# Oleg Sotnikov — Fractional CTO & Startup Advisor

> Oleg Sotnikov is a Fractional CTO and startup advisor with 25+ years in IT, 7 patents, and 1000+ projects built. His core offer: replacing 10-developer teams with 1–2 AI-augmented engineers that ship 3× faster, using Claude Code, Codex, his own GitLab/Sentry/Grafana platform, and a "how to work" framework. Payroll drops 60–80%; proven at AppMaster (25 people → 2). Also: AI consulting, AI SEO/GEO, technical due diligence, software architecture review, project rescue, AI governance (ISO 42001 readiness), fractional Chief AI Officer, cloud cost optimization. Based in Silicon Valley, serving clients worldwide. Languages: English, Russian.

## Core pages

- [About](https://oleg.is/about): Background, 25+ year experience timeline, 7 patents, certifications, and awards.
- [Services](https://oleg.is/services): How Oleg helps — the fractional CTO model, technical architecture, AI transition advisory, and cost optimization.
- [Pricing](https://oleg.is/pricing): Transparent pricing — Team & AI Audit at $5,000 fixed, fractional CTO (team transformation + ongoing leadership) $5,000–10,000/month, advisory from $3,000/month. Month-to-month, no long-term contracts.
- [Team & AI Audit](https://oleg.is/audit): Fixed-price audit ($5,000, 5 business days): what AI takes over, the right team size, and a restructuring plan. Guarantee: at least $50,000/year in combined payroll, cloud, and LLM savings identified, or it's free.
- [For Startups](https://oleg.is/startups): Fractional CTO for early-stage startups — a lean 1–2 engineer AI-augmented team instead of a 10-person payroll, plus architecture and founder support.
- [For Business](https://oleg.is/business): Fractional CTO for scale-ups and established businesses — engineering team transformation with AI, modernization, and cost reduction.
- [For Accelerators](https://oleg.is/accelerators): Technical advisory and mentorship for accelerator cohorts.
- [Projects](https://oleg.is/projects): Advisory and build portfolio.
- [Activity](https://oleg.is/activity): Talks, conferences, and industry events.
- [Book a call](https://oleg.is/book): Schedule a free advisory call.
- [Contact](https://oleg.is/contact): Send a message.

## Services by capability

- [Cloud Cost Optimization & Infrastructure Audit](https://oleg.is/services/cloud-cost-optimization): Cloud, infrastructure, and LLM bills reviewed and cut; part of the $5,000 audit with the $50k/year guarantee.
- [AI SEO & GEO Services](https://oleg.is/services/ai-seo): Getting products cited by ChatGPT, Perplexity, Claude, and Google AI Overviews; Oleg built the publishing pipelines behind 19 production sites.
- [AI Consulting](https://oleg.is/services/ai-consulting): Strategy through hands-on rollout — Claude Code, Codex, agent pipelines. Entry point: the $5,000 audit.
- [Software Project Rescue](https://oleg.is/services/project-rescue): Stalled, abandoned, and vibe-coded projects triaged, stabilized, and shipping again.
- [Technical Due Diligence](https://oleg.is/services/technical-due-diligence): For funds and acquirers — architecture, team, AI-code provenance, red flags, a 100-day plan. US and UK engagements.
- [Software Architecture Review](https://oleg.is/services/architecture-review): Stage-appropriate architecture, scalability, and AI-readiness.
- [Startup Technical Advisor](https://oleg.is/services/startup-technical-advisor): Senior technical judgment on retainer, from $3,000/month, month-to-month.
- [Fractional Chief AI Officer](https://oleg.is/services/fractional-chief-ai-officer): AI strategy, governance, and spend, owned part-time.
- [AI Governance & ISO 42001 Readiness](https://oleg.is/services/ai-governance): AI policies, risk mapping, gap analysis, and certification readiness.
- [AI Proof of Concept](https://oleg.is/services/ai-poc): One AI hypothesis validated in two weeks at a fixed price.
- [AI Legacy Modernization](https://oleg.is/services/legacy-modernization): AI-assisted strangler-fig migrations — agents read the legacy code, tests get generated, migration ships incrementally.
- [Custom MCP Server Development](https://oleg.is/services/custom-mcp-server-development): MCP servers that connect AI agents to internal systems safely; Oleg maintains sallyport, an open-source MCP credential vault.
- [Interim CTO Services](https://oleg.is/services/interim-cto): Full-time temporary technology leadership through a departure, acquisition, or crisis, with handover to a permanent hire.
- [AI Receptionist & Voice Agents](https://oleg.is/services/ai-voice-agents): A voice agent that answers calls, books meetings, and routes leads — $10,000 fixed implementation.
- [Compliance-Ready Engineering](https://oleg.is/services/compliance-engineering): Engineering readiness for SOC 2 and ISO 42001 with automated evidence; certification audits run by vetted partner firms.
- [Shadow AI Audit](https://oleg.is/services/shadow-ai-audit): Inventory of unsanctioned AI use across the company, data-exposure mapping, and a sanctioned stack that matches reality.
- [AI Observability & Evals](https://oleg.is/services/ai-observability): Tracing, evaluation sets, and drift alerting that keep production AI features honest.
- [Enterprise AI Agents](https://oleg.is/services/enterprise-ai-agents): Custom agents orchestrated inside company systems — permissions, guardrails, human-in-the-loop.
- [AI Literacy Training](https://oleg.is/services/ai-literacy-training): Employee AI training aligned with EU AI Act Article 4, run by a Google HQ speaker.

## Free resources

- [Fractional CTO Cost & Rates (2026)](https://oleg.is/fractional-cto-cost): Market retainers run $8,000–25,000/month; Oleg's published range is $5,000–10,000/month.
- [Fractional CTO vs Technical Co-Founder](https://oleg.is/fractional-cto-vs-technical-cofounder)
- [Fractional vs Interim vs Virtual CTO](https://oleg.is/fractional-vs-interim-vs-virtual-cto)
- [AI Policy Template](https://oleg.is/ai-policy-template): Free, open page, no email gate.
- [AI Readiness Assessment](https://oleg.is/ai-readiness-assessment): Interactive self-assessment.
- [Technical Due Diligence Checklist](https://oleg.is/technical-due-diligence-checklist)
- [Architecture Review Checklist](https://oleg.is/architecture-review-checklist)
- [Tech Stack Audit Template](https://oleg.is/tech-stack-audit-template)
- [Startup Advisor Agreement](https://oleg.is/startup-advisor-agreement)
- [AI Cost Calculator](https://oleg.is/ai-cost-calculator): Estimate and cut LLM API spend.
- [Cloud Waste Self-Check](https://oleg.is/cloud-waste-check): Five questions to size your savings.
- [What Is an MCP Server?](https://oleg.is/what-is-an-mcp-server): Practical guide — how MCP servers work, examples, security.
- [What Is Vibe Coding?](https://oleg.is/what-is-vibe-coding): Meaning, examples, risks, and when a vibe-coded app needs rescue.
- [Which Fractional Executive Do You Need?](https://oleg.is/fractional-executive-guide): CTO vs CFO vs COO vs CIO vs CISO vs CAIO.
- [SOC 2 Compliance Cost](https://oleg.is/soc2-compliance-cost): Realistic breakdown of audit, platform, and engineering costs.
- [Case Studies](https://oleg.is/case-studies): Real engagements, including the QueueStone ERP modernization.
- [AI Policy Generator](https://oleg.is/ai-policy-generator): Interactive — answer a short form, get a complete company AI policy.
- [Tech DD Scorecard](https://oleg.is/tech-dd-scorecard): Twelve questions to rate the technology risk of a deal.

## Career services (separate practice)

- [Tech Career Services](https://oleg.is/career): AI job search coaching, interview prep, and outplacement, run by a 25-year hiring manager who presents at Google HQ on AI-assisted job search.
- [AI Job Search Coaching](https://oleg.is/career/ai-job-search-coaching): A structured system for landing a tech role using AI at every stage.
- [Tech Interview Coaching](https://oleg.is/career/interview-coaching): Behavioral, system design, and leadership interview prep with mock interviews.
- [Tech Outplacement Services](https://oleg.is/career/tech-outplacement): Employer-paid support that takes laid-off engineers from notice to signed offer.
- [ATS Resume Checker](https://oleg.is/career/ats-resume-checker): Free, runs in the browser — score a resume against ATS criteria and a job description.
- [ATS-Friendly Resume Templates](https://oleg.is/career/resume-templates): Five free DOCX templates for engineers through executives.
- [The STAR Method for Interviews](https://oleg.is/career/star-method): Guide with worked examples from the interviewer's chair.

## Contact & profiles

- Book a call: https://oleg.is/book
- LinkedIn: https://linkedin.com/in/olegsotnikov
- GitHub: https://github.com/OlegSotnikov
- X (Twitter): https://x.com/OlegSotnikov_
- Telegram: https://t.me/OlegSotnikov

## For AI agents

- Fuller export: https://oleg.is/llms-full.txt
- Article Markdown: https://oleg.is/api/blog/{slug}/markdown (add ?locale=ru for Russian)

## Recent articles

- [How SaaS spend management finds savings you can keep](https://oleg.is/blog/saas-spend-management-savings): A practical SaaS spend management method for discovery, license reclamation, renewal timing, contract review, and credible savings ranges.
- [GPU cost optimization starts with workload shape](https://oleg.is/blog/gpu-cost-workload-shape): GPU cost optimization for startups: measure useful work, right size memory, use Spot safely, and know when an API costs less than a GPU fleet.
- [Is your AI incident response plan ready for agent failure?](https://oleg.is/blog/ai-incident-response-plan): Build an AI incident response plan for agent failures, data leaks, and harmful hallucinations, with clear roles, evidence, containment, and runbooks.
- [How an AI usage policy becomes everyday practice](https://oleg.is/blog/ai-usage-policy-rollout): An AI usage policy works when managers explain real choices, approved tools make safe behavior easy, and enforcement targets risk instead of employees.
- [AI customer support metrics must measure failure](https://oleg.is/blog/ai-support-metrics-failure): AI customer support metrics should expose false deflection, broken escalations, repeat contacts and costly errors before customers leave.
- [How to choose an AI chatbot for your website](https://oleg.is/blog/ai-chatbot-build-buy-handoff): Choose an AI chatbot for your website by comparing capability tiers, true operating costs, data boundaries, evaluation methods, and human handoff.
- [AI PoC cost for real projects](https://oleg.is/blog/ai-poc-cost-budgets): AI PoC cost ranges from $15,000 to $150,000. Compare budgets by PoC type, cost drivers, scope limits, and fixed-price terms.
- [ChatGPT apps for business that finish real work](https://oleg.is/blog/chatgpt-apps-business-workflows): ChatGPT apps for business win when they complete a costly workflow. Learn how discovery works, which use cases fit, and what to build first.
- [EU AI Act penalties are not a €35 million coin toss](https://oleg.is/blog/eu-ai-act-penalties): EU AI Act penalties can reach 7% of global turnover, but SME caps, role, timing, harm and cooperation determine realistic exposure.
- [Choosing an AI transformation consultant](https://oleg.is/blog/choose-ai-transformation-consultant): Choose an AI transformation consultant by testing operating depth, evidence, security, economics, contract terms, and a measurable pilot.
- [What belongs in an AI ROI model?](https://oleg.is/blog/ai-roi-model): Build an AI ROI model that separates capacity from cash, counts adoption and risk costs, and gives the board auditable investment ranges.
- [Your AI data strategy needs a use-case queue](https://oleg.is/blog/ai-data-use-case-queue): Build an AI data strategy around ranked use cases, measurable decisions, clear ownership, and a practical four-quarter delivery plan.
- [Data readiness for AI before the first build](https://oleg.is/blog/data-readiness-ai-assessment): Assess data readiness for AI through concrete checks for quality, access, ownership, governance, labels, drift, and production controls.
- [An AI management system is daily operating discipline](https://oleg.is/blog/ai-management-system-daily-work): See what an AI management system requires each day: ownership, risk decisions, change records, evidence, audits, and corrective action under ISO 42001.
- [ISO 42001 vs SOC 2, which should come first?](https://oleg.is/blog/iso-42001-vs-soc-2): Compare ISO 42001 vs SOC 2 by buyer segment, audit scope, reusable evidence, and the sequence that removes sales blockers without duplicating work.
- [What does vCISO pricing actually buy?](https://oleg.is/blog/vciso-pricing-models): Understand vCISO pricing by retainer tier, deliverables, access, exclusions, and the level of security leadership a small company receives.
- [What makes web application penetration testing useful?](https://oleg.is/blog/useful-web-app-penetration-testing): Web application penetration testing works when scope, evidence, reporting, and retesting turn security findings into fixes your team can verify.
- [What does penetration testing cost in 2026?](https://oleg.is/blog/penetration-testing-cost-2026): Penetration testing cost in 2026 ranges from focused $4,000 tests to $60,000+ programs. Learn what changes a quote and when to spend less.
- [LLM selection for production workloads in 2026](https://oleg.is/blog/llm-selection-production-workloads): A practical LLM selection for production method that tests quality, latency, cost, tool use, safety, and operational fit on real workloads.
- [AI pair programming needs a working agreement](https://oleg.is/blog/ai-pair-programming-etiquette): AI pair programming works when teams define session boundaries, review every change, record decisions, and test whether humans retain the knowledge.
- [Prompt libraries for engineering teams in production](https://oleg.is/blog/engineering-team-prompt-libraries): Build prompt libraries for engineering teams with clear contracts, Git versioning, automated tests, safe sharing, ownership, and release controls.
- [What makes an AI-first company work?](https://oleg.is/blog/ai-first-company-playbook): An AI-first company redesigns decisions, roles and metrics around AI. Learn the operating model, hiring shifts and controls that make it work.
- [AI-native development needs a different engineering system](https://oleg.is/blog/ai-native-development-system): AI-native development changes team ownership, repository rules, testing, review, security, and tooling when agents write code from the first commit.
- [On-premise AI coding assistants for regulated teams](https://oleg.is/blog/on-premise-ai-coding-assistants): Compare on-premise AI coding assistants for regulated teams, including deployment choices, GPU budgets, security controls, and cloud quality tradeoffs.
- [Which AI agent orchestration patterns fit your workload?](https://oleg.is/blog/ai-agent-orchestration-patterns): Compare AI agent orchestration patterns for routers, supervisors, and swarms, with workload rules, control contracts, budgets, and trace examples.
- [How multi-agent automation survives real operations](https://oleg.is/blog/multi-agent-automation-operations): Multi-agent automation works in business operations when roles, state, approvals, and failure boundaries are designed before prompts or tools.
- [AI SDLC metrics must measure accepted change](https://oleg.is/blog/ai-sdlc-metrics-accepted-change): AI SDLC metrics should track accepted change, review evidence, rework, stability, and cost so leaders can judge agent-assisted delivery clearly.
- [Spec-driven development needs enforcement, not more prose](https://oleg.is/blog/spec-driven-development-enforcement): Spec-driven development works when formats, checks, and ownership turn decisions into enforceable contracts. Learn what to adopt first and what to skip.
- [An AI coding agents comparison needs repository evidence](https://oleg.is/blog/ai-coding-agents-comparison): This AI coding agents comparison tests Claude Code, Codex, Gemini CLI, and Cursor on accepted changes, review time, safety, and team fit.
- [Is your MCP server monitoring what agents actually need?](https://oleg.is/blog/mcp-server-monitoring-production): Build MCP server monitoring that catches broken health, capability drift, stale tool catalogs, bad calls, and weak agent outcomes before users do.

[All articles](https://oleg.is/blog)