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Service 03 · AI & RAG Applications

AI that works
in production.

LLM-powered apps, retrieval pipelines, APIs, and MCP servers grounded in your own data — built to be accurate, secure, and ready for real users.

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Best for
  • Teams with deep domain knowledge
  • Products adding AI features
  • Workflows buried in documents
Tools & stack
ClaudeOpenAILangChainpgvectorPineconeNext.jsPythonMCPAWS Bedrock
Overview

We built the NoteDoctor.AI Prior Auth Engine: a RAG app, a public API, and an MCP server running inside Claude and ChatGPT.

Beyond the demo: AI your customers can rely on.

It’s easy to wire up a chatbot. It’s hard to build an AI product that gives correct, cited answers from your own documents, handles edge cases, and holds up under real traffic. That’s the work we do.

We design retrieval pipelines around your data, choose the right models for cost and quality, add evaluation so you know how well it performs, and ship it as a web app, a public API, or an MCP server that plugs your product straight into Claude, Cursor, and ChatGPT.

What's included

Everything you need, handled by one team.

01
RAG pipelines
Ingestion, chunking, embeddings, vector search, and reranking tuned to your documents.
02
LLM applications
Full-stack apps built around Claude, OpenAI, or open models, with streaming and tool use.
03
Agents & workflows
Multi-step agents that call your tools and APIs to finish real tasks, not just answer questions.
04
MCP servers
Model Context Protocol servers with OAuth that put your product inside AI assistants.
05
Public APIs
Documented REST APIs with scoped keys, a playground, rate limits, and usage-based billing.
06
Evaluation & guardrails
Test sets, quality metrics, and safety checks so you can improve the system with confidence.
How it works

A clear process, from first call to launch.

Week 1–2
Discovery
We map your data, the decisions it supports, and what a correct answer looks like.
Week 3–4
Prototype
A working retrieval and generation pipeline, measured against a real evaluation set.
Week 5–7
Build
The production app, API, or MCP server — auth, billing, logging, and UI included.
Week 8
Launch
Deployment, monitoring, cost controls, and a plan to keep improving quality.
Related work

AI & RAG Applications we've shipped.

NoteDoctor.AI — Prior Auth Engine
AI · API · MCP
NoteDoctor.AI — Prior Auth Engine
RAG-powered prior authorization readiness app built with Next.js, LangChain, and OpenAI, plus a public screening API and an MCP server that puts the engine inside Claude, Cursor, and ChatGPT.
FAQ

Common questions.

Is our data safe?

We design for privacy from the start: your data stays in your infrastructure where possible, providers are configured not to train on it, and access is scoped and logged.

Which model should we use?

It depends on your accuracy, speed, and cost needs. We benchmark candidates on your own data during the prototype phase and recommend the best fit.

What is an MCP server?

The Model Context Protocol lets AI assistants like Claude and ChatGPT use your product’s tools directly. An MCP server makes your product available wherever your users already work with AI.

How do you prevent wrong answers?

Grounding every answer in retrieved sources, citing them, measuring quality with evaluation sets, and adding guardrails for the cases that matter most.

Other services
01Web Design & DevelopmentFast, beautiful sites that convert visitors into customers02Mobile AppsNative-quality iOS & Android from MVP to full product04Branding & IdentityLogos, visual systems, and guidelines built to last05SEO & Digital MarketingOrganic growth strategies that compound over time06UI/UX DesignResearch-led design that reduces friction and lifts retention07Social Media ManagementOn-brand content that builds community and drives leads

Ready to get
started?

Book a free 30-minute call and let's talk about what you're building.

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