Sovereign RAG
Your documents, procedures, contracts and technical bases indexed in a private vector store. Every answer cites its source, down to the passage.
AI & Agents
AI becomes profitable the day it stops being a demo.
Sovereign RAG, tooled agents, controlled plugins, multi-model gateway: we build the full chain, with an operating cost known in advance.
01 — The real problem
Why 8 out of 10 AI projects never reach production
A language model does not know your company. Without your data it rephrases generalities and invents the rest. That is why so many projects look brilliant in a demo and collapse on contact with reality.
What makes the difference is invisible in a demo: retrieval quality, the precision of the tools given to the agent, continuous answer evaluation, traceability and cost control.
0data used for training without written agreement
100 %of model calls traced, timestamped and costed
−54 %monthly AI cost after a gateway is introduced
02 — The architecture
Four building blocks, one system
Each block solves one precise problem. Together they turn a generic model into a reliable production tool.
Autonomous agents
Agents that watch, trigger, execute and report. Explicit action frame, complete journal, human validation on sensitive actions.
Plugins & tools
A toolbox per agent: file reading, business queries, API calls, report generation, operational actions — each tool is bounded.
Model gateway
A single entry point that picks the right model per request, caches answers, caps budgets and measures latency as well as cost.
Your documents, procedures and contracts as one volume the agent walks through before answering — never an invented answer.
The full chain we deploy: sourced retrieval, tool-equipped agents, model gateway and permanent guardrails.
The real chain: sources, proxy, RAG, agents, plugins, API. Every edge is a contract, every node is monitored.
03 — Use cases that pay off
Where AI pays for itself
Knowledge
Internal document assistant
Your teams find the right procedure, the right contract, the right reference in seconds — with the exact source.
- Sourced answers
- Private documents
- EU hosting
Operations
Monitoring agent
Anomaly detection, qualification, ticket creation, runbook execution and automatic reporting.
- Qualified alert
- Runbook executed
- Action journal
Back office
Inbound mail handling
Classification, extraction of useful data, draft response, human control on sensitive cases.
- Structured extraction
- Automatic triage
- Human validation
Management
Reporting and synthesis
Your consolidated indicators, commented and sent to the right person every week, with no manual work.
- Automatic report
- Variance analysis
- Scheduled delivery
Quality
Control and compliance
Document consistency checks, detection of regulatory anomalies, audit preparation.
- Audit trail
- Gap detection
- Full traceability
Operations
Assisted maintenance
Incident history used to suggest the probable cause and the corrective action before the intervention.
- Incident base
- Probable cause
- Suggested action
04 — Under the hood
The same console that monitors your systems
Our agents and our monitoring share one console. You see what the AI does, when it does it, what it costs and what it produced.
Every action is timestamped, attributed and reversible. No agent acts outside the scope you validated.
- Timestamped execution journal, action by action
- Cost per request, per user and per use case
- Business test sets replayed on every change
- Budget caps and automatic shutdown
Zorglon monitoring
LIVE
24Monitored systems
99,95 %Availability
0Open incidents
41 €AI cost / 24 h
- api-core12 ms
- postgres-main4 ms
- worker-sync1,4 s
- gateway-ia320 ms
- backup-nightlyOK 03:12
- certificatsJ-21
- pare-feu-edge0 incident
- portail-client98 ms
Last reading 4 s ago
Runbook ready
05 — Before / After
An assistant that impresses, or an assistant that works
| Before | With Zorglon |
|---|---|
| A chatbot inventing a plausible answer | A sourced answer linking to the exact document |
| Uncontrolled model calls | A single gateway: routed, cached, capped |
| No idea of the real cost per usage | A known unit cost, measured and budgeted |
| Agents acting outside their scope | An explicit action frame and a complete journal |
| A pilot that never survives go-live | An evaluated, monitored, continuously improved system |
06 — AI objections
What we are actually asked
Our data is sensitive. Can we really use a language model?
Yes, provided you never send all your data to a model. We index your documents locally or in the European Union, filter what leaves, anonymise what must be anonymised and trace every request. No provider trains on your data without written agreement — that is a clause in our contracts.
What if model prices change, or a provider disappears?
That is exactly why we introduce a multi-model gateway: your application never depends on a single vendor. Switching models becomes an operational decision, not a rewrite.
How long before a first result?
A first production-usable document assistant takes thirty days on a chosen scope. Additional use cases are added in batches, building on existing infrastructure.
Do we need an internal team to operate all this?
No. Our most satisfied clients have no in-house AI profile. We do train your business referents: they judge answer quality, and we measure those evaluations on every change.
Pick one use case. Cost it. Ship it.
Thirty days later you will no longer debate AI in meetings: you will look at numbers.