Private document search
An internal assistant over documentation, PDFs, manuals, procedures and other files the company already has. Answers with sources, on a retrieval path you can point to.
Private AI · local, on-premise or hybrid
Use AI with Company Data — Privately
Practical AI workflows on infrastructure you control — internal documents, transcription, knowledge search and text generation. Designed for work where data privacy matters.
Or start with a fixed-scope Private AI Readiness Audit — from €1,500 →
- Keep sensitive data inside infrastructure you control
- Reduce dependence on external AI APIs
- Local or self-hosted inference where it fits
- Local, private-cloud or hybrid deployment depending on the workflow
company-data/ ├── audio local speech-to-text ├── documents embeddings / search ├── tickets extract / draft └── public-apis opt-in, not default workflow first, model second
What this is actually for
Not “install an LLM”. A path from data you already have to an output people will use.
Private transcription
Speech-to-text for meetings, interviews, calls and internal recordings. Audio does not have to go to a public speech API for a transcript, a summary or a searchable archive to exist.
Internal text generation
Drafts, summaries and structured text on infrastructure you control. People stay in the loop; the model proposes, it does not publish.
Custom AI workflows
LLMs wired to internal APIs, databases and existing tools — extraction, routing, or a step in a process you already run. The workflow is the product. The model is a component.
Start with the workflow, not with the model
First: which job are you trying to improve, what data it needs, and why that data should stay private. Then the stack.
A local model is not automatically better than a public API. It is better when the workflow, the data path and the operations justify it.
After the use case is named
- The model
- Infrastructure
- Inference setup
- RAG, if the workflow needs documents
- Speech-to-text, if the workflow is audio
- Storage and retention
- Integrations with systems you already have
Private deployment options
Local workstation, on-premise server, private cloud, or hybrid. Public APIs remain the right default for work that is not sensitive and does not justify operations.
This is not a claim of regulatory certification. It is an architecture choice about where prompts and documents go.
Typical shapes
- 01Documents
- 02Parser
- 03Embeddings
- 04Retrieval
- 05LLM
- 06Answer + sources
- 01Audio / video
- 02Speech-to-text
- 03Transcript
- 04Summary / search
- 01Internal context
- 02Private model
- 03Draft
- 04Human review
Have a workflow that should not go into a public chatbot?
Name the input, the output, and who uses it. The call is about whether private, hybrid or a public API is the right fit.
Local, private cloud or hybrid
No row is universally cheaper or safer. The workflow decides.
| Architecture | Best for | Trade-off |
|---|---|---|
| Local workstation | Individual workflows | Simple; limited concurrency |
| On-premise server | Team usage inside one site | Hardware and an operator |
| Private cloud | Distributed teams | Rented GPUs; still not a public chatbot |
| Hybrid | Mixed sensitivity | Sensitive work stays private; the rest can use APIs |
| Public API | Non-sensitive or quality-critical work | Data is processed outside your infrastructure |
How the work usually starts
An assessment of the workflow, then one pilot on real files, then production if the quality and operations hold.
Assessment
Private AI Readiness Audit
From €1,500. Written recommendation, including stay on a public API. No commitment to a pilot afterwards.
Pilot
One real workflow
From €300/day. Transcription, document search or drafting — on files your team actually uses.
Production
If the pilot earned it
From €300/day, or a fixed price quoted from the pilot. Not a platform programme sold up front.
Production backend experience
Backend systems first
Private AI here is a workflow on infrastructure you already understand: APIs, storage, queues, access control. Not a chatbot bolted onto a laptop demo.
Production operations
Deploy, monitor, backup, and keep prompt bodies out of default logs. A private model with public logs is not a private system.
Architecture based on the workload
Local, on-premise, private cloud or hybrid — including cases where a public API remains the most practical option.
Pavel Beliaev
Senior Backend Engineer
I am a senior backend engineer. Private AI in this practice is a workflow problem: APIs, storage, queues, deployment, access control and observability on infrastructure you control.
Questions teams usually ask
Does the model have to run completely on-premise?
No. On-premise is one option. Private cloud, a dedicated workstation, or a hybrid with a public API for non-sensitive work are all valid. The constraint is which data is allowed to leave which perimeter — not a requirement to own a GPU rack.
Can this work with existing internal documents?
Yes. Document search is usually retrieval over files you already have: PDFs, manuals, wikis, exports. Quality depends on parsing, chunking, access control and evaluation on real questions. It is not a magic search engine.
Can you build private transcription?
Yes. Local speech-to-text for meetings, interviews and recordings is one of the more reliable private workflows. A transcript can then be summarised or searched. Speaker separation is a separate step and is not always accurate.
Do we need GPUs?
Not always. A small quantized model on a workstation is enough to test a workflow. A GPU becomes necessary when the model, the context length, the concurrency or the transcription volume will not stay responsive on CPU. Hardware follows the workflow, not the other way around.
Can private AI integrate with our existing systems?
That is usually the useful version of the work: an internal API, a database, a ticket queue, or a folder of documents. The model should not get a shell or production credentials. The application calls your systems after the output is validated.
If the AI question is already settled and the gap is senior backend capacity, that is a separate B2B contractor engagement.
Next step
Have a workflow that should use AI without exposing company data unnecessarily?
Tell me what data you have, what people do manually today, and what the output should look like. The useful answer might still be a public API.