Service
Models that run on your own infrastructure, not someone else's cloud.
Self-hosted and on-device AI. Language and vision models deployed on the business's own infrastructure, fine-tuned on its own data, with nothing leaving the building. This is the privacy and compliance layer that makes AI usable in regulated work, and it is the engine underneath Strygon's own products.
Focus
Self-hosted inference, fine-tuning, retrieval on private data, and de-identification.
- Service
- Local & private AI models
- Deliverables
- Named and scoped below
- Bought as
- A defined build, or run under management
- Ownership
- Every account and platform stays in the client’s name
Inference that never leaves the building
Open models deployed on-prem or on-device. Prompts, documents, and results stay on infrastructure the business controls, and there are no calls to a third-party cloud to audit.
Fine-tuned on your own data
Models adapted to the business's documents, records, and language, with retrieval over private knowledge. They are accurate in exactly the places where a generic API guesses.
Private by construction
A de-identification and policy layer sits in front of every model, so AI can touch regulated data. It is the same layer that stands between ClaimFlow and a HIPAA-eligible deployment.
Scope
What gets built.
Outcome
What changes.
AI in the workflow without shipping data to a third-party cloud.
Compliance becomes a property of the architecture instead of a promise in a policy document.
Predictable cost at scale instead of per-token metering.
Models that speak the business's own language, not the internet's average.
More
The rest of the system.
Start
Start with what's broken.
Send the situation in a paragraph. Strygon comes back with a read on what's likely wrong and what it would take to fix, before anyone talks about price.
Most builds start withleads dying in an inbox., three half-finished pipelines., follow-up nobody owns., numbers that never agree., four vendors blaming each other.
Or email hello@strygon.com
- What to send
- A paragraph. What broke, and where it shows up.
- What comes back
- A read on what is likely wrong and what fixing it takes.
- Price
- The last conversation, not the first
By industry
Where this lands, by vertical.
Local & private AI models answers a different constraint in each vertical. These are the reads worth having before scoping one.