Custom AI agents that reason,
act, and integrate with your systems.
Built on LangChain, LangGraph, CrewAI, and the Model Context Protocol. We design and deploy multi-agent systems, RAG pipelines, and autonomous workflows that handle real business workloads.
Discuss Your Agent ArchitectureWhat We Build
RAG Systems & Knowledge Assistants
AI that reasons over your proprietary data — documents, databases, codebases, support tickets, internal wikis. We build retrieval-augmented generation pipelines with vector stores, hybrid search, re-ranking, and conversational memory.
Multi-Agent Orchestration
Systems where multiple AI agents collaborate on complex tasks. Research agents feed analysis agents that feed decision agents — coordinated through LangGraph state machines with human-in-the-loop checkpoints.
Sales & Support Chatbots
AI chatbots that qualify leads, answer product questions, and escalate to humans when needed. These agents actually reason about customer intent, pull relevant information from your knowledge base, and have natural conversations that close deals.
MCP Server Integrations
We build and connect Model Context Protocol servers that give AI agents secure, structured access to your tools, databases, and APIs. The MCP standard is how modern AI agents interact with the real world.
Document Intelligence
AI pipelines that extract, summarize, classify, and act on information from documents. Contract analysis, invoice processing, compliance review, knowledge extraction — with structured outputs that feed into your existing systems.
Agentic Coding & Internal Tools
Rapid development of internal tools powered by AI agents. Niche intelligence dashboards, content analysis systems, analytics platforms — built fast with Claude Code workflows, optimized for functionality.
How We Approach Agent Development
Architecture First
Every agent project starts with architecture design. We map out the agent graph — what tools it needs, what state it maintains, how it handles failures, and where humans should be in the loop.
Framework Selection
We choose the right framework for the job. LangGraph for complex stateful workflows. CrewAI for role-based multi-agent teams. OpenClaw for autonomous personal agents. Raw API calls when frameworks add overhead.
Evaluation-Driven Development
We build evaluation pipelines alongside the agent itself. Automated testing on real queries, retrieval quality metrics, response accuracy scoring, and regression detection.
Production Hardening
Memory management, token budget optimization, fallback chains, rate limiting, error recovery, and observability. We use LangSmith for tracing so you can see exactly what your agents are doing and why.
Pricing
AI agent development is scoped based on complexity. All projects start with a paid discovery phase ($2,500–$5,000) producing a detailed architecture document and fixed-price implementation quote.
Simple Agent
$5,000–$15,000RAG chatbot, single-purpose tool. Single agent with retrieval pipeline, basic memory, one data source, deployed and monitored.
Multi-Agent System
$15,000–$50,0002–5 agents, complex workflows. Orchestrated agent teams with shared state, multiple tool integrations, human-in-the-loop gates, and evaluation pipelines.
Enterprise Platform
$50,000+Production AI infrastructure. Multiple agent systems, custom MCP servers, admin dashboards, audit trails, team access controls, and ongoing optimization.
Ongoing Retainer
$3,000–$10,000/moContinuous agent improvement, new capability development, monitoring, and support.
Have a use case for AI agents?
Let's talk architecture. We'll assess your requirements, recommend the right approach, and tell you exactly what it'll take to build.
Discuss Your Agent Project