We take on projects where the cost of a wrong answer is real — regulated industries, customer-facing agents, long-running workflows. The categories below are what we actually build; every engagement combines a few of them.
Retrieval that holds up under real documents, real users, and real audits.
Secure enterprise knowledge assistants with role-based access, citation, hallucination detection, and evaluation dashboards.
Vector search, keyword search, and reranking — typically with context compression and dynamic chunking tuned to your corpus.
Contract analysis, regulatory review, research synthesis. Purpose-built for documents that don't fit the usual chunk-and-pray approach.
Agents that do useful work, with tool use, memory, and an honest escalation path.
Hierarchical agent teams — e.g. researcher + writer + reviewer — for report generation, code review, and long-running tasks.
Production voice interfaces with tool use, persistent memory, and clean escalation to humans when the model should not decide.
Codebase-aware agents built on Claude Code and similar — scoped to your repos, review flows, and deployment constraints.
What makes the difference between a pilot and a system leadership trusts.
Output validation, bias and fairness checks, hallucination mitigation, and audit logging — wired into the workflow, not bolted on.
Measuring accuracy, cost, latency, safety, and drift across models — so model upgrades become a decision, not a leap of faith.
Regulated-industry patterns, data redaction, fine-grained access controls, and documentation your auditors will actually accept.
Because most real problems don't arrive as neat text.
Combining text, images, and structured data — for example, financial document analysis that reasons over both prose and charts.
Traditional models (regression, classification, forecasting) alongside LLMs — better accuracy, lower cost, cleaner ownership.
Turning an expensive prototype into something the finance team signs off on.
Dynamically routing between fast models (Haiku) and powerful ones (Opus / Sonnet) based on task complexity, cost targets, and SLAs.
Prompt caching, prompt compression, and evaluation-driven prompt engineering — measured improvements, not vibes.
Meeting your business where it already lives.
Adding Claude-powered agents to Salesforce, SAP, ServiceNow, and internal tools — without forcing a platform migration.
Company-specific copilots for HR, legal, IT support, and engineering — scoped narrowly, trained on your actual workflows.
Two to three weeks. We embed with your team, read the docs, map the failure modes, and write down what "done" looks like before we touch a model.
Prototype in weeks, production in months. Eval harness from day one. No "it works on my laptop" handoffs.
We stay on through launch and at least one production quarter. Monitoring, drift checks, and a clean handover to your internal team.