How we deploy
Cloud, on-premise, or hybrid. Custom or third-party. Adopted either way.
We meet your data and constraints where they are. The deployment model changes with your rules. Our responsibility does not: the system must be used, not shelved.
Where it runs
We deploy where your data and rules require.
Cloud
We deploy into your own cloud tenant on AWS, Azure, or Google Cloud. Fast to stand up, and your data never leaves your account.
On-premise
For regulated or air-gapped environments, we deploy inside your own data center. Nothing calls out that you have not approved.
Hybrid
Sensitive data and inference stay inside your perimeter while the rest runs in the cloud. Common where one workflow touches regulated records.
How it is built
Build the platform, improve the tools, or combine both.
A proprietary agentic platform, end to end
For the company that wants to own the system.
We build a custom agentic platform that runs a whole workflow inside your business, on your data, connected to your systems. You own the platform and the value it creates.
- Agents that plan, act across your systems, and hand off to people
- Custom connectors to the systems that hold your record
- Works across model families and sizes, from compact local models to large hosted systems
Adopt AI through the tools you already have
For the company that wants value without a build.
Sometimes the fastest value is configuring and adopting third-party AI tools your teams already pay for. We pick the right ones, wire them to your workflows, and own the adoption.
- The right tools chosen against your actual workflows, not hype
- Configured, connected, and measured against a baseline
- Change and adoption owned, so the licenses actually get used
The architecture
Models, systems, and the workflow underneath.
The model provides reasoning. Connectors bring the right data and actions. A precise understanding of the workflow turns both into something the business will use.
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Models of every size
The reasoning layer. We work across GLM, Qwen, Anthropic, Mistral, and other model families, from compact local models to large hosted systems. We choose what fits the task, cost, latency, privacy, and deployment environment, without locking you to one vendor or model size.
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Custom connectors
The bridge to your systems of record. We connect to your ERP, CRM, data warehouse, and line-of-business tools, read-only and scoped, so the AI works on real data.
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Deep workflow understanding
The part that actually creates value. We map how the work really happens, then deploy against it, which is why the output gets adopted instead of ignored.
Across models and sizes
We select for the task, cost, latency, privacy, and deployment environment, not for one preferred vendor.
Deployed in your environment
Your tenant and account, or on-premise where your rules require it.
In practice
Two shapes, one standard.
The agentic platform
A mid-market insurer wanted to own an AI system that runs claims triage end to end, not rent it.
A proprietary agentic platform in their cloud tenant: a model selected for the task and deployment constraints, custom connectors into their policy and claims systems, and agents that draft decisions with every fact cited to source.
Target: cut time-to-decision on routine claims by 40 to 60%, with a human owning every final call.
Adoption through existing tools
A services firm had already bought AI licenses across the company, but almost no one used them.
No new build. We mapped the three workflows where the tools could pay off, configured and connected them, and ran the change program that got the team using them daily.
Target: move from under 10% to a majority of the team using AI in the workflow, measured weekly.
Not sure which shape fits your workflow? The deployment review resolves that before any build begins.
The right architecture follows the workflow, not the other way around.
We identify the result worth pursuing, then recommend the deployment model your data, controls, and team can sustain.