Hands-on CTO at Livable Places

At Livable Places I am not working on a generic "AI-first" story. I am working on a real delivery system for a proptech product with actual operational responsibility. That spans monorepo product development, distributed scoring and data workflows, self-hosted platform operations, geo/census services, and the evidence-led preparation of a new office-risk product line.

The product thesis is bigger than a score dashboard: make societal demand for real estate uses measurable and comparable at location level. The operating logic is Demand -> Supply -> Property, so financing, acquisition, ESG, and portfolio decisions can rely on more legible signals.

My leverage is connecting product logic, data pipelines, AI/ML research, geo services, queue orchestration, and platform operations instead of treating them as separate workstreams. The monorepo, workers, auth, observability, GitOps-style deploys, backups, and runbooks form one system that can be built, shipped, and operated.

The AI/ML work stays deliberately evidence-bound. Office-risk fingerprinting, PCA, k-means, Location Absorption Score, benchmarking, and feature ablation are used for characterization and screening until target quality, residualization, and cross-city stability can support stronger claims.

• Built and evolved a pnpm/Turborepo monorepo for the web app, services, workers, and operating tools
• Worked hands-on on distributed scoring and data workflows with Bun, BullMQ, FlowProducer, QueueEvents, and Redis
• Used Redis for locking, Pub/Sub, and job-completion signaling instead of fragile glue logic
• Built authenticated async report export paths with service-side auth guards, shared export domain logic, and XLSX generation
• Owned a self-hosted bare-metal platform with Docker Swarm, Traefik, Authentik, Ansible bootstrap, Docker Secrets, restic backups, WireGuard, and a full observability stack
• Established IaC/GitOps-style GitHub workflow paths for build, release, deployment, rollback, scaling, backup, restore smoke tests, maintenance, and self-healing
• Shaped the geo/census service layer around Kotlin, Spring Boot, PostGIS, OpenAPI, FastAPI, Valhalla, OSM, and external POI imports
• Prepared a new office-risk product line through evidence-led product and delivery work
• Designed and challenged ML workflows around PCA, k-means clustering, LAS calibration, benchmark matrices, target quality, residualization, and feature ablation
• Used multi-agent research workflows, local agent skills, and plugin-style skill development as part of the product evidence layer, not only as developer convenience
• Translated strategy into visible goals, scopes, work items, development boards, releases, demos, and customer-facing feedback loops
• Built demo and campaign measurement paths with anonymous access, UTM tracking, event analytics, and conversion-funnel thinking
• Built internal planning and reporting tooling with Next.js, React, SQLite, Zod, and localized product surfaces

• Delivery as a system: monorepo, platform, queues, geo services, and product work are not optimized in isolation
• Decision quality over dashboards: product work is judged by whether it helps customers make better financing, acquisition, ESG, or portfolio decisions
• Infrastructure as code: platform state, auth baselines, stack definitions, secrets, backups, and operations are encoded and repeatable
• Operability by design: logs, metrics, traces, alerts, and auth are part of the product, not follow-up work
• Distributed orchestration with explicit dependencies, locking, and visibility instead of silent background jobs
• AI-first where it matters: use agents, LLM tooling, and research automation to improve evidence quality and delivery speed, but keep claims bounded by data
• ML with evidence gates: fingerprint, benchmark, calibrate, residualize, ablate, and only then claim predictive value
• Geo/data products as infrastructure: OSM, census, POI, routing, and market data are treated as versioned, observable product dependencies
• Visible operating model: strategy, roadmap, goals, scope, work items, board, release, demo, and customer feedback stay connected
• Small batches and visible ownership instead of roadmap and process signaling
• Evidence-first product work: observation, hypothesis, experiment, then scale