Service
AI Products & Agents
Production AI agents, copilots, RAG systems, and workflow automation built for reliability, security, and real-world use.
The problem
Building an AI prototype is increasingly easy. A model can answer questions, call tools, search documents, and automate a workflow after only a small amount of initial development.
Turning that prototype into a dependable product is harder.
Real users bring incomplete requests, unexpected data, changing requirements, and edge cases that do not appear in a controlled demonstration. Models can return incorrect answers, retrieve weak evidence, misuse tools, or behave differently as prompts, data, and dependencies evolve.
The difficult work is therefore not adding an AI API. It is building the software around the model: interfaces, retrieval, permissions, validation, observability, testing, deployment, and human control.
Common patterns:
- A successful demo has no clear path to a maintainable product
- AI behaviour is buried inside prompts and difficult to test
- Retrieval returns plausible context without proving that it is authoritative
- Agents receive broad tool access before permission boundaries are designed
- Users cannot inspect sources, decisions, or actions taken by the system
- Model, application, and infrastructure concerns are tightly coupled
- Logging captures technical failures but not meaningful AI behaviour
- Teams keep refining prompts while the surrounding product remains incomplete
The result is often a feature that looks impressive in a presentation but remains too fragile, opaque, or expensive for regular use.
Who this is for
Founders and engineering teams that want to build an AI-enabled product, move an existing prototype towards production, or add AI capabilities to a working platform.
- Startups building their first AI product or agent
- Product teams adding copilots or intelligent workflows to an existing application
- Teams building RAG systems over specialist or internal data
- Companies automating repeatable work with AI agents and tools
- Engineering teams replacing a prototype with a maintainable architecture
- Organisations that need stronger security, observability, and control around an existing AI system
This is not a generic AI strategy engagement or a collection of disconnected prompt experiments.
It is hands-on product and software engineering for AI systems that people are expected to use.
Engineer in the loop
I use AI throughout the development process, but I do not treat generated code or model output as inherently correct.
AI can accelerate exploration, implementation, testing, and analysis. Engineering discipline is what turns that speed into a system that remains understandable and maintainable.
Important decisions about product behaviour, system boundaries, data, permissions, and failure handling remain explicit and reviewable.
In practice, this means:
- Defining product behaviour and acceptance criteria before generating large amounts of code
- Keeping model behaviour behind clear application and domain boundaries
- Testing deterministic software separately from probabilistic AI behaviour
- Reviewing tool use, permissions, data access, and generated actions explicitly
- Using logs, evaluations, and user feedback to improve the complete system rather than only the prompt
The goal is not to remove engineers from the process. It is to use AI to build better software without losing control of how that software works.
Scope
AI agents
Tool-using agents, permission gates, human approval, session state, structured actions, audit trails, and failure handling.
Copilots & assistants
Context-aware product assistants, domain-specific chat, structured outputs, guided workflows, citations, and user-facing controls.
RAG & knowledge systems
Document ingestion, chunking, embeddings, hybrid retrieval, reranking, metadata, knowledge graphs, citations, and grounded responses.
Workflow automation
AI-assisted business processes, deterministic workflow steps, tool integration, background jobs, approvals, retries, and escalation.
Product & application layer
Web interfaces, APIs, authentication, user sessions, feedback flows, result inspection, and integration with existing products.
Security & reliability
Identity, secrets, access boundaries, prompt-injection controls, observability, evaluation, testing, deployment, and operational safeguards.
Cloud deployment
AWS architecture, containers, serverless components, storage, networking, monitoring, CI/CD, and environment design.
Existing system improvement
Architecture review, prototype hardening, retrieval improvement, cost reduction, workflow redesign, and production-readiness work.
What you receive
The exact output depends on whether the engagement starts with an idea, a prototype, or an existing product. It can include:
Working product
A functional AI application, agent, copilot, retrieval system, or automated workflow built around the agreed scope.
System architecture
Clear boundaries between the product, model, retrieval layer, tools, data, infrastructure, and external services.
Production codebase
Structured, version-controlled application code designed for continued development rather than a disposable demonstration.
Retrieval pipeline
Ingestion, indexing, retrieval, ranking, grounding, citations, and evaluation for systems that use private or specialist knowledge.
Agent controls
Tool definitions, permission boundaries, approval flows, execution constraints, audit events, and safe failure behaviour.
Testing & evaluation
Automated application tests, model-behaviour checks, retrieval evaluation, representative scenarios, and regression coverage.
Deployment & observability
Cloud deployment, CI/CD, structured logging, usage visibility, failure monitoring, and operational documentation.
Technical handover
Architecture notes, setup instructions, design decisions, operating guidance, and a clear path for continued development.
Work can also begin with a smaller, focused engagement:
- Product discovery and technical feasibility
- Architecture and prototype review
- A focused proof of concept for one high-risk capability
- Retrieval or agent-quality evaluation
- Security and production-readiness hardening
The objective is a useful product with a clear architecture and a credible path forward—not a prototype that has to be discarded once real development begins.
Background
I am a software engineer and architect with more than 20 years of experience building web applications, cloud platforms, developer tools, security systems, and distributed software.
My recent work includes AI agents, retrieval systems, knowledge graphs, multimodal search, AI-assisted engineering workflows, and secure cloud applications on AWS.
I also build open-source AI projects to test these ideas against real code, tools, data, interfaces, and operational constraints. This keeps the work grounded in implementation rather than abstract recommendations.
Have an AI product to build or a prototype that needs to become real software?
Tell me what you are building, where the difficult parts are, and what already exists. I will reply directly and suggest a practical way to approach the work.