04 — AI strategy for SaaS companies
From Software as a Service to Service as a Software.
AI does not make SaaS obsolete: it changes how software is built and what it promises. Software no longer merely equips the work — it does the work, and your customers validate the outcome. Differentiation will not come from a tool: it will come from your domain expertise and your proprietary data, encapsulated in AI-augmented products.
The approach
An end-to-end AI strategy, not an experiment.
From assessment to go-to-market, we drive the transformation with the same discipline as our value creation plans: dimensions assessed, use cases prioritised, execution governed — and human review before anything goes to production.
The target architecture: your expertise and data stay sovereign; external models are interchangeable behind an abstraction layer.
01 — AI maturity assessment
Know where you start from
Assessment of the organisation's AI maturity, dimension by dimension, then framing of the high-value use cases — crossing the leadership's vision with field workshops across product, sales and support.
02 — AI doctrine & sovereignty
Principles before tools
Data sovereignty, a strict demarcation line between AI-assisted code and customer data, GDPR compliance, intellectual property of generated code: a clear doctrine that protects your asset.
03 — Architecture
Sovereign and vendor-agnostic
Sovereign RAG on your proprietary data, a hybrid architecture separating sovereign and non-sovereign flows, an LLM-agnostic abstraction layer: your strategy depends on no single vendor.
04 — AI-augmented products
Your expertise, encapsulated
Guided workflows and answers sourced from your certified data — not generic self-serve AI. AI multiplies the value of your product and the efficiency of your customers.
05 — Code factory
R&D transformed
AI-assisted code generation, review processes, control of technical debt: the software construction chain is changing — we help your teams take command of it.
06 — Governance & go-to-market
From doctrine to revenue
Data and corpus governance, packaging and pricing of the AI offering, pilot customers, team organisation: an AI plan run like a transformation plan — all the way to revenue.
In the field
Real missions, conducted hands-on.
Our mandates are confidential; the missions speak for themselves.
Frequently asked questions
What we get asked about AI.
What is “Service as a Software”?
The reversal of the SaaS promise: software no longer merely equips the work — it does the work, and your customers validate the outcome. Value migrates from the tool to the result, which changes the product, the pricing and the organisation.
How do we protect our proprietary data in an AI project?
With a doctrine before tools: data sovereignty, a strict demarcation line between AI-assisted code and customer data, sovereign RAG over your corpora, and an LLM-agnostic abstraction layer so you depend on no single vendor.
Where should a software company start its AI strategy?
With a maturity assessment, dimension by dimension, then by framing the highest-value use cases — crossing the leadership's vision with field workshops across product, sales and support. Experimenting without a doctrine is expensive and produces no revenue.
Does AI make SaaS obsolete?
No: it leaves interchangeable tool-software behind and multiplies domain software. Companies that encapsulate their expertise and proprietary data in AI-augmented products come out stronger.