Services

Enterprise AI platform leadership—from architecture to governance.

Four areas of practice: choosing the work and building its data foundation, building the platform, making it production-ready, and enabling the organization around it. Ten services underneath — engaged individually or as a full platform mandate.

Strategy & Foundations

Choosing which AI work is worth doing, and building the data underneath it before the platform depends on it.

AI Portfolio Strategy & Use-Case Prioritization

Deciding what to build before building it: a ranked use-case portfolio sequenced against data readiness, delivery capacity, and business case—and an explicit kill list for everything that won't clear one.

What You Get

  • A ranked use-case portfolio with business cases, sequenced against data readiness and delivery capacity
  • An explicit kill list—initiatives retired before they consume a quarter
  • Business processes redesigned around AI-enabled workflows, so value lands in operating metrics rather than pilot demos

Data Foundations & Architecture

The data layer AI adoption depends on: architecture, ownership, and lineage across enterprise sources—modernized incrementally, without interrupting the business running on it.

What You Get

  • Data architecture, ownership, and lineage defined across enterprise sources
  • Legacy pipelines modernized to cloud in stages, with the business running throughout
  • Retrieval-ready data—quality, freshness, and access controls that RAG and agent systems can depend on

Architecture & Build

Designing the platform, retrieval, and agent systems that production AI actually runs on.

LLMOps Platform Architecture

The platform layer under your production AI: deployment, evaluation, prompt and dataset governance, and lifecycle management designed as one system your teams ship on.

What You Get

  • Model and prompt releases promoted through CI/CD with staged rollout
  • Prompt and dataset governance with versioning and rollback
  • Automated evaluation gates—LLM-as-judge plus human review—in the release path

RAG System Design & Evaluation

Retrieval as context engineering—governing what enters the model's context at each step of an agent loop. Hybrid search, evaluation harnesses, and accuracy tied to business metrics.

What You Get

  • Retrieval quality measured against a versioned test set before launch, not judged from a demo
  • Hybrid search with BM25 + vector + reranking
  • Context assembly, ranking, and token budgeting within agent loops

AI Agents & Orchestration

Agent systems that coordinate tools and workflows inside explicit safety, permission, and audit boundaries, with the observability to debug them in production.

What You Get

  • Multi-agent orchestration with LangGraph, with explicit state and retry semantics
  • Permission scoping, safety boundaries, and audit-ready logging
  • Model Context Protocol (MCP) integration

Production Readiness

Keeping those systems reliable, governed, and affordable once real users depend on them.

Continuous Evaluation & Agent Observability

Evaluation infrastructure that catches silent regressions before your users do: trajectory grading, prompt and tool-call regression tests, and LLM-as-judge pipelines with human review.

What You Get

  • Agent trajectory grading and prompt/tool-call regression testing
  • Drift detection and production AI observability (Braintrust, Langfuse, Inspect)
  • Shortened release cycles for prompt and model updates

AI Governance & Compliance

Governance that accelerates deployment rather than gating it: a defined path from proposal to production that gives security, legal, and audit the evidence they need on a predictable timeline.

What You Get

  • Model and agent approval paths from proposal to production to retirement, replacing ad hoc escalations
  • Agent identity, delegated authorization, and permission scoping
  • SOC 2 evidence for enterprise AI vendor reviews and EU AI Act readiness for European market access

Cloud Infrastructure & Cost Optimization

Making AI spend attributable to the agents, workflows, and tool calls driving it—then reducing it through routing, caching, and right-sizing rather than usage caps.

What You Get

  • Every AI dollar traced to the agent, workflow, and tool call that spent it—so spend maps to value, not just to a monthly bill
  • Intelligent routing, caching, and fit-for-purpose model selection across a multi-model stack
  • Token budgets and cost guardrails for agent loops and reasoning-model inference

Organizational Enablement

Most engineering teams already have the tools; the gap is in how they're used. Rebuilding the SDLC around coding agents, and leaving the capability behind rather than the deliverable alone.

AI-Assisted Engineering Enablement

Turning installed tools into delivery gains: the practitioner skill, working standards, and review discipline that keep AI-assisted delivery improving after the engagement ends.

What You Get

  • Practitioner training and working standards for coding agents (Claude Code, Codex)
  • Agentic SDLC workflows: spec-driven development with human review gates
  • Team enablement, metrics, and guardrails for sustained delivery velocity

Capability Transfer & Interim Leadership

Engagements scoped to end: the AI or data leadership seat filled now, and the platform, the evaluation discipline, and the team to run them handed to permanent internal ownership.

What You Get

  • Interim AI or data leadership—the VP seat filled while the search runs
  • A standing AI or data function transitioned to permanent internal ownership
  • Handoff artifacts that outlast the engagement: runbooks, evaluation suites, and governance records

Not sure which of these you need? That is what the diagnostic answers.

See the Diagnostic

Or work through the full platform control checklist on your own.