —and with AI generating faster without specification or tests— you multiply speed and debt at the same time. The problem is not Copilot or Cursor; it's the method around them.
How do I put AI (RAG, agents) into my product without mortgaging the roadmap?
With context engineering and verification at every step: gathering how your product and your business work, codifying it as the context the AI uses, and taking each feature to governed production with the cost measured from day one. That way your product's AI is consistent and auditable, not a demo praying not to break — and you don't stop the roadmap to get there.
What results have you achieved at software companies?
Three real examples: a B2B SaaS went from missing deadlines to 1 predictable weekly release with ×8 speed and 0 lost sprints; Plannet took its social MVP to production in 12 weeks, 100% cloud-native; and Hacktua built an app with 380+ pieces of content and a profiling algorithm, with 86,100 users and 5.0★. A universal method, applied to product companies.