Transcription & AI reporting
A local transcription and summarization workflow, built around confidentiality and around reports that teams can put to use.
Read the case study : Transcription & AI reporting02
On-premise LLMs
On-premise LLMs: a model integrated into your own environment.
Design a local AI pipeline, evaluate it on your use cases and integrate it with your tools. For teams with a hosting constraint or a need to work offline.
This work fits your situation if…
Keeping processing in a controlled environment
You have established that some data must stay on your infrastructure. The project has to examine the entire path, from document import to the response produced.
Searching internal documentation
Your teams look for answers in a defined body of documents. Document retrieval paired with the model can be considered, with sources users can consult and appropriate access rights.
Bringing AI into a business tool
The model’s output has to feed an application, a report or an existing step in the workflow. The interface, error handling and review matter as much as the choice of model.
What the engagement can deliver.
The scope and deliverables are agreed together during scoping.
- Verified sizing
- An inventory of hardware and use cases, a shortlist of candidate models and measurements on an agreed set of examples. Quality, response time and load are evaluated before a configuration is chosen.
- An integrated inference pipeline
- Generation service, interface or connection to your tools, access management and failure handling. Which components run locally is stated explicitly.
- Document access, where needed
- Ingestion, search and source display for a defined corpus. The evaluation also covers documents that are missing, outdated or not accessible to the user.
- An operations handbook
- Installation, configuration, dependencies, checks, updates and recovery. Maintenance responsibilities and handover arrangements are set in the engagement scope.
A clear sequence.
Establish the constraints
List the infrastructure, concurrent users, data, authorized flows and expected tasks. Choose representative examples and failure cases to test.
Evaluate a complete pipeline
Measure results on your tasks with the intended configuration. Check sources, access and how the system behaves when it cannot answer.
Integrate and hand over
Connect the service to the user journey, run acceptance testing, then document operations. Go-live follows the criteria agreed with your team.
Case studies that show the work.
Le Salon — meeting room concierge
A case study that describes local transcription and several document search options. Offline operation is a deployment option there, not an assumed property of the public demo.
Read the case study : Le Salon — meeting room conciergeBefore we start.
Do we need to buy a server with a GPU?
Hardware is chosen after looking at the model, the volumes and the response time you expect. An inventory of your equipment and a representative trial show what is usable and what is missing, before any purchase.
What does RAG add compared with the model alone?
RAG (retrieval-augmented generation) means retrieving passages from a corpus and passing them to the model so it can produce a contextualized answer. Both the retrieval and the answer need to be evaluated: citing a source does not remove the need to check that it actually supports what is written.
Who handles updates after delivery?
This is settled before go-live. Handover can be organized with your team; ongoing support can also be scoped. Components, procedures and responsibilities must be identified so that the system can be taken over.
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Next step
Let’s start from your need.
Tell me your use case, the infrastructure available and the level of network connectivity allowed. These details let us define a relevant local trial.