Use tools
Call APIs, query databases, search the web, and execute code with robust error handling.
Multi-agent · Tool use · Production guardrails
Agents that act
We build autonomous AI agents that reason, plan, and execute complex business tasks — with tool use, persistent memory, guardrails, and human-in-the-loop controls. From single-agent automations to multi-agent orchestration systems.
MultiAgent
Tools+ APIs
HITLControls
ProdReady
What is an AI agent
Beyond chatbots
An AI agent is an autonomous system powered by LLMs that perceives, reasons, decides, and acts. Unlike chatbots that simply respond, agents use tools, maintain memory, plan multi-step workflows, collaborate with other agents, and self-correct — built for real enterprise workloads in healthcare, fintech, insurance, and SaaS.
Maintain memory
Remember context across sessions, user preferences, and build knowledge over time.
Plan workflows
Break complex tasks into sub-tasks and execute sequentially or in parallel.
Collaborate & self-correct
Work with other agents in coordinated systems and retry with different approaches.
Decision guide
Chatbots vs AI agents
Most enterprises need both — we help you draw the line and build what actually moves the needle.
Reactive conversation
- Respond to user messages in a UI
- Single-turn or simple multi-turn Q&A
- Limited tool access, if any
- No autonomous planning or delegation
Proactive execution
- Plan and execute multi-step tasks autonomously
- Deep tool, API, and system integration
- Persistent memory across sessions
- Multi-agent collaboration with guardrails
What we build
Agent capabilities
Production-grade agents — not demos. Every system ships with observability, safety, and enterprise integration built in.
Multi-Agent Orchestration
Coordinated agent teams where specialized agents collaborate on complex tasks. Supervisor agents manage workflow, delegate, and ensure quality.
- LangGraph stateful workflows
- CrewAI role-based teams
- Supervisor + worker patterns
Single-Agent Automation
Autonomous agents handling complete business processes — data entry, document review, customer responses, report generation.
Tool-Use & API Integration
CRM, EHR, databases, APIs, cloud services — with error handling and retry logic.
Memory & Context
Short and long-term memory across sessions, preferences, and accumulated knowledge.
Guardrails & Safety
Input validation, output filtering, PII detection, action constraints, and approval workflows.
Agent Observability
Decision logs, action traces, performance metrics, cost tracking, and anomaly detection.
Our process
How we build
Discovery to production in ~12 weeks — iterative sprints with real data and continuous edge-case testing.
Discovery & use case mapping
Identify high-impact workflows, map decision trees, data sources, integrations, and success metrics.
Week 1–2Agent architecture design
Agent roles, tool definitions, memory systems, guardrails, and orchestration patterns.
Week 2–3Build & iterate
Rapid prototyping with real data in 2-week sprints. Continuous testing against failure scenarios.
Week 3–10Production deployment
Monitoring, alerting, cost tracking, graceful degradation, and human-in-the-loop for critical decisions.
Week 10–12
Technology
Agent stack
Frontier LLMs, proven orchestration frameworks, and enterprise infrastructure — chosen for your workflow complexity and compliance needs.
Layer 01
LLMs
Layer 02
Frameworks
Layer 03
Infrastructure
Layer 04
Observability
Proof of work
Agent case studies
Real multi-agent systems shipped to production — with measurable business outcomes.
Multi-Agent CRM Pipeline
LangGraph multi-agent system automating lead scoring, outreach, and pipeline management for 50K+ accounts.
34%Pipeline growth
3minLead response
AI EHR Onboarding Automation
AI-powered document extraction pipeline reducing patient onboarding from 45 minutes to 8 minutes.
82%Time saved
$1MAnnual savings
~12 wkTo production
MultiAgent ready
10+Years experience
200+Projects shipped
01What is an AI agent and how does it work?
An AI agent is an autonomous software system that can perceive its environment, reason about tasks, make decisions, and take actions to achieve specific goals. Unlike simple chatbots, AI agents can use tools (APIs, databases, web search), maintain memory across interactions, plan multi-step workflows, and collaborate with other agents. They combine large language models with orchestration frameworks like LangChain and CrewAI.
02How much does enterprise AI agent development cost?
Enterprise AI agent development typically costs between $75,000 and $300,000+ depending on complexity, number of agents, integrations needed, and compliance requirements. A single-agent proof of concept can start at $30,000–$50,000. Multi-agent systems with enterprise integrations and guardrails are at the higher end. Contact us for a detailed estimate.
03What is the difference between AI agents and chatbots?
Chatbots respond to user messages in a conversational interface. AI agents go further — they can autonomously plan and execute multi-step tasks, use external tools and APIs, maintain persistent memory, collaborate with other agents, and make decisions with minimal human oversight. AI agents are proactive, while chatbots are reactive.
04What frameworks do you use to build AI agents?
We build AI agents using LangChain, LangGraph, CrewAI, AutoGen, and custom orchestration frameworks. The choice depends on your requirements: LangGraph for complex stateful workflows, CrewAI for role-based multi-agent collaboration, and custom architectures when off-the-shelf frameworks don't meet performance or compliance needs.
Next step
Ready to build your AI agent?
Tell us about your use case — we'll design an agent architecture and provide a detailed estimate within 48 hours.