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AI Web Applications

Every funded startup is asking how AI fits their product. I build LLM apps, RAG systems, and intelligent workflows for real customers — not demos that stall in staging.

What you get

  • ✓LLM copilots and chat interfaces with streaming and guardrails
  • ✓RAG pipelines grounded in your docs, tickets, and internal data
  • ✓AI agents that take multi-step actions across your tools
  • ✓Production deployment, monitoring, and handoff documentation

How it works

01

AI strategy & scoping

We map your use case, data sources, and success metrics. You get a fixed scope and timeline before build starts.

2-3 days
02

Architecture & model selection

Choose the right stack — OpenAI, Anthropic, open models, vector DB, and integration points.

3-5 days
03

Build & integrate

Implement features in weekly increments with demos. Auth, APIs, and AI features ship together.

4-10 weeks
04

Test & launch

Evaluate accuracy, latency, and fallbacks. Deploy to production with a support window.

1-2 weeks

Frequently asked questions

What types of AI can you integrate into web apps?+

LLM chat and copilots, RAG over your knowledge base, recommendation systems, content generation, and agent workflows that connect to CRMs, email, and internal APIs.

Do you use existing AI APIs or custom models?+

Both. I integrate OpenAI, Anthropic, and open-source models for speed, and can fine-tune or host models when you need data privacy or domain-specific performance.

How do you prevent hallucinations in production?+

RAG with source citations, validation datasets, confidence thresholds, human-in-the-loop fallbacks, and continuous monitoring of answer quality.

Can you work with our existing codebase?+

Yes. Most engagements plug into an existing Next.js or React app. I match your conventions and ship via PRs with weekly demos.

Technologies

OpenAIAnthropicLangChainPythonNext.jsPineconePostgreSQL

Read: production RAG patterns →

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