LLM features in your app
Drafting, summarizing, classifying, and extracting structured data with the Claude or OpenAI APIs, streamed into your UI.
AI application development adds model-based functions such as summarization, extraction, classification, retrieval, or assisted drafting to a software product. It is intended for teams with a defined workflow and data source that need an AI feature integrated with existing systems and measured for accuracy, cost, and failure cases.
What I build
What is included in AI application development?
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
What problems can AI application development solve?
- 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 does AI application development look 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
What technologies power AI application development?
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 does AI application development work?
01
Find the fit
Pin down where AI adds real value — and where it doesn't.
02
Ground it
Add retrieval over your data so answers are accurate.
03
Build & guard
Ship the feature with guardrails, streaming, and limits.
04
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
API Integration Services
API integration connects applications so they can exchange data and trigger actions without manual transfer. It is intended for teams that need a custom API, a third-party service connection, webhook processing, or a secure partner-facing interface.
SaaS MVP Development
SaaS MVP development turns a defined product idea into an initial web application with the core features needed to test it with users. It is intended for founders and teams that need authentication, billing, tenant-aware data, deployment, and analytics in a deliberately limited first release.
Next.js Development Services
Next.js development uses React with server rendering, static generation, route handlers, and the App Router to build websites and web applications. It is intended for teams whose product needs search-visible pages, server-rendered content, integrated backend logic, or content managed through a CMS.
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.