AI Agent Developer for Hire — Custom Agents Without Vendor Lock-In
Production AI agents built in your stack, on your infrastructure. Not demos. Not SaaS dashboards. Code you own, models you can swap.
What I Build
Production AI agents for real use cases — not demos that fall apart in week two.
RAG Pipelines
Retrieval-augmented generation for document Q&A, internal knowledge bases, and customer-facing search. Your documents stay in your storage. Every answer cites its sources.
Autonomous Task Agents
Multi-step agents that plan, execute, and verify. Code review bots. Data pipeline agents with human checkpoints. These are workers, not chatbots.
LLM-Integrated APIs
Custom GPT or Claude endpoints behind your product — users see a feature, not a vendor. Prompt engineering, rate limiting, fallback logic, and structured output validation included.
AI Code Assistants
Internal developer tooling built to know your repo and conventions — PR review automation, documentation generation, codebase search with semantic understanding.
Workflow Automation Agents
Event-driven agents that orchestrate work across tools. Webhook in, AI decision, action out. Built in n8n for speed or custom Python/TypeScript for control.
Tech Stack
Open standards, swappable models, your infrastructure.
Model-agnostic by design. I pick the right LLM per task — GPT-4 for reasoning, Claude for long context, open-source models for cost-sensitive workloads — and build abstractions so you can swap providers without rewriting code.
Why No Vendor Lock-In Matters
You should own your AI stack. Most tools make sure you don't.
Most AI tools sell you convenience and charge you for it forever. OpenAI Assistants API, Microsoft Copilot, the dozens of “AI agent platform” SaaS products — they all share the same trap. Your prompts live in their dashboard. Your data flows through their servers. Your business logic is configured in their UI. The day they raise prices, change a policy, or sunset a product, you have nothing to migrate.
I build AI agents the same way I build any other software. Your code lives in your repo. Your prompts are checked into git. Your vector store runs on your infrastructure. The LLM is a swappable dependency — switch from GPT-4 to Claude to a self-hosted Llama with a config change, not a rewrite.
You own the system. I just helped you build it.
Why Custom AI Agents?
Three reasons custom beats off-the-shelf every time
Tailored to Your Business Logic
Off-the-shelf AI tools are generic. Custom agents understand your domain, your data model, and your workflows. They do exactly what you need — nothing more, nothing less.
Data Privacy & Control
Your data stays yours. Custom agents run in your infrastructure, use your API keys, and never train third-party models on your proprietary information.
Cost Control at Scale
SaaS AI tools charge per seat and add up fast. Custom agents let you optimize token usage, choose the right model per task, and scale without runaway costs.
Who This Is For
Three kinds of teams that get the most value.
Startup CTOs who need AI features without hiring an ML team
You're shipping a product. You want AI features in it. You don't have headcount for a dedicated ML engineer and you don't want to spend nine months on infrastructure before shipping a single feature. I plug into your existing team and ship the AI parts at senior level.
Product teams with repetitive internal workflows
Your team spends hours per week on pattern-matchable work: triaging tickets, summarising meeting notes, categorising inbound, drafting first-pass content. These are exactly the workflows AI agents are good at. I build it, integrate it with your tools, and hand it back maintainable.
Companies with large knowledge bases that need Q&A interfaces
You have years of documents, wikis, Slack threads, or support tickets and search inside them is broken. A RAG pipeline grounds answers in your data and cites sources. Probably my favourite kind of project.
How It Works
A clear path from idea to deployed agent.
Discovery Call
Free 30-minute call. We talk about what you want, what is actually feasible, and what is not worth building yet.
Scope & Architecture
I write up the scope: which agent, which model, which integrations, what success looks like, fixed price or hourly estimate.
Build & Iterate
Weekly check-ins. Working demos early in your environment, not on a staging server I control. Prompt engineering with your real data.
Deploy & Handover
Deployed to your infrastructure with documentation, runbooks, and a handover session. No lock-in to me either — that is the point.
FAQ
The questions I get on every first call.
Ready to Build?
Book a free 30-minute call to talk through your project. I'll tell you honestly what's feasible, what model fits, and whether AI is even the right answer for what you're trying to do.