Services

Three Ways to Engage

Most technology consultants hand you a slide deck and disappear. I open a terminal. Every engagement leaves you with artifacts you keep: Architecture Decision Records, working systems, and a team that’s sharper than when I arrived, instead of a report that sits on a shelf.

I have nothing to sell you but judgment: no product, no preferred vendor, no implementation hours to upsell. I bring a decade of pattern-matching across hundreds of production environments to your hardest decisions.

Three primary service areas for teams adopting and running technology, each built to put senior technical judgment where you need it most. Building a technical product rather than buying one? There’s a dedicated offering for technology vendors and startups at the bottom of this page.

1. Fractional CTO & Engineering Leadership

For organizations that need executive-level technology leadership without the overhead of a full-time hire.

I work as an operator, not an advisor on the sidelines. I sit in on your architecture reviews, own the hard calls, and tell you the things you may not want to hear. I’m not here to bill hours indefinitely. I give you the right technology leadership for right now, for a company that doesn’t yet need (or can’t yet justify) a full-time executive.

What This Looks Like

  • Strategic Technology Roadmapping: Align engineering initiatives with business goals, force the hard priorities, and communicate tradeoffs in plain language
  • Architecture Governance: Establish Architecture Decision Records (ADRs), review critical design proposals, prevent costly mistakes before they’re in production
  • Build vs. Buy Decisions: Honest analysis of when to build custom, when to buy off-the-shelf, when to use open source, and when to walk away
  • Technical Risk Assessment: Identify failure modes, assess technical debt, plan mitigation strategies
  • Vendor Evaluation & Negotiation: Draw on 10+ years of vendor relationships and deal experience to get better terms and avoid lock-in
  • Engineering Team Coaching: Mentor senior engineers, establish practices that scale, build culture around ownership and operational excellence

Ideal For

  • Startups scaling from product-market fit to enterprise readiness
  • Mid-market companies needing strategic guidance between Director and full-time CTO
  • Organizations undergoing platform modernization or cloud migration
  • Companies evaluating major technology investments or pivots

Engagement Model

Typically 10-20 hours per month on retainer. Weekly strategic sessions, async communication via Slack/email, availability for critical decisions and escalations. My goal is to make myself unnecessary: to leave you with the architecture, the practices, and the internal leadership that no longer need me.

2. Platform Engineering & Observability

Design and implementation of modern cloud platforms built on Kubernetes, OpenTelemetry, and GitOps principles.

My approach is opinionated: paved golden paths with guardrails, not gates. Make the right way the easy way for developers, encode governance as defaults rather than approval queues, and treat the platform as a product its users want to adopt. On the observability side, your telemetry stays vendor-neutral and portable: instrument once with OpenTelemetry and never pay the re-instrumentation tax to switch backends.

Core Capabilities

Platform Architecture & Implementation

  • Kubernetes platform design (EKS, OpenShift, self-managed)
  • GitOps and self-service infrastructure (ArgoCD, Crossplane, Terraform)
  • Autoscaling and capacity management (Karpenter, HPA, VPA)
  • DevSecOps integration (OPA, Vault, Snyk, Wiz, Sysdig)
  • Service mesh and network policy (Cilium, Istio)

Observability Strategy & Implementation

  • OpenTelemetry architecture and instrumentation golden paths
  • Distributed tracing for microservices and monoliths
  • GenAI observability (TTFT, tokens/s, LLM performance metrics)
  • SLO/SLI design and error budget policy
  • Observability cost reduction and FinOps (OpenTelemetry is free; storage and indexing are not)
  • Tool rationalization: consolidating observability sprawl into a portable, OTEL-native stack
  • Vendor migration (e.g. off proprietary SaaS) with parallel running and metric parity before cutover, avoiding big-bang risk

Resilience & Reliability Engineering

  • Chaos engineering and resilience testing
  • Failure mode analysis and mitigation design
  • Incident response process design
  • Runbook automation and self-healing systems

Representative Projects

  • Fortune 50 Pharmaceutical — CrossPlane Implementation: Major self-service infrastructure platform enabling developers to provision resources across hybrid cloud
  • Global SaaS Platform — Resilience Assessment: Identified critical failure modes and architected mitigation strategies across the cloud platform
  • Fortune 500 Semiconductor & Financial Services — Observability Rationalization: Consolidating tooling, reducing costs, improving signal quality through OTEL-native architectures
  • Trace3 Cloud Center of Excellence Labs: EKS + Karpenter autoscaling, Harness.io GitOps, CrossPlane + Terraform, full DevSecOps stack, OpenTelemetry golden pathways

Engagement Model

Most engagements start with a fixed-scope Platform & Observability Audit (2-4 weeks): an honest read on your current stack, a costed remediation plan, and a prioritized 90-day roadmap, delivered as ADRs rather than a slide deck. From there, project-based builds (6-12 weeks typical) or an ongoing retainer for platform support and evolution. Every engagement includes architecture design, hands-on implementation, team enablement, and documentation.

3. AI Strategy & Enablement

Production AI is an observability problem. I make AI systems measurable, reliable, and cost-governed, then leave your team able to do the same.

What Makes This Different

The demos are easy. Production is hard. The discipline that separates a working AI product from a brittle one is the measurement loop: can you see what your models and agents are doing, evaluate whether they’re getting better or worse, and control what they cost?

That’s where I come from. I’m MIT-certified in Applied Generative AI and build production GenAI observability for a Fortune 10 company’s ML teams, and I’ve helped many organizations stand up token-economics dashboards for agentic coding tools like Claude Code and Claude Cowork on top of OpenTelemetry data. I come at AI from production observability rather than from a model lab.

The range is real: while I’m instrumenting agents for enterprises, I’m also at Fryeburg Academy, a prep school, helping faculty adopt AI with care. Whether you’re shipping LLM-powered features or deciding whether your team should use Claude at all, I meet you where you are.

Observability & Tokenomics for AI: where I’m strongest

  • AI Observability & Tracing (OpenTelemetry-native): Instrument LLM apps and agents with OpenTelemetry / OpenLLMetry so every prompt, agent decision, tool call, and retrieval becomes a traceable span, with quality, latency, and cost in one place and no vendor lock-in
  • Tokenomics & AI FinOps Dashboards: Per-team, per-workflow, per-user, per-model cost attribution on OTEL data, using the same dashboards I’ve built for Claude Code and agentic coding fleets. Turn “we know our total bill but not where it goes” into spend you can govern
  • Inference Cost & Performance Optimization: Model routing and cascades, prompt and semantic caching, self-host-vs-API tradeoffs, TTFT and throughput tuning, treating cost as a first-class engineering concern alongside latency and reliability

Evals, Agents & Retrieval

  • Eval-Driven Development: Golden datasets, code-based and LLM-as-Judge evaluations, and regression suites wired into CI and deploy gates, so no prompt or model change breaks what was working without you catching it. Evals are the hottest skill in AI for a reason: if you can’t measure it, you can’t improve it
  • Agent Reliability: Map how agents fail (drift, hallucination, runaway cost, broken planning and tool calls), instrument those failure modes, and turn each into an eval test case. Observability surfaces the failure, evals capture it, policy prevents the recurrence
  • RAG & Context Engineering: Diagnose retrieval quality (precision, recall, faithfulness, reranking) and add the evaluation framework most RAG systems are missing, framed as context engineering for agents rather than a one-off RAG build

For Non-Technical Organizations

  • AI Literacy & Education: Workshops and enablement for teams new to AI: capabilities, limitations, and practical use cases
  • AI Adoption Strategy: Which tools make sense for your workflows, what to pilot first, how to measure success
  • Governance & Policy: Establishing guardrails around AI usage, data privacy considerations, ethical guidelines
  • Use Case Discovery: Working with your teams to identify the highest-impact opportunities for AI augmentation

Current Engagement: Fryeburg Academy

Management consulting engagement educating faculty on practical AI adoption in education. Building frameworks for thoughtful tool selection, addressing concerns about academic integrity, and enabling teachers to enhance (not replace) their practice with AI.

This work sits outside traditional tech consulting, but it shows how broadly organizations need to understand AI.

Engagement Model

Technical Orgs: Ongoing retainer or project-based implementation. Includes architecture design, instrumentation, monitoring setup, and team training.

Non-Technical Orgs: Workshops, strategic planning sessions, pilot program design, and ongoing advisory. Typically 5-10 hours per month for 3-6 months.

For Technology Vendors & Startups

A different audience, the same root skill: I make technical value legible and trusted. If you sell to engineers, your hardest problem isn’t messaging, it’s credibility. Developers can tell in seconds whether the person talking to them has built something real.

I’ve spent a decade as the technical voice in the room on enterprise deals: Field CTO, SE leader, and partner-ecosystem builder. The commercial results (the technical credibility behind $30M+ in enterprise decisions) came from understanding the product at the architecture and code level, then translating that into something buyers and partners could trust. That’s the work I can do for your company.

What I Help Technology Vendors With

  • Technical GTM Strategy: Positioning and narrative for technical buyers, competitive landscaping, and the technical proof points that move a sophisticated audience
  • Sales Engineering & Pre-Sales Enablement: Building or sharpening your SE function, demo and POC processes, technical discovery, and the playbooks that turn engineers into trusted advisors in the deal
  • Developer Relations & Technical Marketing: Credible technical content, reference architectures, and developer-facing material that earns trust instead of pitching
  • Partner & Technical Alliances: Channel and technical-alliance motions, partner enablement, and the repeatable model behind them (the approach I used to co-build Groundcover’s partner ecosystem from scratch)
  • Technical Due Diligence: Honest assessment of a stack, an architecture, or an acquisition target, buy-side or sell-side

Why Me, Not a Marketer

Most technical-GTM help comes from marketers who learned to talk to developers. I’m the inverse: an engineer and Field CTO who learned to sell, market, and build partner ecosystems. I can write the reference architecture and the narrative around it, enable your SEs because I’ve led them, and speak to a skeptical staff engineer as a peer.

Engagement Model

Fractional advisory retainer or project-based (a positioning sprint, an SE-enablement build, a product launch). Priced at the same senior tier as my CTO work.

Let’s Discuss Your Needs

Not sure which service area fits? Most engagements blend several: a fractional CTO engagement often includes platform architecture decisions and AI strategy considerations.

Reach out and let’s talk about where you need the most help.