Every engagement starts with one of these patterns. Most blend two or three. All are delivered by an embedded expert squad inside your data, repos, and governance perimeter — against measurable outcomes, not slideware.
Experienced AI, platform, and product operators sit inside your team and ship governed agents into production sprint by sprint. The squad joins the repo, owns delivery, and earns the next phase. Outcome we track: pilot-to-production cycle time.
Permissioned retrieval · citations · retrieval evals. Trustworthy retrieval over your private corpus, with row-level permissions, hallucination evals, document lineage, and human-grade citations. Outcome we track: hallucination and citation pass rate.
Multi-step AI workflows that read from your systems, take approved actions, and route risk back to humans. Specialist agents call your tools and APIs under policy guardrails, with approval gates where it matters. Outcome we track: reviewer throughput and human escalation rate.
Gateway · evals · observability · cost & policy controls. The plumbing every team ships on: model gateway, prompt registry, evals harness, traces, cost guards, policy engine. Built once, used by all. Outcome we track: cost per completed workflow.
Reusable · audited · permissioned. Internal catalogues of vetted agents and tools your business units can compose — with audit trails, approval flows, and rollback. Outcome we track: audit completion time and reuse rate.
Replatform · rewire · retire. Legacy systems retired or wrapped with governed agents that bridge old data and new workflows — without ripping out what works. Outcome we track: pilot-to-production cycle time on the migration slice.
Most teams start with a 2-week diagnostic — we map your highest-leverage agent opportunity, the data and governance perimeter, and scope a 6-week pilot.