Many organisations have launched AI experiments. Not all of them manage to industrialise them.
The difference is not just about which model you choose. It comes down to the organisation's ability to master its data, knowledge, risks, costs and real-world usage.
POC tolerates approximation. Production does not.
A POC can work with: a limited scope, manually prepared data, a handful of expert users, close supervision, and results interpreted with caution.
Production demands something else: reliable sources, controlled access, explainable results, clear responsibilities, audit capability and measurable business value.
This transition from POC to production is often where AI projects stall.
The quality of internal assets becomes critical
AI models are powerful, but they do not automatically fix an organisation's internal weaknesses.
If data is inconsistent, AI amplifies the inconsistency.
If documentation is outdated, AI reformulates the outdated content.
If business rules are implicit, AI works with incomplete context.
If sources are not traceable, responses become hard to verify.
Industrialising AI therefore starts before AI itself. It starts with the quality of internal assets.
What Adilian addresses
Adilian helps organisations move from experimentation logic to controlled exploitation logic.
KONTEX works on application knowledge: understanding systems, rules, dependencies, decisions and implicit know-how.
DUPLIK works on operational data: reconciling, consolidating, normalising and auditing critical repositories.
These two dimensions are complementary. Together they provide teams and AI systems with more reliable, better governed and easier-to-exploit assets.