Process automation
We turn your processes into reliable flows, integrated with your existing tools, then industrialize them in code you own. Deliverable: automated processes, documented and supervised in production.
Development
From audit to production, in code you own.
Three strands, one delivery regime: everything is written in code, versioned, tested and handed over. What is built here can be reread, taken over and taken away.
Three strands
Automations, agents, applications: a single way of delivering.
A mission produces one or more of these three objects. They often combine: an automation calls an agent, an agent ends up in an application.
AI agents & RAG
Assistants connected to your internal data, which cite their sources and whose answers are measured by an evaluation set replayed at every model change, with human validations at the sensitive points. Deliverable: agent in production, of which you are sole owner.
Custom AI applications
Internal tools whose code belongs to you, designed to last in operation. Deliverable: application in production, source code handed over.
Method
Nothing is developed before the GO.
Development is step 03 of the PROVE method. The two steps before it end with a GO / NO-GO decision, and that is the only moment a build budget is committed.
Orchestrate, once the scope is written
Prioritizing and settling have produced the audit report, a costed roadmap, a specification for each selected mission and a briefing note: development starts from documents, not from an intention. Human validations are placed where the audit decided, not where the system turns out to be fragile after the fact.
Two steps follow the release. Verify covers performance, security, hallucination control and actual adoption. Prove documents the evidence and hands over the sustainability plan.
Read the PROVE methodHandover
What you hold in your hands at the end.
Ailix Consulting delivers eight items, identical on every mission. A demonstration that went down well in a meeting produces none of them.
The source code, documented and versioned
Handed over with the change history and the deployment instructions. Your teams, or a third party, take over maintenance the day you decide.
The architecture documentation
Flow diagram, dependencies, technical choices and the reason for each choice. A CIO discovering the project can pick it up by reading, without us.
The automated tests
They run at every change to the code. An evolution that breaks an existing behavior is flagged before going live.
The AI evaluation sets
A set of questions and expected answers, replayed at every change of model or configuration, which measures the quality of what the system answers.
The execution logs
Every answer is traced: question asked, documents consulted, model called, action triggered. An internal control or a GDPR request is handled on records.
Supervision and alerts
Errors, abnormal response times and cost overruns are reported automatically, before users notice them.
The recovery plan
What to do if a model provider goes down, if an access key expires, if a flow stops. A written procedure, executed at least once during the mission.
The 5-year sustainability plan
What will have to evolve, on what horizon and for what order of magnitude of budget, over the 5 years following the production release.
The same inventory applies to a simple automation as to a complete business application. It appears in the briefing note, before the first day of development.
Models
No commercial commitment to any model provider.
The question comes up at every audit: which model do you use, and what happens when it changes?
No commitment to a provider
Ailix Consulting has no commercial commitment to any model provider. The model is a component, chosen for the task and replaceable: when a better one comes out, it is swapped in without rewriting the system. RAG, agents, MCP and embeddings are written in code, in a repository you own.
Before developing
What is built here was first priced elsewhere: the audit establishes where AI creates value for you, at what cost and with what risks, and ends with a GO / NO-GO decision.
See the AI auditTell us about the system to build.
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