QAOcean

AI features inside your web and mobile applications

Chatbots, RAG-powered search, agentic workflows, and LLM features - engineered into your product, not sold as separate AI infrastructure.

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What is AI Integration?

AI Integration at QAOcean means building AI-powered features into the application you're already shipping. Not a separate "AI platform" engagement - features that live inside your product: a chatbot in your dashboard, RAG search over your knowledge base, an agentic workflow that automates a slow internal process, content generation, classification, summarization.

We're a development studio that ships AI features the same way we ship any feature: with senior engineers, embedded testing, observability, and a clear path from prototype to production. Your AI feature works on day one, doesn't hallucinate into your customers' faces on day fifty, and has the cost monitoring you need before the bill surprises you.

What We Deliver

01

LLM-Powered Features

OpenAI, Anthropic Claude, and open-source LLMs integrated into your app for chat, summarization, classification, content generation, and more.

02

RAG & Knowledge Agents

Retrieval-augmented generation grounded in your data - vector databases, document ingestion, citation tracking, hallucination guards.

03

Agentic Workflows

Multi-step agents that take actions: pulling from APIs, writing to your database, orchestrating tools, with audit trails and human-in-the-loop checkpoints.

04

Conversational AI

Chatbots and voice assistants that integrate with your product and your support stack - not generic widgets dropped onto a page.

05

AI Quality & Safety

Evaluation frameworks, prompt-injection defenses, output validation, and continuous monitoring of model behavior in production.

06

Cost & Performance Optimization

Caching, prompt compression, model routing, and observability so AI features don't blow up your bill or your latency budget.

Our Process

1

Use Case Definition

Sharpen the AI use case to a concrete user-visible feature - what does the user click, what does the model produce, what's the win?

2

Prototype & Evaluate

Build a working prototype on real data; measure accuracy, latency, and cost before investing in production engineering.

3

Production Engineering

Wire the prototype into your app with proper error handling, rate limiting, fallbacks, and the observability you need to operate it.

4

Quality & Safety Layer

Evaluation harness, guardrails, output validation, prompt-injection defenses - production AI is not just "call the API."

5

Launch & Continuous Improvement

Roll out, monitor, and iterate. Every AI feature ships with a feedback loop and a way to improve the prompt or model in flight.

Tools & Technologies

OpenAIAnthropic ClaudeLangChainLlamaIndexPineconeWeaviatepgvectorVercel AI SDKSentryLangSmithPromptLayer

Why Choose QAOcean

AI features that ship with your product, not as a separate AI platform engagement
Senior engineers building production-grade integrations - not consultants writing PowerPoints
Observability and cost monitoring from launch day
Quality engineering and safety guardrails embedded - not bolted on after a hallucination incident
Clear path from prototype to production with measurable accuracy/latency/cost gates
Maintainable code your team can own after we hand it off

Frequently Asked Questions

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