AI Engineering
I build practical AI systems — LLM applications, RAG, agents, document intelligence and automation — wired into business data, tools and workflows, and deployed like any other production system.
01Not a chatbot on a website
I build practical AI applications — systems where a model is one component inside a real product, wired to business data, tools and workflows — rather than adding a chatbot to a website and calling it AI.
A capable model with no retrieval, no tools and no validation is a demo. The application layer — context, guardrails, evaluation, cost control — is where reliability comes from.
Answers and actions should come from your documents, records and systems, with sources attached, not from whatever the model remembers from the internet.
When output feeds code, it is schema-constrained and validated. Free text is for humans; JSON is for systems.
Automation that can send, spend or delete gets an approval step. The goal is trust, not just autonomy.
02Architecture
From the model outward: each layer is engineering work, and each is where projects succeed or fail.
AI model
Choosing hosted or open models per task, with provider abstraction so the choice can change.
Application layer
Prompt and context management, output validation, evaluation sets, logging and cost controls.
Business data
Ingestion pipelines, chunking, embeddings, vector and keyword indexes, access control on retrieval.
Tools
Well-typed functions the model can call — your APIs and internal services, with limits and audit logs.
Workflows
Queues, retries, approvals and human-in-the-loop steps that make automation trustworthy.
User interface
Interfaces that show sources, allow correction and fit how people already work.
Deployment
Containers, CI/CD, secrets management, monitoring of latency, quality and spend.
03Capabilities
Applications built around modern language models.
Prompt design, context management, evaluation and cost control for features that run on language models in production.
Knowledge bases, document search and semantic retrieval.
Chunking, embeddings, hybrid retrieval and reranking so answers are grounded in your own documents and data.
Tool calling, APIs, workflows and multi-step tasks.
Agents that call your systems through well-defined tools, with guardrails, retries and human checkpoints where they matter.
AI-assisted business processes and repetitive workflow automation.
Classification, routing, drafting and enrichment steps embedded inside existing queues, CRMs and back-office flows.
Extraction, classification, summarization and document question answering.
Turning PDFs, forms, emails and scans into structured data your systems can validate and act on.
Semantic search and intelligent knowledge retrieval.
Search that understands intent across products, records and documentation — combined with keyword search where precision matters.
Reliable JSON outputs, classification, extraction and workflow integration.
Schema-constrained outputs, validation and fallbacks so model responses can be trusted by downstream code.
Connecting models to business data, applications, APIs and internal systems.
Model providers, vector stores and internal APIs integrated into applications with observability and access control.
04Topics
The kinds of systems I design and build — each one a different engineering problem, not a different prompt.
Products and features where a language model does real work — drafting, answering, transforming — with the engineering around it to make that reliable.
Retrieval-augmented generation over your own documents and records: ingestion pipelines, chunking strategy, embeddings, hybrid search and citation.
Search that understands meaning rather than exact words, layered on top of keyword search so precision is not lost.
Multi-step systems that plan, call tools and complete tasks against your APIs, with limits, logging and human approval where risk is high.
AI steps embedded in existing business processes: triage, classification, extraction, enrichment and routing.
Extracting structured, validated data from PDFs, forms, invoices, emails and scanned documents.
Internal knowledge bases that staff and customers can actually query, with access control and source attribution.
Assistants grounded in your data and connected to your systems — able to look things up and take actions, not just chat.
Generation pipelines with templates, review steps and structured inputs so output stays on-brand and accurate.
Schema-constrained outputs (JSON, enums, typed fields) that downstream code can trust and validate.
Connecting hosted and open models into existing applications with provider abstraction, caching, cost and latency control.
End-to-end flows that combine models, rules, data and people — designed around how the business operates.
Writing
You do not need a rewrite to add AI to a Laravel or Next.js application. A step-by-step approach: find the right feature, isolate the model behind a service, validate outputs, handle cost and failure, and ship behind a flag.
Underneath the hype, an AI agent is a loop: a model chooses a tool, the tool runs, the result goes back in. Here is that loop in detail, the engineering that makes it safe, and how to decide whether you need an agent at all.
Retrieval-augmented generation explained from the engineering side: ingestion, chunking, embeddings, hybrid search, reranking, permissions and citations — and the decisions that determine whether a knowledge base actually answers questions correctly.
From retrieval and agents to document intelligence, I build AI systems that plug into real business data and workflows.