Services
Four lanes. Each sells an outcome, states what it includes, and links to proof.
01 · backend-platform
Backend / Platform Contracts
Can he build or stabilize the core system?
Backend and platform work is my core craft: Go and Python services, PostgreSQL and ClickHouse data layers, Kafka event streaming, Redis caching and queues. I take systems from idea to production and keep them reliable as they grow.
Includes
- APIs
- Integrations
- Internal platforms
- Backend systems
- Reliability and operational quality
- Architecture and technical direction
- Delivery systems
Outcomes
- A core system you can ship features on without fear
- Integrations that survive partner API changes
- Analytical and reporting workloads that stay fast as data grows
- Documented architecture decisions the team can build on
Good fit when
- You need a backend or platform built, extended, or stabilized
- Your data and integrations have outgrown the original design
- You need senior technical direction, not just more hands
02 · ai-automation
AI Automation Systems
Can he turn our repetitive work into an AI-assisted workflow?
I build AI automation as an engineering discipline: clear data flow, retries, observability, human checkpoints where stakes are high. The goal is leverage you can trust, not a fragile demo.
Includes
- Agent workflows
- LLM integrations
- Document and data pipelines
- Operations automation
- Internal AI tools
- Human-in-the-loop workflows
Outcomes
- Repetitive operations turned into supervised automated workflows
- LLM features integrated with proper engineering controls
- Pipelines that process documents and data without manual steps
- Internal tools your team actually uses
Good fit when
- Your team spends hours on work a supervised agent could do
- You want LLM features built with engineering discipline, not demos
- You need automation that humans can inspect and override
03 · ai-dev-culture
AI Development Culture
Can he help our team use AI like serious engineers?
I work AI-native every day: custom agent plugins, skills, hooks, token-optimized tooling, and structured verification. I help teams adopt the same discipline — AI as a multiplier on engineering judgment, never a replacement for it.
Includes
- Development plugins
- Codex and AI-agent workflows
- Review rituals
- Prompt systems
- Team practices
- AI-assisted implementation systems
Outcomes
- Developers who get real leverage from AI tools, with quality intact
- Plugins, skills, and hooks tailored to your codebase and process
- Review rituals that catch AI-generated mistakes before production
- A shared prompt and context system instead of individual hacks
Good fit when
- Your team adopted AI tools but output quality is inconsistent
- You want engineering-grade AI workflows, not ad-hoc prompting
- You need someone who runs this workflow daily, not a slide deck
04 · quality-knowledge
Quality + Knowledge Systems
Can he make our engineering work more controlled and reusable?
Controlled engineering scales; heroics do not. I set up the loops — tests, reviews, QA gates, evaluations — and the knowledge layer — AI-readable docs, context architecture, decision records — that make a team’s work compound instead of evaporate.
Includes
- QA gates
- Test and review loops
- Evaluation systems
- AI-readable documentation
- Memory and context architecture
- Team knowledge bases
Outcomes
- Quality gates that stop regressions before they ship
- Documentation that both people and AI tools can actually use
- Knowledge bases that survive team changes
- Evaluation loops for AI features so quality is measured, not assumed
Good fit when
- Process is scattered and quality depends on individual heroics
- Documentation is stale, tribal, or unreadable for AI tools
- You want decisions and context captured once and reused