An end-to-end consulting practice for agentic AI — from architecture and engineering through operations, compliance, and governance.
Design and build production-grade agentic systems — or bring a stalled prototype across the line.
A working demo is not a working product. Production systems require edge-case handling, graceful failure, cost predictability, and observability deep enough to diagnose issues after they happen. Our engineering practice is built around closing that gap.
Engagements typically cover architecture review, agent design patterns (ReAct, plan-and-execute, multi-agent), tool integration, memory and context management, and evaluation framework setup.
The operational discipline required to run AI systems reliably in production — observability, evaluation, cost governance, and CI/CD for model-in-the-loop pipelines.
Operating an LLM-based system without instrumentation is operating blind. Regressions surface only after they reach users, and cost overruns surface only after the invoice. Our LLMOps practice applies software engineering rigor to AI systems.
The objective is quantitative confidence: a measurable answer to whether each change to the system improves or degrades its behavior.
Agentic workloads for healthcare, financial services, and government — built with compliance, auditability, and data sovereignty as first-class requirements.
Regulated industries cannot retrofit compliance. The architectural decisions that determine audit outcomes — data residency, access control, audit trails, human-in-the-loop checkpoints — must be made at design time.
Our team has shipped sovereign cloud infrastructure for global healthcare platforms and delivered agentic systems inside U.S. state government. We engage where most firms cannot.
Corporate AI policy that gives teams the freedom to build — with guardrails that protect the business without blocking progress.
AI policies commonly fail in one of two directions: too restrictive, which drives shadow adoption, or too vague to be actionable. Our frameworks are specific enough to enforce and flexible enough to remain durable as the technology evolves.
Engagements include policy drafting, data classification frameworks, acceptable use guidelines, vendor approval processes, and incident response procedures — aligned to NIST AI RMF and emerging regulatory requirements.
Hands-on sessions for engineering teams and executive leadership — from foundations to production-ready practice, and strategy workshops that produce real roadmaps.
Every workshop is structured to produce a concrete artifact — a working prototype, a prioritized backlog, or a strategic roadmap the team has authored and can act on.
Half-day or full-day deep dive on LLM fundamentals, prompt engineering, agent patterns, and tool use. Teams leave with a working proof of concept.
90-minute to half-day session for leadership teams. Landscape orientation, competitive context, and a framework for identifying where AI creates durable advantage for your business.
Structured half-day session to surface, evaluate, and prioritize AI opportunities within a specific business domain. Output: a scored backlog of AI candidates with rough effort estimates.
Two-day intensive for engineering teams already building with LLMs. Covers evals, observability, cost management, and deploying to production on GCP.
A 30-minute discovery call is usually enough to identify where to start. No pitch — a focused conversation about your challenge.
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