AI & Automation
AI Development Services
AI development, in practice, mostly means integrating an existing large language model API (OpenAI, Anthropic) into an application's actual workflow — not training models from scratch. The engineering work is in prompt design, retrieval of the right context, handling failures gracefully, and wiring the output into something the rest of the app can use reliably.
I build these as regular application features — a chatbot, a document processor, a lead-qualification step — backed by an LLM API, rather than standalone AI demos that don't connect to anything.
Problems This Solves
- arrow_rightRepetitive tasks — summarizing, categorizing, drafting responses — that currently take staff time and follow a fairly consistent pattern
- arrow_rightCustomer-facing content or support that needs to scale without linearly scaling headcount
- arrow_rightUnstructured data (documents, emails, form submissions) that needs to be turned into structured, usable information
- arrow_rightWanting AI features but not knowing where the actual engineering complexity is — usually not the model, but the integration around it
What's Included
- checkLLM API integration (OpenAI, Anthropic, or others depending on the use case)
- checkPrompt design and testing for the specific task
- checkRetrieval-augmented generation for answers grounded in your own data
- checkStructured output parsing so AI responses feed cleanly into the rest of the application
- checkFallback handling for when the model output isn't usable
- checkCost monitoring, since LLM API usage is billed per token
How I Work
- Define the specific task precisely — vague 'add AI' requests get scoped down to a concrete input/output problem
- Prototype with a small test set to validate the approach works before building the full integration
- Build the integration with proper error handling and fallback behavior
- Add monitoring for cost and output quality once it's live
- Iterate on prompts based on real usage, not just initial testing
Technology Stack
- arrow_rightOpenAI API / Anthropic API
- arrow_rightNode.js
- arrow_rightVector databases for retrieval
- arrow_rightTypeScript for structured output typing
Frequently Asked Questions
Do you train custom AI models?add
No — for the vast majority of business applications, integrating an existing large language model via API and grounding it with your own data (retrieval-augmented generation) gets better results faster and more reliably than training a model from scratch, which requires far more data and infrastructure than most projects can justify.
How much does an AI feature cost to run?add
LLM APIs bill per token processed, so cost scales with usage volume and how much context each request sends. I set up monitoring so the actual cost is visible from early on, rather than discovered as a surprise bill.
What happens when the AI gives a wrong answer?add
This gets designed for explicitly — confidence thresholds, fallback to a human or a default response, and grounding answers in your actual data (RAG) rather than the model's general training, which reduces but doesn't eliminate incorrect outputs.
Is my data used to train the AI model?add
That depends on the provider's terms and the API tier used — most enterprise API tiers from major providers explicitly exclude your data from training. This is worth confirming against the specific provider's current policy before sending sensitive data.