Capabilities · AI
We develop AI as a product and integrate it as a feature: agents, assistants, and LLM-powered workflows grounded in your data, measured by evals, and engineered with the same rigor as everything else we ship.
You’re building something AI-native. We take it from concept to launch: model selection, agent architecture, conversational UX, and the unglamorous engineering that turns a demo into a business.
Your product works; you want it smarter. We integrate LLM-powered capability into the software you already run, from search and summarization to support and automation, without destabilizing what already earns.
“AI-powered” is not a feature. Before we build, every AI surface has to name the number it moves: revenue, resolution rate, cycle time, retention. If it can’t, we’ll say so and build the thing that can.
The gap between an impressive demo and a dependable product is engineering, not magic. This is how we close it. We’ll also tell you plainly when AI is the wrong tool for the job. It’s the same discipline we hold ourselves to: see how we use AI.
Every AI surface ships with an eval suite and quality gates run against real tasks. If we can’t measure it, we don’t ship it.
Bounded tools, graceful degradation, and human-in-the-loop where the stakes demand it. Failure modes are designed, not discovered.
Token spend and response time are engineering budgets, tracked like performance. No surprise invoices, no spinner fatigue.
The same delivery lifecycle we’ve sharpened over a decade of shipping, specialized for AI: evaluation and hardening are first-class phases, not afterthoughts.
Use-case mapping · feasibility · risk/ROI · a straight answer if AI isn’t the tool
Source audit · grounding & retrieval design · privacy review
Model selection · prompt & context engineering · thin-slice build
Golden sets · eval suites · quality gates against real tasks
Guardrails · fallbacks · cost & latency budgets · red-teaming
Integration · incremental rollout · observability & tracing
Monitoring · drift & regression watch · model upgrades
User feedback · eval expansion · capability roadmap
Team enablement · playbooks · handover or ongoing ownership
Built for regulated environments
We’ve shipped HIPAA-compliant infrastructure and privacy-first data platforms; the same posture governs every model we put in production. AI raises the stakes, so the guarantees are explicit.
No training on your data. Vendor data-use terms are vetted before a model sees a token; retrieval corpora live inside your boundary.
Minimization and redaction before the context window, retention rules after it. HIPAA-, GDPR-, and CCPA-grade handling by default.
Agent actions are traced and logged; consequential calls keep a human in the loop. When someone asks “why did it do that,” there’s an answer.
Compliance designed in from day one, not retrofitted: the same posture that has carried our HIPAA, SOC 2, and PCI work.
The full alphabet soup, with evidence: delivery in regulated environments →
Tell us what you’re trying to build, or what stalled. We reply within one business day.