What's included

• Requirements & Use-Case Analysis Report

  Document outlining business needs, workflows, and how AI agents will fit in.

• System Architecture & Design

  1. High-level architecture diagram showing LangChain, LangGraph, APIs, databases, and integrations. 2. Technical documentation explaining the design choices.

• All other delieverables

  Custom AI Agent(s) - Configured AI agents built with LangChain & LangGraph. - Includes logic graphs (LangGraph workflows), memory management, and tool integrations. Knowledge Base Integration - Ingestion pipelines for client documents, databases, or APIs. - Vector database setup for semantic search (e.g., Pinecone, Weaviate, or FAISS). Tool & API Integrations - Connectors to external services (CRM, Slack, email, databases, etc.). - Custom tools wrapped for the agent (e.g., calculators, data fetchers). Conversation Interface / UI - Chat interface (web app, Slack bot, or internal dashboard). - APIs for third-party system integration. Agent Control & Orchestration - Multi-agent collaboration setup if needed (e.g., one agent for data retrieval, another for reasoning). - Error handling and fallback strategies. Testing & Validation - Test cases covering workflows, accuracy, and reliability. - Performance benchmarks (response times, cost tracking, etc.). Deployment & Hosting - Production-ready deployment on client’s infrastructure (AWS, GCP, Azure, or Vercel). - CI/CD pipeline for updates and scaling. Documentation & Training - User manual for non-technical team members. - Developer documentation for future maintenance. - Training session(s) with client’s team. Post-Delivery Support (optional) - Bug fixes for a defined period. - Ongoing optimization and fine-tuning.