Self-hosted LLMs and private AI systems
Run AI inside your own environment when public APIs are not an option
For many teams, the biggest blocker to using AI is not interest. It is data risk. We help companies build private AI systems that stay inside their environment, connect to internal data, and support real workflows without sending sensitive context into public tooling.
What this solution usually needs from day one:
Where this solution usually shows up
These are the situations where teams usually realize a generic tool is not going to get them much further.
Compliance or security requirements block the use of public AI APIs.
You need natural-language access to internal data without moving that data outside your environment.
Document-heavy teams want semantic search and Q&A on private files.
You have experimented with AI already and now need something more operational.
The business needs a controlled AI layer tied to internal systems and permissions.
How we take it from idea to production
The goal is to get a useful version live quickly, then improve it with real feedback instead of building in a vacuum.
Scope the real workflow
We start with the jobs the product has to do, the systems it touches, and the narrowest version worth shipping first.
Prototype and integrate
We shape the core experience early, connect the important systems, and make sure the product fits how your team actually works.
Launch and improve
We launch a useful version, watch how people use it, and keep refining the product around real usage instead of guesses.
What we typically build into this kind of product
These are the building blocks we usually end up designing around when the product has to work in the real world.
Private deployment
- Self-hosted model and inference setups
- Infrastructure aligned with your security constraints
- Deployment choices based on the real risk profile
Knowledge and data access
- Private document retrieval and Q&A
- Connections to databases and internal APIs
- Assistants grounded in internal source systems
Governance
- Role-based access controls
- Safer retrieval and answer patterns
- Visibility into what the assistant can and cannot access
Production readiness
- Pilots that can evolve into maintained systems
- Monitoring and tuning for real use
- Architecture that scales with adoption
Why not just force this into an off-the-shelf tool?
Most teams come to us after trying to stretch a generic product beyond what it was built to do. At that point, the workarounds cost more than the software is saving.
Frequently asked questions
A few of the questions teams usually ask before deciding whether a custom build is the right move.
Need AI without leaving your boundary?
We can help you scope a private AI system that fits your data constraints, your infrastructure, and the workflow you actually want to support.
Book a discovery call