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

  • 30–45% relative gain in retrieval recall@k over demo-grade baselines—fixed chunking, dense-only search, no reranking
  • 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
  • Structured tool use and planning loops

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
  • SOC 2 evidence for enterprise AI vendor reviews, EU AI Act readiness for European market access, and ISO/IEC 42001 alignment for AI management system maturity
  • Audit-ready evidence produced by pipelines

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

  • Up to 40% reduction in AI and cloud run-rate against pre-optimization baselines—through routing, caching, and right-sizing, not usage caps
  • Cost attribution by agent, workflow, and tool call—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

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

Tell me what you're trying to get to production.

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