David Wolf · Project Use Case
AI SECURITY · PRODUCT SECURITY · INTERNAL PRODUCT
Internal Product
Rust/WASM Supabase AI Security Engine Platform
A WASM-first AI/security backend architecture using Rust engines, Supabase Edge Functions, PostgREST, canonical schemas, scoring engines, extraction...
Designed and implemented a Rust/WASM-first backend architecture for AI security, job intelligence, fit scoring, schema extraction, prioritization, and workflow automation using reusable Rust engines compiled to WASM, Supabase...
Client
Internal Product / Confidential Platform
Engagement Type
Internal product buildout
Period
2025–2026
Role
Rust/WASM Systems Architect / AI Product Security Engineer / Platform Architect
Focus Areas
Rust/WASM Engines, Supabase Edge Functions, PostgREST APIs, Canonical Schemas
The Research Narrative
Strategic Problem
The challenge was creating reusable logic that could run across browser, edge, native, and backend environments while staying testable, typed, and governable. LLM output needed validation and normalization...
What David Did
David designed Rust engines compiled to WASM, JSON function interfaces, Supabase Edge Function loaders, canonical schemas, PostgREST views, RPC endpoints, and event envelopes so scoring...
What Became Clearer
The result is a reusable AI platform foundation for job intelligence, fit scoring, recruiting intelligence, AI security analytics, workflow automation, and governance evidence. It...
Consulting Proof
This is evidence of turning messy security telemetry into explainable dashboards, alert-quality improvements, and executive-ready operating views.
The Context
AI platforms become fragile when every feature hides logic inside prompts or duplicates code across frontend, backend, extension, and desktop surfaces. This platform uses Rust/WASM engines as the deterministic core beneath AI workflows.
The Challenge
The challenge was creating reusable logic that could run across browser, edge, native, and backend environments while staying testable, typed, and governable. LLM output needed validation and normalization instead of blind trust.
What I Did
David designed Rust engines compiled to WASM, JSON function interfaces, Supabase Edge Function loaders, canonical schemas, PostgREST views, RPC endpoints, and event envelopes so scoring and extraction logic could be shared across the platform.
- •Designed a Rust/WASM-first engine strategy for reusable scoring, extraction, normalization, matching, recommendation, prioritization, and decision logic
- •Built Rust engines with flat module patterns and JSON twin functions so they could be called consistently from JavaScript, TypeScript, browser workers, Supabase Edge Functions, and native contexts
- •Compiled selected Rust crates to WASM for deterministic execution in Deno/Supabase Edge Functions and browser-compatible runtimes
- •Used static dictionaries and import.meta.url-safe WASM loaders to make Supabase Edge Functions more reliable across deployment contexts
- •Separated LLM prompting from deterministic engine logic so AI outputs could be validated, scored, enriched, and normalized through typed functions
- •Designed canonical schemas for jobs, profiles, skills, applications, fit scores, recommendations, events, and evidence records
- •Created PostgREST-friendly database views and RPC patterns, including flattened LLM-friendly views for job dossiers and server-side search/ranking
- •Used Supabase Edge Functions as an orchestration layer around WASM engines, database queries, enrichment flows, and AI-assisted processing
The Outcome
The result is a reusable AI platform foundation for job intelligence, fit scoring, recruiting intelligence, AI security analytics, workflow automation, and governance evidence. It demonstrates practical Rust/WASM engineering applied directly to AI product-security architecture.
Research Outcomes
Signal Quality
Improved the trustworthiness of operational security signals
Operational Clarity
Translated complex security data into clearer operating views
Stakeholder Visibility
Made technical risk and status easier to explain
Operational Impact
Turned raw telemetry into actionable security intelligence
Capabilities Demonstrated
Security Analytics
Signal investigation and event analysis
Telemetry Normalization
Consistent and trusted data
Operational Reporting
Actionable views for security operations
Public-Safe Evidence
Shareable insights without sensitive data
IAM / Access Control
Identity telemetry and access insights
SIEM Alert Debugging
Noise reduction and signal validation
Dashboard Development
Operational and executive views
Executive Reporting
Security data translated for leadership
Key Deliverables
- •Rust/WASM engine architecture
- •Reusable scoring and extraction engine patterns
- •WASM loaders for Supabase Edge Functions
- •Static dictionary loading strategy
- •JSON twin function interface pattern
- •Canonical schema strategy
- •PostgREST API and RPC patterns
- •LLM-friendly database view design
Tools & Technologies
Consulting Translation
The reusable pattern is not Disney-specific: normalize fragmented security telemetry, debug low-signal alert behavior, build trusted operating views, and give leadership evidence they can act on without exposing sensitive systems.