Technical leadership for teams that need it part-time, not part-way
For companies with strong engineers but no seasoned technical executive yet, or ones bridging between CTOs. We bring architecture judgment, reliability discipline, and the same evidence-first habits we use in compliance work, applied to your roadmap instead of an auditor's checklist.
How we engage
Strategic retainer
Ongoing CTO or VP-Engineering function: design reviews, vendor negotiations, release governance, and an escalation path for incidents. Weekly or biweekly cadence.
- Architecture council & decision records
- Technical due diligence (buy, build, or partner)
- Hiring loops & level calibration
Targeted sprint
Deep work on a bounded problem: a reliability program bootstrap, a pre-audit system review, or a load-testing push before a launch. Clear deliverables, fixed timebox.
- Written playbooks you keep
- Handoff to your internal team
Interim leadership
Bridge coverage between CTOs or during a key technical hire. Focused on stabilizing on-call, vendor posture, and technical debt before the next leader starts.
- Incident and vendor triage
- Board- and customer-facing updates
Typical cadence
Most retainers start with a 2 to 4 week discovery: a codebase walkthrough, observability and incident history, an identity and data map, and a look at where your architecture and your compliance obligations already overlap. From there we set quarterly goals and a weekly operating rhythm (office hours, pre-mortems, release go/no-go).
Output artifacts
Strategic: a multi-quarter architecture outlook, cloud cost models, and a written technical narrative for customers or investors. Tactical: design review minutes, vendor scoring, and escalation playbooks for production incidents.
Signals this fits
- A growing engineering team without a dedicated technical exec
- Customer SLAs and cloud spend both climbing at once
- "We need SOC 2 or ISO this year" without a shared plan for it
- A key technical hire pending, but delivery can't pause
Currently in the seat: an AI biotech company
This isn't theoretical. AlphaVerify's active fractional CTO practice includes a venture-backed AI biotech company, where the work spans four areas at once.
AI systems
Architecture for a proprietary AI platform, including model data pipelines and the governance layer around them.
Infrastructure
The systems work underneath the AI: data pipelines, storage, and the platform choices that determine what the AI can reliably do.
Scale
Planning for a research and data platform that grows in both data volume and computational demand, not just user count.
Regulatory alignment
Life-sciences-adjacent data handling and AI governance built in from the architecture stage, not retrofitted before a partner's due diligence review.
Resilience is a product feature
Users don't experience uptime as a percentage. They experience slow pages, partial failures, and confusing errors when a dependency goes down. We help you design, measure, and invest in the behaviors that actually matter to revenue and trust.
Graceful degradation
Feature flags, read-only fallbacks, and a visible "reduced service" state that beats a silent failure or a full outage.
Load & back-pressure
Queuing, concurrency limits, and client-side throttling that protect shared resources before autoscaling quietly runs up the bill.
Operability
Runbooks and dashboards that reflect your actual dependencies, so engineers stop guessing during an incident.
Service levels that hold up
We work backward from customer-visible journeys, not internal service names, to set SLIs you can measure today, SLOs the business will actually defend, and error budgets that inform release policy. Many compliance frameworks (SOC 2, ISO 27001) expect evidence of monitoring and incident response anyway, so a healthy reliability program doubles as part of your assurance story instead of running as a parallel effort.
Scale is more than request volume
We've designed and steered infrastructure across continents, regulated health data, and government partnerships. Scale here means traffic, data gravity, organizational complexity, and the economic reality that cloud spend grows on its own if nobody's watching.
Request & data path
Horizontal scaling, sharding discipline, and cache coherence. We profile where latency really lives, which is usually not where the architecture diagram says.
Geography & residency
Multi-region design when it's justified, honest RTO/RPO when it isn't, and data residency treated as a design input from the start, not an afterthought.
Cost & FinOps
Unit economics measured per user journey, not per instance hour, and the architectural choices that quietly lock in waste if made without thinking it through.
Organizational scale
Fifty engineers change how your platform works whether you plan for it or not. We focus on team interfaces: who owns a shared service's SLO, and how a platform team avoids becoming a bottleneck.
Identity and directory federation is its own scale problem: OIDC and SAML for enterprise customers, token lifetimes and rotation, and access reviews that reflect actual group membership instead of a quarterly spreadsheet exercise. 25+ years of that work informs how we think about every other failure mode.
Talk through your technical leadership gap
Half-day to full-day working sessions available if you want to test fit before a retainer.