Own your model. Own your data. Own your costs.
Private AI, running in your own cloud or data center.
50B+ tokens a month, served privately across our clients, at over 90% lower cost than frontier APIs.
See the work
Own your model. Own your data. Own your costs.
50B+ tokens a month, served privately across our clients, at over 90% lower cost than frontier APIs.
See the work02What we do
We recommend the right open-weight model, fine-tune it on your data, and serve it on autoscaling instances in your cloud account or on your own hardware, on-premise. You keep the fine-tuned weights, and no vendor can deprecate or change the model under you.
We build the pipelines that feed it: connectors into your source systems, sensitive fields redacted before anything reaches a model, access controls and audit logs on every call.
We size compute to your real workload and replace per-token bills that grow with usage with infrastructure you can forecast. You get a written cost model before you commit.
03Outcomes
04What we offer
A scoped, paid look at what you send to hosted AI today and what it costs you. You get a plan: what can move in-house, what it would cost to run, and what should stay where it is. The fee comes off the deployment if you go ahead.
We recommend an open-weight model, fine-tune it on your data and serve it in your cloud account on autoscaling instances, or on your own hardware. You keep the weights and hold every credential.
Agents that do real work inside the systems you already run: quoting and configuration, sourcing and purchase orders, handling inbound requests. We connect them to ERPs including Business Central, Dynamics 365 Finance and Operations and Infor SyteLine, and to CX platforms such as Salesforce, HubSpot, Zendesk and Insightly.
Connectors from your source systems, sensitive fields redacted before anything reaches a model, warehousing in platforms like Snowflake, knowledge bases kept in sync with your documents, and knowledge graphs and ontology pipelines that map how your products, parts and customers relate.
Usage policies for what goes to which model, access controls and audit logs on every call, evaluation sets that measure quality, and training so your teams actually use what's built.
After launch, it's your call. We hand over with runbooks and a trained team, or we keep running it on a retainer: monitoring, tuning quality and token usage, and moving to better open-weight models as they ship.
Technical leadership embedded with your engineering team, part-time. We set direction on AI strategy and architecture and work alongside your engineers on what gets built.
05How we work
We map what you send to hosted AI today, what it costs and what data it exposes. You get a written answer on which workloads should move in-house, and which shouldn't.
We recommend the model, the hosting (your cloud or your hardware) and the evaluation plan, with a cost model and a fixed scope and price.
We run the private model side by side with your current API on your real tasks. Nothing moves until it matches on what you care about.
Infrastructure as code into your account, with monitoring, runbooks and your team trained. You hold every credential.
We tune quality and token usage, then move the next workload.
06Let's talk
Tell us what you send to hosted AI today and roughly what you spend. We’ll tell you what can move in-house, what it would cost to run, and what should stay where it is.
The assessment is scoped and paid. The fee comes off the deployment if you proceed.