What is a fractional CTO?

A fractional CTO is a seasoned technology executive who serves on a part-time or project basis - delivering the same strategic engineering leadership a full-time CTO would, but scoped to the time and budget a growth-stage company can support. I lead engineering org design, architecture and AI roadmap, board-ready reporting, and hiring - without the salary, equity, and onboarding cost of a permanent C-suite hire.

When does a growth-stage company need a fractional CTO instead of a full-time hire?

A fractional CTO fits when the company needs senior technology judgment now but is not yet at the scale or runway to justify a full-time CTO comp package. Common triggers: scaling from 10 to 60 engineers, a recent leadership departure, a critical AI or modernization initiative, M&A or fundraising prep, or a board demanding stronger engineering accountability. If the engineering org is already past 100 people with a clear scaling thesis, a full-time hire is usually the right answer.

How long does a typical fractional CTO engagement last?

Engagements typically run three to twelve months, sized to the outcome. A focused initiative - AI adoption, platform modernization, or an M&A integration - might wrap in three to six months. An interim CTO role bridging to a permanent hire often runs six to nine months. A long-term advisory cadence (board reporting, architecture review, hiring) can extend a year or more. The scope and end-state are defined up front, not open-ended.

Can a fractional CTO bridge to a full-time CTO or VP of Engineering hire?

Yes - this is one of the most common engagement patterns. I provide leadership continuity while the search runs, define the role spec based on what the company actually needs (not a generic template), help interview and select the permanent hire, and run a clean knowledge transfer. The goal is to leave the org stronger and the new CTO set up to succeed - not to extend the engagement indefinitely.

What industries do you serve as a fractional CTO?

My strongest fit is growth-stage SaaS under 250 people with engineering teams of 10 to 60. Industry depth: HealthTech and MedTech (HIPAA, HL7/FHIR, Epic EMR), EdTech SaaS, regulated B2B SaaS (SOC 2, ISO 27001), FinTech (PCI), and PE-backed portfolio companies needing engineering accountability and ROI tied to the deal thesis.

How long does an AI-native SDLC rollout take?

A baseline in three weeks and a first measured delta at 90 days. Assisted practice - plan-first review, risk-tiered routing, AI pre-review, generated tests, a merge queue - lands in months 1 to 3. Unattended agents on the safe work class only run in months 2 to 5, gated on change failure rate holding flat. Evidence packs on every release and domain-specific pipelines compound by month 9. Durations flex with team size and regulatory weight; the sequence does not.

Service

Fractional CTO Services

Executive engineering leadership, scoped to your stage. Growth-stage companies need senior technology judgment - on strategy, AI adoption, architecture, hiring, and board reporting - long before they can support a full-time C-suite comp package. A fractional CTO closes that gap without the salary, equity, and onboarding cost of a permanent hire.

I bring more than two decades writing software, plus engineering leadership across multiple growth-stage inflection points, and operational fluency with the modern AI-native SDLC - Claude Code, GitHub Copilot, AWS Kiro, OpenAI Codex, and frontier open-weight models. I have built and scaled engineering organizations from 10 to 60 (and well beyond when the business demanded it), shipped 99.95% SLA platforms in regulated industries, and presented architecture and investment cases directly to PE advisors and the C-suite.

Whether you need an interim leader during a transition, a strategic partner for an AI or modernization push, or a long-term fractional anchor for engineering accountability - the engagement is scoped to your outcome, with a clear definition of done.

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Why Fractional

Top-Tier Leadership Without the Full-Time Bill

Executive Expertise, Right-Sized Cost

Get the strategic judgment of a senior CTO without the salary, equity, bonus, and benefits load of a full-time hire. Engagements scoped to your actual demand - not padded to fill a calendar.

Scalable, On-Demand Engagement

Ramp leadership up or down as the business evolves. Heavier presence during a transformation push or fundraising sprint, lighter cadence during steady-state delivery. Flexibility a permanent hire cannot offer.

Outside Perspective, Real Results

Unburdened by internal politics, legacy decisions, or sunk-cost loyalty - I name what is working and what is not, then drive the change. Fresh judgment, calibrated against current AI and platform reality.

When to Engage a Fractional CTO

Three patterns where a fractional engagement consistently delivers more value than a permanent hire - or a vacuum.

Rapid Growth & Scaling

10-to-30 and 30-to-50 engineering build-outs without breaking culture. Org design, hiring loops, leveling, and engineering operating cadence - set up so the next growth phase does not collapse under its own weight.

Leadership Transition or Interim

Continuity when a CTO or VP of Engineering departs. I keep delivery on track, stabilize the team, define the role spec for the permanent hire, and run a clean handoff - so the search becomes deliberate, not desperate.

AI, Modernization, or M&A Pivot

A strategic initiative the existing team cannot execute alone - AI-native SDLC adoption, platform modernization, post-merger integration, or fundraising prep. Senior bandwidth on the lift without permanently inflating the comp structure.

Capabilities

What I Deliver as Your Fractional CTO

Technology strategy tied to business outcomes
Roadmaps grounded in revenue, cost, and risk - not framework preference. Every architectural decision traceable to the deal thesis or growth plan.
AI roadmap and agentic SDLC adoption
Multi-model strategy across Claude Code, GitHub Copilot, AWS Kiro, OpenAI Codex, Gemini, Bolt, and Lovable. Production agentic AI - not pilot theater - with governance, FinOps, and reliability SLOs baked in.
Architecture and cloud modernization direction
Enterprise platform rebuilds and AWS-native re-architecture, 99.95% SLA designs for 10k+ concurrent users, legacy monolith decomposition. HIPAA, SOC 2, ISO 27001, HL7/FHIR, and Epic EMR integration patterns - shipped, not promised.
Engineering org design, hiring, and mentoring
Right-sized teams across onshore, nearshore, and offshore. Player-coach mentoring of staff and principal engineers. Leveling, performance, and operating cadence that hold under growth.
Board, PE, and investor-ready reporting
Architecture and investment cases presented directly to PE advisors and the C-suite. Engineering metrics that survive board scrutiny - deploy frequency, MTTR, SLA attainment, FinOps - not vanity dashboards.
M&A technology diligence and integration
Pre-deal diligence, post-merger integration plans, portfolio rationalization at platform scale. Honest reads on what the acquired stack can absorb and what it cannot.

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Process

How a Fractional Engagement Runs

1. Discovery and goal-setting
We define measurable outcomes - not "improve engineering," but concrete targets like "ship the AI-native SDLC in 90 days" or "stabilize the org through the CTO search." Goals tie directly to the business plan.
2. Embed and assess
In the first two weeks I run an org, codebase, and architecture audit - listening more than prescribing. The output is a prioritized plan of what to fix, what to invest in, and what to leave alone.
3. Execute and mentor
Drive the priorities directly - architecture decisions, hiring, AI rollout, board reporting - while mentoring existing leaders. Knowledge transfer is intentional, not accidental. Internal capacity grows.
4. Hand-off or continue
Either a clean transition to a permanent CTO or VPE hire, or an evolved scope as the business need changes. End-state defined up front, not improvised.

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Rollout

The AI-Native SDLC Rollout, Phased

"Ship the AI-native SDLC in 90 days" is the most common goal a fractional engagement starts with, so this is the shape it takes. I do not promise a velocity multiple in the first 60 days. I promise a baseline in three weeks and a first measured delta at 90 days, because a number without a pre-AI baseline is a story, not evidence. Durations flex with team size and regulatory weight; the sequence does not.

Phase 0 - Baseline and configure (weeks 1 to 3)
Approved tool list and company-managed accounts, with training opt-out confirmed in writing per vendor. AI usage policy written and communicated before access is granted. Data classification mapped to every AI surface: IDE, chat, CI agent, connector. An API platform org with a service identity for CI, budgets and hard caps set. Seat mix rebalanced toward premium seats for the engineers living in an agentic tool all day. Repo hygiene: root and per-package instruction files, CODEOWNERS, a ticket-contract template, a PR template that asks what a human verified. Baseline instrumentation: cycle time, review latency, change failure rate, escaped defects, deploy frequency, and mutation score on the critical modules.
Exit: baseline published. Cost: seats only.
Phase 1 - Assist (months 1 to 3)
Plan-first review on every non-trivial ticket: a senior human approves the plan, not the diff. Risk-tiered routing live, so docs and tests do not consume the same reviewer minutes as auth and money math. AI pre-review with a first set of org-specific rules, run through an eval fixture set before it comments on a real PR. Tests generated from acceptance criteria on new work. A merge queue. AI-drafted release notes and change records.
Exit: review latency down materially - 40% is a reasonable target - QA cycle time down, change failure rate flat or better.
Phase 2 - Delegate (months 2 to 5)
Unattended agents on the safe work class only: test backfill, dependency bumps, copy, flag-off scaffolding. Bug-to-failing-test automation, where the agent's first deliverable is a test that fails today and it stops if it cannot write one. Pipeline auto-repair on the safe failure class (lockfile, formatting, types, snapshots, flakes). Contract tests for external adapters against recorded fixtures. The learn loop running on a weekly cadence: every escaped defect becomes a review rule.
Exit: 20 to 30 percent of merged PRs agent-originated, change failure rate and incidents per deploy unchanged, and agent cost per merged PR below the human loaded cost for that class of work.
Phase 3 - Compound (months 5 to 9)
An evidence pack generated on every release: change record, risk diff, impact matrix, rollback plan, control mapping. Domain-specific pipelines - for a regulated product, a rule change becomes a config diff, tests, and evidence, with humans adjudicating. An incident agent on call that proposes and drafts but never acts alone. Quarterly model and rule upgrades promoted through the eval suite, never silently.
Exit: this is the phase that becomes a moat. The review rules, skills, fixtures, and evidence are specific to you, and they compound every quarter while a competitor's stay generic.

Diagnostics

Twelve Failure Patterns I Look For in the First Two Weeks

Most organizations that call a fractional CTO about AI already have two or three of these. None of them is exotic, and every one of them is cheaper to fix before the habits set.

Tool-first rollout without guardrails. Licenses distributed before policy, measurement, or review standards exist; by the time leadership sees the gap, the shadow patterns are entrenched.

Measuring without baselines. Every positive signal gets attributed to AI and every negative one to something else, and measurement becomes storytelling.

Productivity theater. Output volume celebrated while bug escape rates rise, review depth falls, and debt accumulates quietly.

Governance after the incident. Reactive governance always costs more than proactive governance, by a wide margin, and it lands after the habits have formed.

One-size-fits-all adoption. The same tools and expectations for every team regardless of maturity, codebase, or risk profile; greenfield web rules do not fit critical financial infrastructure.

Era 3 tooling on Era 2 process. Agents opening PRs into a review process designed for one human reading one human's diff; the queue backs up or the reviews go shallow.

Coverage as the test gate. Generated tests that assert nothing, 90% coverage, green builds, and then a production incident that a mutation score would have caught.

Personal tokens in CI. An agent workflow authenticated as whoever set it up: unattributable, unbudgeted, and broken the day that person leaves.

Instruction file rot. The repo's CLAUDE.md or AGENTS.md was written once, never reviewed, and now contradicts the codebase - and the agent follows it anyway.

Model upgrade without evals. The review agent or the planning skill silently changes behavior, and nobody notices until the defect class it used to catch escapes.

Unattended agents on the wrong work class. Auth, money math, or adapters handed to an agent because the safe class was boring; the safe class is boring on purpose.

Reporting adoption as the outcome. The dashboard says 90% of engineers use AI, the CFO asks what changed, and nobody can answer.

The data behind patterns two, three, and twelve - what happens to review time, incidents, and deploys per week when output goes up and verification does not - is in Authoring Is Cheap. Verification Is the Constraint.

Target Profile

Industries & Stages I Serve

Strongest fit is growth-stage companies under 250 people with engineering teams of 10 to 60 - the inflection point where engineering becomes a strategic asset, not a cost center.

Growth-Stage SaaS

Series A through C SaaS scaling engineering from 10 to 60. Org design, AI-native SDLC, and architecture decisions that hold through the next funding round.

PE-Backed Portfolio

Portfolio companies needing engineering accountability tied to the deal thesis. Board-ready reporting, post-merger integration, and platform rationalization.

HealthTech & Regulated

HealthTech, MedTech, EdTech, and regulated B2B SaaS. HIPAA, SOC 2, ISO 27001, HL7/FHIR, PCI, and Epic EMR integration shipped in production.

Companies Pivoting to AI

Organizations moving from AI experiments to production agentic AI. Multi-model strategy, governance, FinOps, and reliability SLOs - not demos.

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If a fractional CTO engagement is the right fit for your stage, let's talk through the outcome you need and the shape of the engagement that gets you there.

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