Early access — we're selecting our first design partners
Most of what companies actually use LLMs for — summarising, extracting, classifying, drafting, searching internal knowledge — runs perfectly well on a local model you own. Sending all of it to a frontier API means paying premium rates for routine work, and handing your data to someone else to do it.
Hybrid Intelligence deploys open models inside your network, fine-tunes them on your data, and puts a policy layer in front so only the genuinely hard 20% ever leaves — redacted, logged, and by your rules.
The thesis
The industry defaulted to sending every token to the largest available model. It's simple, and it's expensive — technically and commercially. The split we build around:
Open models on your own GPUs, tuned to your domain.
Marginal cost per query approaches zero once the hardware is in.
Frontier models, called deliberately rather than by default.
Sensitive fields redacted before the call. Every request logged.
The exact ratio is yours, not ours — we measure it from your real traffic before recommending any hardware. For some teams it's 95/5. For others local only clears 60% on day one and climbs as the fine-tune matures. The point is that the split should be a decision you make, not an accident of which SDK you installed first.
Architecture
Your applications talk to one OpenAI-compatible endpoint. Swapping a model, moving more work local as your fine-tune improves, or changing cloud provider is a config change on our side — not a rewrite on yours.
What we do
We size the hardware against your actual traffic, stand up open-weight models on it, and wire them into your applications behind a stable API. On your metal, in your data centre, or in a private tenancy you control — your call.
Your documents, tickets, transcripts and code become a model that speaks your domain. Training happens inside your boundary, the resulting weights belong to you, and nothing is used to improve anyone else's model — including ours.
Most companies already have the answer written down somewhere nobody can find. We index it and put a search layer over it that respects existing permissions and cites its sources, so answers can be checked rather than trusted blindly.
When a task genuinely needs a frontier model, the gateway sends it — after redaction, against your policy, inside your budget, with a record of what went. You get the capability without the default of everything leaving.
Who it's for
We're deliberately talking to four groups right now, because the local-first argument lands differently in each — and part of what we're trying to learn is where it lands hardest.
Finance · legal · healthcare · insurance
You have the use cases and the appetite, but client confidentiality, professional privilege or patient data means the obvious tools are off the table. Local inference removes the objection rather than working around it.
Cost control · vendor independence
Inference spend is growing faster than the value it returns, and it's concentrated in one vendor. Moving routine volume onto owned hardware turns a rising variable cost into a fixed one, and keeps a credible exit from any provider.
Agencies · consultancies · software teams
You're already using LLMs heavily and the bill is becoming a real line item. A modest local setup absorbs the repetitive volume, and you keep frontier access for the work that actually justifies it.
Sovereignty · air-gapped environments
Where the data cannot cross a border or a boundary at all, the cloud half simply gets switched off. The same stack runs fully disconnected, which is exactly the scenario a local-first design is built for.
About us
Most organisations adopted LLMs the same way: route everything to the largest available API and deal with the consequences later. That was never really a decision — it was the path of least resistance, and it carries two costs that only surface once you are committed. The bill scales with usage, permanently. And your operational data becomes someone else's input.
Open models have crossed the threshold where that trade-off stopped being necessary for the bulk of real work. A well-chosen open model, fine-tuned on your own material and running on hardware you control, handles the routine majority at a marginal cost approaching zero — without your documents ever leaving the building. The genuinely hard problems still deserve a frontier model. They are a far smaller share of the total than the prevailing architecture assumes.
We do not ask you to take that on faith. Every engagement opens by instrumenting your actual traffic and establishing your real split before any hardware is specified, rented or bought. Where the measurements say a workload belongs in the cloud, that is what we will tell you — there is no commission riding on the alternative.
Everything we build runs on open weights and standard interfaces, and is documented to the point where your own team can operate it without us. Being straightforward to leave is a design goal here, not an afterthought.
Register interest
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