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AI Integration

AI workflows, agents, and RAG systems connected to your product

AI Integration Built for Real Workflows

AI creates real business value when it is connected to the right workflow, data, and product experience. Simply adding an AI API is rarely enough.

Our AI Integration service helps businesses add practical AI features to existing products, internal tools, and backend systems. We build AI workflows that can process documents, summarize information, extract data, generate insights, automate manual steps, and support smarter product experiences.

We work with tools like OpenAI, Claude, AWS Bedrock, LangChain, LangGraph, RAG pipelines, vector databases, and backend APIs. But the goal is not to add AI because it sounds trendy. The goal is to build AI-powered features that are useful, reliable, cost-aware, and connected to real business operations.

We also focus on the engineering details that make AI work in production: data flow, latency, cost, fallback behavior, prompt structure, input validation, logging, monitoring, and safe response handling.

This service can include

  • AI workflow design
  • OpenAI, Claude, or AWS Bedrock integration
  • LangChain or LangGraph implementation
  • RAG and knowledge-base integration
  • Vector database setup
  • Backend integration with existing systems
  • Document processing and data extraction
  • Prompt and response handling
  • Tool calling and agent workflows
  • AI-powered automation workflows
  • Error handling and fallback logic
  • Cost and latency optimization
  • Basic safety, validation, and guardrails
  • Technical recommendations for scaling AI features

Best for

  • Products that need AI-powered features
  • Businesses with repetitive manual workflows
  • Teams that want AI connected to real business data
  • Existing applications that need AI integrated into backend logic
  • Document processing, summarization, or extraction workflows
  • Internal tools that need automation or smarter search
  • Founders validating an AI feature before a full build

How We Work

01

Discovery

We start by understanding your product, workflow, data sources, and the real business problem behind the AI request. Before choosing tools, we define what AI should actually improve.

02

Architecture

We design how AI fits into your existing system: model choice, data flow, backend integration, RAG or agent workflow, error handling, permissions, and cost considerations.

03

Implementation

We build the integration using the right tools - OpenAI, Claude, AWS Bedrock, LangChain, LangGraph, vector databases, APIs, or internal systems - with clean, maintainable engineering.

04

Improvement / Support

After launch, we help monitor quality, latency, cost, and reliability. We improve prompts, workflows, fallback logic, and system behavior as real users interact with the AI feature.

Technologies we work with

OpenAI

Anthropic Claude

AWS Bedrock

LangChain

LangGraph

LlamaIndex

Python

TypeScript

Node.js

FastAPI

PostgreSQL

Pinecone

pgvector

AWS

Docker