AI engineering
We deploy integrated capabilities into the workflows and processes that matter, working with your team.
Most organizations approach AI by buying massive, monolithic software platforms or commissioning ground-up custom builds. Many vendors sell these heavy deployments because their business model depends on maximizing billable hours and owning your technology stack.
The result is often an expensive deployment that takes months to reach production and locks you into a proprietary architecture.
We follow a "best bang for the buck" escalation path. For many enterprise use cases, we start by configuring and orchestrating capabilities across commodity tools to validate a thesis. Often, custom configuration provides a robust medium-term solution at a fraction of the cost of a software build.
When your security, scale, or legacy integration requirements outgrow the ensemble, we execute the heavy-duty, ground-up custom architecture.
Four disciplines, sequenced to your workflow.
Harness engineering
A large language model is a generalist. A harness gives it memory, proprietary logic and reasoning, access to specialized information, and other features that make it a specialist in a specific task or domain. We build custom harnesses for a wide range of industry applications.
Commoditized ensembles
Many capabilities can be deployed at low cost by designing workflows that span multiple existing tools, usually with some custom configuration and harnessing. This approach allows organizations to rapidly validate a thesis, and often deliver outsized value ongoing.
Full-stack architecture
When the workflow requires it, we architect and deploy proprietary, secure, ground-up AI infrastructure. This includes self-hosted models, custom data pipelines, and deep integrations with legacy on-premise systems.
Gap-fill execution
We leverage your existing resources and partnerships to maximize value delivered, and bring the parts that are missing. For some projects, we're the strategic orchestrator. For others, we handle everything end-to-end. It's all about what's best for you.
From leverage point to owned architecture.
- 01Identify the leverage point.
We locate the specific workflow where AI integration produces immediate margin expansion or capacity lift.
- 02Validate via ensemble.
We stand up a lightweight proof of concept using commoditized tools to test the thesis before committing capital to a heavy build.
- 03Engineer the harness.
We build the context, routing, and guardrails required to make the system behave predictably in production.
- 04Escalate if required.
If the validated workflow requires deeper security, scale, or proprietary architecture, we execute the full-stack custom build.
- 05Transfer ownership.
We train your internal team to manage and maintain the system. You own the architecture.