Pavel ShpinPavel Shpin - Technical Due Diligence Expert

The Systemic Architect Behind Your Investment Decision

As a Systemic Architect and battle-tested founder, I partner with CEOs and investors to translate ambitious business goals into resilient and valuable technology. Having navigated the full entrepreneurial lifecycle from bootstrapping to a successful exit, I bring a unique, 360-degree perspective that bridges the chasm between strategy and deep technical execution. My focus is on architecting not just software, but entire systems of people, process, and technology.

This holistic approach moves beyond temporary fixes to build a robust foundation for the future. It de-risks innovation, transforms technical debt into a strategic asset, and ensures your platform is engineered for scalability and a successful exit. I provide the founder-level empathy and deep technical authority needed to turn high-stakes challenges into sustainable growth.

Today, I run independent technical due diligence and pre-investment readiness—bringing a founder's lens to evaluate the system behind the code and the team behind the system.

https://linkedin.com/in/pshpin

Pavel Shpin portrait

Selected Recent Diligence & Readiness Engagements

Client identities and certain specifics are withheld under NDA;
details are anonymized while preserving the substance of findings.

Seed B2B Fintech SaaS — Investor-side 3-Day TDD

Decision: Go (with conditions)

Scope: scalability, release safety, and key-person dependency around the payments and fraud subsystems.

Selected findings (and what we did within the window)

  • Single-threaded CI/CD pipeline owned by one engineer — Why it matters: continuity risk and delivery slippage jeopardize post-close revenue milestones. Action: documented pipeline, added a backup maintainer, and introduced protected branches. Partially addressed

  • Secrets committed to repo; ad-hoc env configs — Why it matters: security incident and regulatory exposure create reputational damage and down-round risk. Action: rotated compromised secrets, moved to managed secrets, and standardized env var contracts. Addressed

  • No automated rollback strategy; manual hotfixes — Why it matters: outage-driven churn and SLO breaches directly impact revenue reliability. Action: sketched canary + feature flag rollout and created a rollback runbook. Plan approved

  • Risky DB migrations; missing verified backups — Why it matters: data loss/compliance events trigger escrow/indemnities and closing delays. Action: enabled daily PITR backups and instituted pre-flight migration checklist. Addressed

Investor decision: Go, with conditions. Conditions tied to completing rollout safety and cross-training within 30 days.

Series A DevTools (AI-enabled) — Founder-side Readiness

Decision: Go (with conditions)

Scope: AI governance, IP hygiene, and maintainability to defend valuation during diligence.

Selected findings (and what we did within the window)

  • AI-generated code hotspots with duplication and limited tests — Why it matters: maintainability and velocity risk inflate burn and threaten growth targets. Action: instituted contribution guidelines; prioritized refactors with unit tests around critical paths. Partially addressed

  • Shadow AI usage without governance — Why it matters: IP contamination/ownership ambiguity invites valuation haircut and legal friction at exit. Action: drafted and adopted an AI usage policy; centralized model access; created redline prompts repository. Addressed

  • Third-party code snippets with unclear licenses — Why it matters: IP uncertainty can trigger closing conditions, escrow, or rework. Action: replaced suspect snippets; performed OSS license sweep; updated attribution. Addressed

Investor decision: Go, with conditions. Conditions tied to test coverage gates and AI governance evidence in the data room.

Healthcare Data Platform — Independent Investor TDD

Decision: Defer (remediate, then revisit)

Scope: data lineage and privacy posture, access control, model risk, and recoverability.

Selected findings (and what we did within the window)

  • PII co-mingled in analytics bucket; lineage unclear — Why it matters: regulatory non-compliance and liability lead to delays, escrow, or indemnities. Action: segregated datasets; added tags and lineage annotations; updated retention policies. Partially addressed

  • Shared admin credentials; weak least-privilege IAM — Why it matters: elevated breach probability impacts cyber insurance, brand, and board risk. Action: introduced group-based roles; enabled MFA; rotated credentials. Addressed

  • No model drift monitoring in production — Why it matters: harmful/low-quality recommendations create legal and brand exposure. Action: implemented lightweight drift dashboards; defined model performance SLOs. Plan initiated

  • Unclear RTO/RPO; no DR drills — Why it matters: reliability uncertainty and extended MTTR impact revenue stability and SLAs. Action: drafted DR plan; scheduled quarterly restore tests. Plan approved

Investor decision: Defer. Revisit in 60 days post-DR drill and consistent lineage reporting.

Marketplace SaaS — Scalability & Reliability TDD

Decision: Go (milestone tranches)

Scope: performance under growth, architecture brittleness, and operational maturity.

Selected findings (and what we did within the window)

  • Hollow-core monolith with N+1 queries on hot paths — Why it matters: scalability ceiling drives margin compression; CAC payback extends. Action: prioritized query optimization; defined a strangler-fig decomposition plan. Partially addressed

  • Synchronous external API in checkout critical path — Why it matters: revenue leakage via timeouts and SLO breaches increases churn. Action: introduced queue-based fallback design; added timeouts and retries. Plan approved

  • Near-zero observability; logs only — Why it matters: high MTTR and blind spots obscure risk; undermines revenue stability. Action: added request tracing starter pack and error-rate dashboards. Addressed

  • Payment integration owned by a single engineer — Why it matters: key-person dependency threatens continuity and roadmap execution. Action: created runbook and cross-training plan. Partially addressed

Investor decision: Go, with milestone tranches tied to decomposing the hot path and achieving SLOs.

IoT Logistics Platform — Infra, Licensing & Release Safety

Decision: Go (contingent)

Scope: infrastructure-as-code maturity, blue/green deploy safety, and open-source licensing risk.

Selected findings (and what we did within the window)

  • Manual server provisioning; minimal IaC — Why it matters: environment drift and slow change jeopardize scale-up plan and audit readiness. Action: began Terraforming core services; codified networking and secrets. Partially addressed

  • No blue/green or canary; risky full-cutover deploys — Why it matters: release outages during peak season drive SLA penalties and churn. Action: designed blue/green pipeline with health checks and gradual traffic shifting. Plan approved

  • AGPL-licensed component embedded in a core service — Why it matters: license contagion threatens IP defensibility; potential deal blocker. Action: replaced with permissive alternative; initiated legal review for contamination risk. Addressed

  • Vendor lock-in with no portability plan — Why it matters: pricing power and portability risks can compress gross margins post-close. Action: introduced abstraction at storage and messaging layers; exit checklist created. Partially addressed

Investor decision: Go, contingent on legal clearance of IP and completion of the first blue/green cutover.

"If these patterns feel familiar, it's because they are. The value of diligence is turning recurring failure modes into predictable, investable outcomes."

What I look for (and why it matters)

Beyond code quality, I audit for AI-era failure modes that destroy enterprise value: Shadow AI IP leakage, AI-induced technical debt, key-person 'AI whisperer' risks, concept drift in production models, and the absence of observability for probabilistic systems. Each finding is quantified for business impact and tied to an actionable mitigation plan.

"Technology is a mirror. I evaluate the operating system of the team that built it."

A Career Built on Solving Systemic Problems

Roadshow.ai

Solved crippling internal 'process debt' by architecting a sophisticated optimization engine for our own operations.

Consolidesk

Productized an internal marketing tool into a commercial SaaS platform, mastering event-driven microservices.

Targetflow

Pioneering 'cognitive architecture' for AI agents that can reason about business goals.

Credentials & Continuous Learning

Executive Education, Harvard Business School

Executive Education, NYU Stern School of Business

Executive Education, Stanford University

Executive Education, MIT Sloan School of Management

M.S. Industrial Electronics, Tomsk State University of Control Systems and Radioelectronics (TUSUR)

Philosophy: Systems Thinking Applied

Every technology decision reveals something fundamental about an organization's DNA. A well-architected system doesn't happen by accident—it's the inevitable result of disciplined thinking, strategic planning, and long-term vision. This is what I evaluate in every engagement: not just what was built, but the quality of the thinking that guided the building.

"Resilient systems are built by resilient teams. My job is to assess both."