RYAN ZERNACH

Senior AI Systems Engineer 🇺🇸

Ryan_Zernach_2025_Senior_AI_Systems_Engineer_Remote_United_States

Finequities 📲 Social Investing FinTech AI Startup

I was hired as a full-stack mobile app engineer to build Finequities, an investing platform that combined retail trading, social-network mechanics, and AI-driven portfolio guidance. The product direction was to make investing collaborative and data-informed: users could trade, follow investors, compare performance, and act on recommendation flows in one app.

Primary Sources
iOS App
Android App
Finequities 📲 Social Investing FinTech AI Startup
Finequities NASDAQ launch moment on June 9, 2023.

SOC 2 Compliance Work (Detailed)

At Finequities, I treated SOC 2 readiness as core product engineering work, not a side process for audits. In a social investing app, controls are not abstract: account integrity, transaction trust, production access discipline, and incident response quality all show up directly in user risk and platform credibility. My role was to translate control requirements into real software behavior and operational guardrails across mobile, backend, and release workflows. That meant reducing security ambiguity in high-change areas, making production actions attributable, and tightening system boundaries so control owners could actually prove what was happening in the environment.

  • Access control and least privilege: tightened environment access boundaries, removed broad permissions, and aligned privileged actions to explicit ownership.
  • Change management and release evidence: strengthened release workflow consistency so production changes had traceable paths, clearer reviewers, and repeatable rollback logic.
  • Auditability and observability: improved event visibility across app behaviors and operational paths so investigation and control verification did not depend on guesswork.
  • Secure development baseline: drove TypeScript migration and architecture cleanup that reduced hidden runtime risk and made compliance-sensitive logic easier to reason about.
  • Incident readiness: supported clearer production debugging and escalation pathways so operational incidents could be contained and analyzed with better speed.
  • Data-handling rigor: treated sensitive account and portfolio surfaces with stricter boundary discipline across feature work, integrations, and internal tooling.
  • Third-party integration scrutiny: evaluated external partner touchpoints (brokerage, onboarding, and financial connectors) with trust boundaries and failure isolation in mind.
  • Operational continuity: improved engineering velocity without bypassing control expectations, so the team could ship quickly while preserving compliance posture.

How This Mapped to SOC 2 Criteria

I mapped day-to-day engineering decisions to SOC 2 trust-service expectations so control coverage could be explained in product terms, not just compliance language. That meant defining exactly where each control lived in the delivery lifecycle: design decisions, code paths, deployment controls, runtime monitoring, and incident response. The practical model stayed consistent. Security was implemented through access hardening, role boundaries, and tighter privileged workflows. Availability was reinforced through release discipline, rollback readiness, and clearer operational response loops. Confidentiality was protected through stricter data-scope handling and boundary awareness across features and integrations. Processing integrity was improved by reducing ambiguous execution paths and making critical system behavior easier to verify under change. I also treated evidence quality as part of the work. Controls are only valuable when teams can prove they operated as intended, so I emphasized traceability in both technical implementation and operational process.

  • Security: least-privilege enforcement, production access ownership, and stronger permission boundaries.
  • Availability: change controls, rollback expectations, and better incident containment workflows.
  • Confidentiality: tighter sensitive-data scope management and reduced unnecessary data exposure paths.
  • Processing Integrity: consistent execution behavior, clearer validation surfaces, and stronger audit traceability.

Finequities product demo

What the Product Actually Did

Finequities was not a single-feature trading app. It blended execution, social proof, and personalization in one user flow so discovery and allocation could happen in the same interface.

  • Commission-free stock and ETF trading, with public materials also referencing select cryptocurrency access.
  • Social graph mechanics: investor profiles, following, discussion, and sentiment-oriented community signals.
  • Public portfolio leaderboards ranked by return percentages rather than visible dollar balances.
  • Copy-portfolio workflows designed to let users allocate proportionally to another investor strategy.
  • AI recommendations positioned around risk profile, goals, and portfolio context rather than static watchlists.

My Technical Scope

I worked across product surfaces and platform plumbing. The fastest way to explain my contribution is by ownership zones:

  • Portfolio replication logic: copy-portfolio execution paths and proportional allocation behavior.
  • Discover and ranking surfaces: trending investors, trending assets, and curated stock-group experiences.
  • Social subsystem rebuild: profiles, posts, comments, likes, and followers/following graph behavior.
  • Growth mechanics: referral flow with deep-link routing to major messaging channels.
  • Developer operations and velocity: TypeScript migration from legacy JavaScript, screen-level architecture cleanup, and release workflow improvements (including OTA update practices).

Team and Delivery Context

The core team was small: two full-time developers, one project manager, and one UI/UX designer. With a lean team and broad scope, speed depended on disciplined separation of concerns, rapid handoff loops between design and engineering, and aggressive prioritization of features tied directly to user behavior.

Architecture Signals from Public Evidence

Public artifacts indicate a mobile architecture centered on React Native clients with brokerage, AI, and onboarding partners layered into the stack:

  • Mobile frontend: React Native for iOS and Android.
  • Trading backbone: ViewTrade announced as brokerage/technology partner (July 6, 2023).
  • Conversational onboarding: Bitext Copilot case study references GPT-backed onboarding flows (February 22, 2024).
  • Finance integrations: bank-account linking plus multi-asset portfolio surfaces in production releases.
  • Admin visibility: internal web dashboard work to track function performance and operational health.
Architecture Evidence
ViewTrade Announcement
Bitext Case Study

Maturity and Market Signals

Timeline Compression (2018 to 2026)

Distribution and Monetization Signals

Regulatory Caveat (Material)

As of April 17, 2026, the most material diligence item is regulatory status. A Federal Register notice states SEC intent to cancel registrations of certain advisers and includes Finequities LLC. The listed hearing-request deadline was May 11, 2026. This is a notice of intent, not by itself a final fraud finding, but it is significant and should be checked directly against current IAPD/SEC records before relying on older marketing claims about active registration.

Regulatory Source
Federal Register Notice

Bottom Line

Finequities was a serious full-stack fintech build with credible product depth: multi-asset trading flows, social investing mechanics, recommendation systems, and partner-backed brokerage infrastructure. The strongest public execution window appears in 2021 to 2024, with visible iOS continuity into 2025. The open regulatory question in 2026 is the key gating factor for any present-tense operating claims.