Services
We don't do everything. We do three things well: finding where AI creates value, building those systems, and teaching your team to maintain them.
Strategy.
The hardest part of AI adoption is deciding what to build. We audit your operations to find the intersection of technical feasibility and business value. You get a defensible roadmap, not a slide deck of generic possibilities.
Example engagement
The Client: A national logistics provider struggling with manual routing exceptions.
The Ask: "Build us an AI chatbot to replace our dispatchers."
The Result: We advised against the chatbot (too brittle) and instead mapped a strategy for an LLM-assisted triage inbox that kept human dispatchers in the loop but reduced processing time by 70%.
Implementation.
We build production AI systems. That means proper evaluations, error handling, telemetry, and fallback mechanisms. We favor small, deterministic components over monolithic agents.
Example engagement
The Client: A B2B SaaS platform in the legal space.
The Ask: A reliable way to extract structured entity data from unstructured contracts.
The Result: A production pipeline utilizing small, task-specific language models with strict JSON-schema enforcement, achieving 99.2% accuracy across 40 document variants.
Enablement.
We don't want to be your bottleneck. We train your engineers to build and maintain AI features responsibly. We conduct architecture reviews, host workshops, and provide ongoing advisory.
Example engagement
The Client: A mid-market healthcare software company.
The Ask: Their product team wanted to use AI, but engineering didn't know where to start safely.
The Result: A 4-week embedded program where we built a low-risk internal tool alongside their senior engineers, establishing patterns for prompt management and eval frameworks they now use company-wide.