What it is
The engineering that turns an AI model into a reliable, monitored and maintainable production system.
What's included
- Reliable deployment of models to production.
- Performance and degradation monitoring.
- Model and data versioning.
- Retraining and continuous improvement.
- AI security and cost control.
How we work: service methodology
- Phase 1 · Hardware Audit and Sizing (Week 1) — Assessing available compute capacity and selecting the right model.
- Phase 2 · Inference Engine Installation (Weeks 1-2) — Configuring the environment and model-serving software.
- Phase 3 · Private Interface Deployment (Weeks 2-3) — Configuring an internal-use interface with a local knowledge base, if applicable.
- Phase 4 · Performance and Security Testing (Week 3) — Load testing with concurrent users on the internal network.
- Phase 5 · Training and Going Live (Weeks 3-4) — An admin manual and training for the IT team.
What we need from your team
- Available dedicated hardware (a server or machine with enough capacity for the chosen model).
- Assignment of a fixed local IP and internal network rules.
- Remote or physical access to the server for installation.
- The company's document repository to be queried locally, if local RAG applies.
Who it's for
- Companies with models that must run 24/7.
- Teams that moved from pilot to production.
- Businesses scaling their AI usage.
Signs you need it
- AI models that worked well in the pilot start failing or losing accuracy once they're in production.
- Nobody monitors whether the model's performance is degrading over time until a user reports the problem.
- There's no clear versioning process, so it's hard to know which model or data produced a specific result.
- Retraining or updating a model depends on one person doing it manually, with no repeatable process.
Expected outcome
- Stable, available AI, not fragile experiments.
- Early detection of model degradation.
- AI costs under control and orderly scaling.
Why jsadsAI
We bring the engineering discipline missing between 'we have a model' and 'AI is a reliable part of our operation'.
Frequently asked questions about this service
What problem does deploying AI models on local infrastructure solve?
It avoids sending confidential data to external servers, reduces dependence on variable billing from API token usage, and removes the critical dependency on an internet connection.
What hardware is needed?
It depends on the model to deploy; it's defined during the diagnostic based on your budget and available compute capacity.
What interface do end users use?
A private, conversational-assistant-style web interface, accessible from the authorized internal network.
Does it integrate with my internal documents?
Yes, when the local-knowledge (RAG) component is activated, the system can query your internal document repository.
Technology stack
- MLOps
- Despliegue de modelos
- Monitoreo
- Versionado
- CI/CD para IA