Which models do you use — Claude or GPT?

Both. I use the Claude (Anthropic) and OpenAI APIs and pick per task — often Claude for long-context reasoning and writing — and keep the code model-agnostic so you can switch.

How do you stop the AI from hallucinating?

Retrieval-augmented generation: the model is grounded in your actual documents and asked to answer only from them, with citations. That plus validation removes most made-up answers.

Can it answer questions from our own documents or database?

Yes — that's RAG. I embed your content into a vector store (pgvector or Pinecone) and retrieve the relevant passages at query time so responses are specific to you.

How do you keep API and token costs under control?

By caching, choosing the right model per task, capping context size, and adding per-user usage metering — so cost scales with value, not surprise.

Can you build a full AI SaaS, not just a prototype?

Yes. AI saas development including auth, billing, streaming UI, and usage limits is a core part of what I build — a shippable product, not a demo.

AI Application Development

Practical AI built into real products — LLM features, retrieval-augmented chat grounded in your own data, and automation — using the Claude and OpenAI APIs, embeddings, and vector search, not demos that hallucinate.

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What I build

What's included

LLM features in your app

Drafting, summarizing, classifying, and extracting structured data with the Claude or OpenAI APIs, streamed into your UI.

RAG over your data

Embeddings + a vector store (pgvector or Pinecone) so the model answers from your documents, with citations instead of guesses.

AI automation

Pipelines that read, decide, and act — tag support tickets, route emails, or extract invoice fields into a database.

Assistants & chatbots

Grounded, tool-using assistants with guardrails and fallbacks, wired to your systems rather than a generic chatbot.

AI SaaS builds

A full AI product with streaming responses, auth, and per-user usage metering — ai saas development, not a notebook prototype.

Problems this solves

Sound familiar?

An LLM demo that sounds confident but makes facts up because it can't see your data.
Staff manually reading and classifying the same documents or tickets every day.
A support team answering the same questions that live in your own docs.
A promising prompt stuck in a notebook that never became a real feature.
Unpredictable token costs with no visibility or limits.

Example use cases

What this looks like in practice

Grounded knowledge assistant

A RAG assistant over company docs using pgvector, answering staff questions with citations to the source paragraph.

Document extraction pipeline

An automation that reads incoming PDFs and emails, extracts the fields, and writes clean rows into a database.

AI SaaS feature

A streaming AI writing feature with auth and per-plan usage limits so costs stay tied to revenue.

Tech stack

The tools I use for this

Models

Claude (Anthropic)
OpenAI GPT

Retrieval

Embeddings
pgvector
Pinecone

App

Next.js streaming
Vercel AI SDK
TypeScript

Ops

Usage metering
Caching
Cost tracking

Process

How it works

Find the fit

Pin down where AI adds real value — and where it doesn't.

Ground it

Add retrieval over your data so answers are accurate.

Build & guard

Ship the feature with guardrails, streaming, and limits.

Measure costs

Track tokens and usage so spend stays predictable.

AI features need data plumbing to be useful, so I pair them with solid API integration services and deliver the whole thing as full stack development.

Related services

FAQ

Questions, answered

Which models do you use — Claude or GPT?: Both. I use the Claude (Anthropic) and OpenAI APIs and pick per task — often Claude for long-context reasoning and writing — and keep the code model-agnostic so you can switch.
How do you stop the AI from hallucinating?: Retrieval-augmented generation: the model is grounded in your actual documents and asked to answer only from them, with citations. That plus validation removes most made-up answers.
Can it answer questions from our own documents or database?: Yes — that's RAG. I embed your content into a vector store (pgvector or Pinecone) and retrieve the relevant passages at query time so responses are specific to you.
How do you keep API and token costs under control?: By caching, choosing the right model per task, capping context size, and adding per-user usage metering — so cost scales with value, not surprise.
Can you build a full AI SaaS, not just a prototype?: Yes. AI saas development including auth, billing, streaming UI, and usage limits is a core part of what I build — a shippable product, not a demo.

Let's build it together

Email me with what you need — I'll reply with a clear plan and next steps. Prefer chat? WhatsApp works too.