01 Cloud, DevOps and CI/CD cost
Find what your platform reserves but doesn't use, and cut it without slowing anything down.
- Right-sizing Kubernetes services, GPUs and autoscaling, with scale-to-zero for batch work
- Smaller images and faster builds
- CI that only builds what changed: merge to deploy, tag to release
- Code-level fixes for the hot paths that drive the bill
Ask about this → 02 RAG and AI search quality
Make AI answers more accurate and cheaper per question, and prove it on your own data.
- Document extraction for PDFs, tables and scans, tiered by speed and cost
- Chunking, embeddings, hybrid retrieval and score fusion
- Choosing a vector store and migrating to it, from pgvector to LanceDB or Turbopuffer
- Evaluation sets, cost-per-answer metering and the right model for each role
Ask about this → 03 Frontends built with AI that still scale
Use AI coding tools to ship UI fast, without ending up with a codebase nobody can change.
- A design system and component rules that AI agents follow
- Next.js and React structure: routing, data, sign-in and SEO
- Review rules and tests that catch what generated code gets wrong
- Websites rebuilt for search and AI answer engines
Ask about this → 04 A codebase structured for the AI era
Set up your repository so AI agents and people can both work in it quickly and safely.
- A monorepo with shared libraries and one release process
- Rules, skills and docs that agents read, such as CLAUDE.md and AGENTS.md
- Tests and CI as guardrails for AI-written changes
- Design docs and review habits that let a small team ship like a big one
Ask about this → 05 Security reviews and hardening
Close penetration-test findings fast and get ready for enterprise security reviews.
- Fixing VAPT and pentest findings
- Sessions, tokens and logout done properly
- Framework and dependency upgrades
- Access checks between tenants
Ask about this → Something else?
If it involves AI in production, cloud cost or how a team ships, tell me about it. If I'm not the right person, I'll say so.
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