CEO | Tech Innovator | Cloud & SaaS Expert
Founder & CEO of Starlight Retail Inc.
Designing intelligent, cloud-native automation for modern commerce.
🌐 Website • LinkedIn • ORCID • Forbes Councils • Microsoft Learn • Google Cloud Skills
Engineer resilient, low-friction data + AI platforms that compress operational overhead, accelerate decision velocity, and unlock scalable leverage for retail and commerce ecosystems.
Algorithmic leverage + adaptive cloud architecture + pragmatic automation.
Python · TypeScript · FastAPI · Node.js · PostgreSQL · Redis · Vector DB (pgvector / Pinecone) · Cloudflare Workers · Docker / Kubernetes · Terraform
| Domain | Focus |
|---|---|
| SaaS / Multi-Tenancy | Isolation boundaries, tenant metadata, usage metering |
| Event & Streaming | Async pipelines, idempotent processors, dead-letter strategy |
| AI Assistants | Tool/function orchestration, retrieval pipelines, structured action loops |
| Edge Execution | Latency-sensitive routing, auth at edge, rules engines |
| Data Systems | Quality gating, observability, lineage, incremental ingestion |
| Platform Ops | CI/CD provenance, cost-to-feature ratio, security posture |
Problem: Manual meeting artifact handling (agenda → decisions → actionables) created operational drag.
Solution: AI-driven ingestion and normalization: semantic chunking, retrieval, and action extraction with function-calling agents.
Architecture: Ingestion (webhook/upload) → Parsing (LLM + heuristics) → Vector + relational persistence → Summarization & action synthesis → API / dashboard delivery.
Stack: Python, FastAPI, Vector DB (pgvector/Pinecone), Cloudflare Workers, OpenAI (function calling), PostgreSQL.
Impact (public): 1,500+ active beta users; meeting processing reduced from ~45 minutes to <5 minutes; action extraction precision ~82%; pilots across 8 countries / 12 branches.
Next: Real-time streaming summarization and deeper integrations with task systems (Jira / Linear).
Problem: Slow patent landscape scanning for product and IP strategy.
Solution: Hybrid semantic + keyword retrieval and clustering to surface high-signal candidates.
Architecture: Query normalizer → multi-index (keyword + embedding) → scoring & clustering → summaries + export.
Stack: Python, embeddings (OpenAI / SentenceTransformers), pgvector / Pinecone, TypeScript dashboard.
Impact (public): Research cycle reduced ~60%; 200+ high-relevance candidates surfaced in pilots; filing prep accelerated ~15 days.
Problem: Fragmented internal automation and slow prototype cycles for ML-assisted tasks.
Solution: Modular assistant framework (tool registry, context assembly, guardrails, memory adapters).
Architecture: Orchestrator → context builders → tool invocation (function calling) → output validation → delivery (CLI/API).
Stack: Python, TypeScript (CLI/UI), Redis (ephemeral memory), Vector store, GitHub Actions.
Impact (public): Prototype build time cut ~40%; 25+ routine workflows automated; ~120 ops hours saved/month.
Roadmap: Multi-agent negotiation, cost-budgeting layer, offline evaluation harness.
- ARR target: $1M by end of 2025; $10M by 2029.
- Infra cost savings: ~35% per request via edge/serverless routing.
- Latency: p95 response times <300ms for edge-triggered flows.
- Global footprint: 12 branches across 8 countries.
- Engagement lift: AI features improved pilot retention by ~20%.
For brevity on public pages: 15+ multi-cloud certifications across AWS, Google Cloud, Microsoft, and IBM.
Selected highlights (public):
- AWS Fundamentals: Building Serverless Applications
- Getting Started with AWS Machine Learning
- Digital Transformation with Google Cloud
- Introduction to Responsible AI; Responsible AI: Applying AI Principles (Google Cloud)
- Leverage AI Tools and Resources for Your Business (Microsoft)
- Cloud Pak for Business Automation — IBM
(Expand a full list in a separate CV or About page if required.)
- Models: OpenAI GPT‑4o (via Azure OpenAI & OpenAI API), Claude for evaluations, local LLM experiments.
- Frameworks: LangChain, LlamaIndex, custom retrieval pipelines.
- Embeddings: OpenAI, Cohere, SentenceTransformers.
- Practices: hybrid retrieval (dense + keyword), function-calling orchestration with schema validation, embedding lifecycle/versioning, guardrails and regression evaluation harness.
Short: Publish MLBot core modules; expand MeeTaker AI summarization accuracy; release Android client.
Mid: Vector governance toolkit; AI evaluation harness; open-source edge auth middleware.
Long: Partner integration ecosystem; standardized agent policy layer; whitepaper on hybrid retrieval in retail.
- Deterministic surfaces around probabilistic cores.
- Observability as first-class (telemetry everywhere).
- Design with cost & latency as constraints.
- Automate repeatable cognition; preserve scarce creative cycles.
Los Angeles-based founder building intelligent commerce automation. Outside work I enjoy strategy games and exploring urban architecture — both sharpen systems thinking.
Email: [email protected]
LinkedIn: https://www.linkedin.com/in/mammonbaloch