Fractional CTO and Engineering Advisory

Translating your business model and growth trajectory into a concrete technical strategy, not a slide deck of principles, but a working 12-18 month roadmap your engineering team can execute from and that your board or investors can scrutinise.

The strategy process starts with the business context: your current revenue model, where the business is going in 18 months (new market, new product line, enterprise upmarket move, geographic expansion), and the technical constraints that are already limiting growth. From there, we map the architectural decisions that will determine whether you can support that trajectory: the data model that allows multi-tenant enterprise expansion vs. the one that requires a complete re-schema; the monolith that ships features in 2-week cycles vs. the premature microservices split that doubles deployment complexity; the cloud infrastructure that costs $8,000/month at current scale vs. $80,000/month at 10x.

Strategic prioritisation framework: each initiative on the roadmap assessed on three dimensions, business impact (what revenue or retention risk does this address), reversibility (how expensive is it to undo if the business direction changes), and technical debt cost (what is the current rate at which this constraint slows engineering delivery, in days per quarter). High-impact, low-reversibility decisions are scheduled earliest because they're the ones where delay is most expensive.

Platform bet evaluation: we assess which bets are defensible at your current scale and which are premature optimisation. A company at $2M ARR does not need Kafka; at $20M ARR with real-time data requirements it might. A company with 10 engineers doesn't need a microservices architecture; at 50 engineers with separate product domains it might. The strategy document explicitly states the conditions under which each architectural decision should be revisited, so the next major review isn't triggered by a crisis but by a planned inflection point.

Reviewing major architectural decisions before they're made and before they're expensive to reverse. The decisions that matter most are the ones that feel small at the time: a database schema chosen for MVP simplicity, an API contract that becomes an integration standard, an infrastructure choice that defines your operational model for three years.

Decision types we review: database and data model design (PostgreSQL vs. MongoDB vs. a polyglot approach; normalised relational schema vs. document model for your specific query patterns; partitioning strategy for tables that will reach 100M+ rows); API design (REST vs. GraphQL vs. gRPC; versioning strategy that doesn't break existing clients; pagination contract for endpoints that must stay stable as data volumes grow); service decomposition (where the monolith-to-services split makes sense and where it adds complexity without benefit, the strangler fig pattern timeline for a specific bounded context); and infrastructure choices (RDS managed PostgreSQL vs. self-managed on EC2; ECS Fargate vs. EKS Kubernetes for a team of 8 engineers; Vercel edge functions vs. a traditional API server for your specific latency and throughput profile).

Architecture Decision Records (ADRs): every major decision reviewed produces an ADR documenting the context (what problem prompted this decision), the options considered with their trade-offs, the recommendation and rationale, and the conditions under which this decision should be revisited (e.g., "revisit this decision when monthly active users exceed 50,000 or when the team exceeds 15 engineers"). ADRs stored in the Git repository alongside the code, so context is available to future engineers without relying on institutional memory.

Security architecture review included where relevant: authentication flow design (JWT vs. session tokens; token rotation policy; multi-tenancy data isolation enforcement at the API layer vs. database layer); API rate limiting strategy; secret management (AWS Secrets Manager vs. environment variables for different secret categories); and network topology (public vs. private subnet design for database and internal service access).

Structured assessment of your engineering organisation's delivery capability when you suspect a problem but can't diagnose its root cause. Slow shipping, high defect rates, and constant firefighting have specific causes, the assessment identifies them with evidence, not intuition.

Assessment methodology (2-3 weeks): codebase analysis using static analysis tools (SonarQube for code quality metrics, Semgrep for security pattern detection, dependency-check for known vulnerability scanning) to produce objective data on test coverage, cyclomatic complexity, code duplication, and security debt; git history analysis (commit frequency, PR cycle time from open to merge, deployment frequency, revert rate) to measure delivery velocity and deployment health; incident and bug review (production incident frequency, mean time to resolution, recurring issue categories) to identify reliability patterns; 1:1 sessions with engineers and leads (structured around what slows them down, what decisions they're waiting on, and what they'd change if they could) to surface systemic process issues that don't appear in the code metrics.

Root cause categorisation: slow delivery most commonly traces to one of five root causes, accumulated technical debt in a specific area of the codebase that affects every feature touching it (identifiable from complexity metrics and commit frequency in the affected modules); unclear ownership across team boundaries creating handoff delays (identifiable from PR review patterns and who has commit rights to what); missing deployment automation creating fear of deployment (identifiable from deployment frequency and PR-to-production cycle time); insufficient test coverage creating risk aversion (identifiable from test coverage in the modules with the highest change frequency); or skill gaps in a specific area (backend performance, database query optimisation, frontend state management) that creates bottlenecks on one or two engineers.

The assessment output is a findings report with a prioritised remediation plan: specific actions per root cause, estimated engineering effort, expected improvement in delivery velocity, and a sequenced plan that addresses the highest-leverage problems first. Most leadership teams find at least one finding that reframes how they understood the problem.

Objective evaluation of specific build-vs-buy decisions with a total cost of ownership model that makes the trade-off quantitative, not intuitive. The answer is not always "buy", but the analysis makes the decision defensible to your board and your engineering team.

Evaluation framework applied to each decision: integration cost (how many engineer-weeks does the integration take, what is the ongoing maintenance burden, and does it require specialised skills your team doesn't have?); recurring cost (SaaS licensing or API usage fees at your current and projected volume, $500/month at 1,000 users can become $50,000/month at 100,000 users if pricing is usage-based); build cost (how many engineer-weeks does it take to build to the required quality level, and what is the opportunity cost of that time vs. product features?); operational burden (who supports it at 2am when it fails, and does your team have the expertise?); vendor risk (what is the switching cost if the vendor raises prices by 3x or discontinues the product, and what is the probability of that scenario?).

Categories we evaluate frequently: identity and authentication (Auth0 at $0.02/MAU vs. Clerk at $0.02/MAU vs. building on NextAuth + PostgreSQL, the right answer depends on enterprise SSO requirements, SAML support, and audit log requirements); search (Algolia at $1/1000 operations vs. Elasticsearch self-hosted at $2,000/month engineering burden vs. pgvector for semantic search, depends on query volume, latency requirements, and whether full-text search or semantic search is the primary use case); observability (Datadog at $15-23/host/month vs. self-hosted Prometheus + Grafana at 20 hours setup + 2 hours/month maintenance, Datadog wins below 20 hosts, self-hosted wins above 50 hosts for most companies); AI infrastructure (OpenAI GPT-4o API at $10/million output tokens vs. self-hosted Llama 3.1 70B on AWS g5.12xlarge at $4.096/hour, break-even depends on query volume and latency tolerance).

The evaluation produces a decision document with the TCO calculation at 12 months and 36 months, the key assumptions, the recommendation, and the conditions under which the recommendation would flip (e.g., "buy Auth0 now; evaluate switching at 50,000 MAUs when monthly cost exceeds $1,000").

Technical due diligence for investment and acquisition decisions: structured evaluation of a target company's technical assets and liabilities, producing a findings report that frames risk in terms an investor or acquirer can act on, not a list of code quality scores that require a CTO to interpret.

Diligence scope (typically 5-10 business days depending on codebase size): codebase quality assessment using SonarQube (code coverage, duplication, cyclomatic complexity, known security vulnerabilities via CVE database match on dependencies); architecture review (is the architecture capable of supporting the claimed product roadmap, or does it require a re-architecture before new features can be built?); infrastructure and deployment review (deployment frequency, rollback capability, uptime monitoring, disaster recovery documented and tested); security posture (authentication implementation, data encryption at rest and in transit, OWASP Top 10 exposure in the web application, secrets management, GDPR or HIPAA compliance where applicable); technical debt inventory (estimated remediation cost in engineer-months for identified issues); and team assessment (are the claimed engineering capabilities present in the team, or is the system dependent on one or two key people who constitute a departure risk?).

Risk classification in the findings report: each identified issue classified by severity (material, requires immediate remediation before or as a condition of the transaction; significant, requires a remediation plan with timeline; minor, normal engineering debt not affecting transaction risk) and remediation cost (sprint-level, quarter-level, or multi-quarter). The executive summary translates findings into business risk language: "the authentication implementation has a session fixation vulnerability that would require 3-4 weeks of remediation; this is exploitable in a targeted attack but unlikely in a commodity attack" rather than "CWE-384 detected in authentication module."

Investor and acquirer use: the report is written to be read by non-technical board members and investment committee members. We include a one-page risk summary with a traffic light assessment per area (green/amber/red) and the estimated impact on transaction value or post-close engineering investment required.

CTO coverage during leadership transitions, between a departing CTO and a permanent hire, during a technical co-founder's transition from technical to commercial leadership, or while evaluating whether a senior engineer or engineering manager is ready for promotion to CTO. Leadership gaps create specific engineering risks that compound if unaddressed: decisions that require CTO-level authority accumulate; the engineering team waits for direction on architectural choices; and the permanent hire joins a backlog of unresolved technical decisions and a team that has been in holding pattern for 3-6 months.

What interim coverage covers: weekly attendance at architecture reviews and sprint planning with decision authority on architectural questions (choice of database, API contract decisions, infrastructure choices, service decomposition decisions); monthly board-level technical briefings with an infrastructure and security update that the CEO can present without a CTO; quarterly engineering health reviews covering delivery metrics, incident frequency, and team attrition; and day-to-day availability via Slack for engineering team questions that require senior technical judgment. The coverage is structured to preserve continuity, the engineering team continues to ship at the same cadence rather than operating with a de-facto technical lead who lacks the authority to make binding decisions.

Transition architecture: the engagement is explicitly designed to transfer cleanly to the permanent hire. Context documentation produced throughout the engagement includes: an architecture decision log covering all significant decisions made during the interim period with full rationale; a technical roadmap document covering the next 12 months of initiatives; a team assessment with individual strengths, development areas, and hiring priorities; vendor and partner contacts with relationship history; and a security and compliance status document covering the current posture, open issues, and scheduled renewal dates. The new CTO arrives with full context rather than discovering the organisation's history through investigation.

Promotion evaluation: for companies evaluating whether an engineering manager is ready for the CTO role, the interim CTO engagement includes a structured assessment of the candidate's technical depth, strategic thinking, communication with non-technical stakeholders, and leadership presence, producing a documented recommendation with development areas if a promotion timeline of 6-12 months is realistic.