Founder & Chief Architect, FoundLab

Building deterministic, cryptographically-verifiable infrastructure for regulated AI and financial systems.

Trust by physics, not by policy.

Auditable Trust Infrastructure — systems where sensitive payloads disappear, but cryptographic evidence survives.

This means:

• ✅ Evidence-first architecture (regulatory posture should be evidenced, not asserted)
• ✅ Deterministic engines that can be audited by math, not auditors
• ✅ Zero-persistence processing (data never stored = LGPD by design)
• ✅ Event-driven, serverless-first, horizontally auditable
• ✅ Merkle chains, hash-based evidence, DecisionID lineage
• ✅ Real-time observability (OpenTelemetry, not black boxes)

Reference architecture for verifiable secrecy systems (Shannon 1949).

Public design notes separating:

• Secret handling — ephemeral, zero-persistence processing boundaries
• Policy enforcement — deterministic decision gates, fail-closed under ambiguity
• Cryptographic evidence — Merkle chains, hash-based audit receipts, ECDSA signatures
• Non-sensitive auditability — OpenTelemetry tracing and analytical audit views

Includes Architectural Decision Records (ADRs), threat model documentation, and Python verifier examples for independent auditor validation.

→ Read secrecy-architecture

Deterministic consistency validation for investment data (Go).

Converts subjective declarations (cap tables, traction claims, corporate history) into reproducible consistency signals. Produces signals, not opinions. Proof-of-check artifacts.

• Deterministic TrustScore
• Inconsistency detection
• Audit-grade output with verifiable proofs

Cryptographic evidence substrate for financial decisions.

Hash chaining, DecisionID assignment, Merkle proofs, policy snapshots. Whitepaper with reproducible schema and audit validation guide.

• Canonical whitepaper (Markdown → PDF)
• Cryptographic proof levels and regulatory mapping
• Reproducible audit methodology

Cloud-native infrastructure scaffold for modular AI governance.

GCP infrastructure-as-code: Cloud Run services, BigQuery analytical audit views, Vertex AI orchestration, CloudBuild CI/CD.

• Modular service architecture
• Terraform patterns for governance
• React frontend scaffold, API-first design

Institutional blueprint with claims, KPIs, and reproducible evidence.

Evidence-first documentation, technical claims mapped to observable KPIs, public benchmark structure, audit manifests.

• JSON Schema for verifiable claims
• Proof-of-measurement artifacts
• GitHub Pages documentation

• Go — Deterministic engines (Spezzatura, high-performance validation)
• Python — Data validation, ML ops, compliance pipelines
• TypeScript — Frontend, full-stack applications with React

• Google Cloud — Primary platform (Cloud Run, Vertex AI, Pub/Sub, KMS)
• Storage — Cloud Storage Bucket Lock for immutable evidence packages
• BigQuery — Analytical audit view (not root immutable ledger)
• Cloud Spanner — Chain head and strong consistency
• Terraform — Infrastructure-as-code, reproducible environments
• Docker — Containerized, immutable builds
• CloudBuild — CI/CD automation

• Pub/Sub — Event-driven processing
• OpenTelemetry — Full distributed tracing, SLO/SLA metrics
• PostgreSQL / MongoDB — Transactional and operational databases

• SHA-256 — Hash chains for cryptographic evidence
• ECDSA P-256 — Signatures for signed logs and DecisionID
• Zero-persistence design — Ephemeral containers, forced TTL, no data residue
• Cloud KMS — Key management and cryptographic operations

• Vertex AI — ML pipelines, drift guards, continuous learning
• Gemini — LLM integration with runtime governance
• Explainability-by-design — Models that produce interpretable decisions

• Evidence over narrative — Show proof, not promises
• Determinism as auditability — Every decision must be reproducible
• Sensitive payloads disappear; cryptographic evidence survives — Zero-persistence is architecturally enforced
• Fail-closed under ambiguity — Uncertain paths don't execute; they escalate
• Compliance evidence is produced by architecture — Regulatory posture should be evidenced, not asserted
• Audit is continuous, not forensic — Every transaction produces an artifact
• Secrets don't persist by default — Data minimization through ephemeral processing

Ex-lawyer (Brazil Bar 53.705/SC) — specialized in market integrity, compliance automation, regulated fintech.

Published:

• "Insider Trading: Crime de informação privilegiada" — cited as legal precedent in Buenos Aires jurisprudence
• Featured: "O Arquiteto Stealth: Como um Ex-Advogado Está Construindo a Próxima Infraestrutura 'Auditável' do Mercado Financeiro" (2025)

Affiliations:

• Member, CQF Institute Societies New York
• Member, Google Cloud Innovators
• Member, NVIDIA Developer Program
• Active in quantum finance, market infrastructure, explainable AI

• Auditable AI infrastructure — organizations building trust infrastructure for regulated workloads
• Cryptographic evidence systems — teams exploring hash-based audit and proof mechanisms
• Zero-persistence architecture — regulated fintech solving LGPD/GDPR by design, not encryption
• Deterministic scoring engines — investment, credit, or compliance-grade validation systems

Infrastructure     → GCP (Cloud Run, Vertex AI, Pub/Sub, KMS), Terraform, Docker
Evidence Storage   → Cloud Storage Bucket Lock (immutable), BigQuery (analytical view)
Core Engines       → Go (deterministic), Python (ML ops), TypeScript (full-stack)
Security           → SHA-256, ECDSA P-256, zero-persistence, Merkle chains
Observability      → OpenTelemetry, audit-first logging, policy-as-code
AI/Governance      → Vertex AI, Gemini, explainability-by-design

• Website: foundlab.com.br
• LinkedIn: alex-bolson-941b1925a
• Google Developer: g.dev/alexbolson
• Research: foundlab-research
• Architecture: secrecy-architecture

FoundLab — Sensitive Payloads Disappear. Cryptographic Evidence Survives.