Services

Enterprise AI platform leadership—from architecture to governance.

Three areas of practice: building the platform, making it production-ready, and enabling the organization around it. Seven services underneath — engaged individually or as a full platform mandate.

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 satisfies security, legal, and audit on a predictable timeline.

What You Get

  • Model and agent approval paths—proposal to production to retirement—that replace ad hoc escalations
  • Readiness for the regime that's blocking you: SOC 2 for enterprise deals, EU AI Act for European market access, ISO/IEC 42001 for AI vendor diligence
  • Audit-ready evidence produced by the pipeline, not reconstructed after the fact

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 — moving past autocomplete into agent-native workflows, rebuilding the SDLC around coding agents, and setting review gates that keep quality and security accountable as more code is machine-drafted.

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

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

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