IA privée
Déploiement d’IA privée et hors ligne
Une IA qui ne quitte jamais vos murs, et qui continue de fonctionner quand la connexion s’arrête.
- Secteur
- Services
- Ce que vous obtenez
- 7
Notre approche
Déploiement d’IA privée et hors ligne
Most AI advice assumes three things: that your data may be sent to someone else’s cloud, that there is a reliable internet connection, and that you are comfortable with both. For a great many organisations in Africa none of those hold, which is why so much AI ambition stalls before it starts.
We deploy ready-made models on hardware you own, inside your perimeter. The system can run with the network cable physically removed: no external calls, no telemetry, no outbound traffic to explain to an auditor. It is tuned on your documents, your terminology and your workflows, so it answers in the language your staff already use.
This is a productised deployment, not a research project. A known architecture installed in weeks, with the evaluation and monitoring that keep it honest. A smaller model chosen deliberately, because it is accurate enough for the task and runs on hardware you already have.
Ce que vous obtenez
Chaque mission produit des livrables qui vous appartiennent et que vous pouvez confier à n’importe quel ingénieur ou auditeur.
- Hardware sizing and specification for your workload
- Model selection, quantisation and installation on your premises
- Air-gapped deployment with no outbound network path
- Fine-tuning on your documents, terminology and workflows
- Retrieval over your own governed corpus
- Operator interface for the teams who will use it
- Evaluation harness, monitoring and update procedure
Résultats attendus
Résultats attendus
- AI in production without your data leaving the building
- A capability that works where connectivity cannot be relied on
- No per-seat or per-token cost that grows with adoption
- A defensible answer to data-sovereignty and audit questions
Outils et plateformes
Outils et plateformes
- Open-weight models (Llama, Mistral, Qwen)
- vLLM and llama.cpp
- Quantisation and ONNX Runtime
- GPU and CPU-only inference
- pgvector and Qdrant
- Docker on your own hardware
Notre façon de travailler
Notre méthode de livraison
Nous appliquons les meilleures pratiques et méthodologies de développement comme socle pour bâtir des solutions technologiques de pointe, de manière structurée et méthodique.
- 01
Planning
We collect the relevant information from you and your stakeholders, including the constraints you have not written down.
- 02
Defining
We document the software needs, agree acceptance criteria, and write down what is out of scope.
- 03
Designing
System and interface documents are prepared against the specification: architecture, data model and stack.
- 04
Building
We implement in short cycles, with a working increment and a visible demonstration at the end of each.
- 05
Testing
We evaluate quality to find and fix defects: automated where possible, exploratory where judgement matters more.
- 06
Deployment
Release runs through an automated pipeline, checked for deployment issues, with a rehearsed rollback.
- 07
Maintenance
We keep the system current, secure and monitored, so you hear about problems from us first.
Questions
Questions fréquentes
Is an offline model as good as a hosted one?
Not for everything, and we will say so. For extraction, classification, summarisation and retrieval over your own documents, a well-tuned smaller model is often indistinguishable in practice, and it never sends your data anywhere.
What hardware does it need?
It depends on the model and the volume. Some workloads run on a single workstation-class machine; larger ones need a GPU server. We size it before you buy anything.
Can it be genuinely air-gapped?
Yes. It can be installed and updated with no outbound network path at all, including offline model and security updates delivered physically.
How long does deployment take?
Typically a few weeks for a standard deployment, longer if the fine-tuning corpus needs work.
Services associés
Services associés
Parlons-en
Construisons votre prochain avantage.
Dites-nous ce que vous cherchez à accomplir. Vous parlerez à un ingénieur, pas à un centre d’appels, généralement en quelques minutes.
Du lundi au vendredi, de 08:00 à 18:00 heure d’Afrique de l’Est (GMT+3). Support urgent en dehors de ces horaires sur arrangement.