AI Agents Built for Production, Not Demos

We design, build, and deploy custom AI agents that handle real business workflows — with proper security, error handling, and human-in-the-loop controls.

Duration: 1-4 months Team: 2-4 Senior AI Engineers

You might be experiencing...

Your AI prototype works in demos but breaks under real-world conditions
Agents hallucinate, miss edge cases, and lack proper error handling
No clear architecture for multi-agent orchestration and escalation
Security, compliance, and audit requirements are afterthoughts

We build AI agents that handle real business workflows — not chatbot wrappers around an LLM API call.

Every agent we build follows our production-grade methodology: structured architecture, comprehensive skill mapping, security-first design, and human-in-the-loop controls for high-stakes decisions.

Our agents integrate with your existing systems — CRM, ERP, HRIS, databases, APIs — through proper authentication, authorization, and audit trails. We don’t build black boxes. We build transparent, observable, and controllable AI systems.

We specialize in agents for sales automation, customer support, operations, fintech compliance, and logistics optimization — domains where we have deep expertise in both the AI technology and the business context.

Engagement Phases

Weeks 1-2

Design

Agent architecture design, skill mapping, integration planning, security model, and human-in-the-loop workflow definition.

Weeks 3-10

Build

Agent development with iterative testing, skill/plugin implementation, system integration, monitoring setup, and prompt engineering.

Weeks 11-16

Harden & Deploy

Production hardening, load testing, security audit, compliance review, deployment to production, operational runbook creation.

Deliverables

Production-ready AI agents with full test coverage
Custom skills and plugin integrations
System integration layer with auth/authz framework
Monitoring dashboards and alerting
Operational runbooks and escalation procedures
Knowledge transfer and team training

Before & After

MetricBeforeAfter
Agent Accuracy60-70% (prototype)95%+ (production-grade)
Error HandlingSilent failuresGraceful degradation with human escalation
Security PostureNo guardrailsFull auth, audit trails, prompt injection protection
Time to Deploy6-12 months (traditional)1-4 months

Tools We Use

Claude / GPT-4o Claude MCP LangChain / LangGraph Langfuse Kubernetes Pinecone / pgvector

Frequently Asked Questions

How long does it take to build a production AI agent?

Typical engagements run 1-4 months depending on complexity. The first 2 weeks focus on architecture design and skill mapping, weeks 3-10 on development and testing, and weeks 11-16 on production hardening, security audit, and deployment.

What makes your agents different from chatbot wrappers?

Our agents integrate with your existing CRM, ERP, HRIS, and APIs through proper authentication and audit trails. They include structured error handling, human-in-the-loop controls for high-stakes decisions, and prompt injection protection — not just an LLM API call behind a chat interface.

Which AI models and frameworks do you use?

We work with Claude, GPT-4o, and other foundation models, using frameworks like LangChain, LangGraph, and Claude MCP. We select the best model for each use case based on accuracy, cost, and latency requirements.

Do you provide ongoing support after deployment?

Yes. Every engagement includes operational runbooks, monitoring dashboards, and knowledge transfer training. We also offer a Managed AI Operations retainer for ongoing prompt optimization, model updates, and incident response.

What domains do you specialize in?

We have deep expertise in agents for sales automation, customer support, operations, fintech compliance, and logistics optimization. These are domains where we understand both the AI technology and the business context required for production-grade results.

Get Started for Free

Schedule a free consultation with our AI agents team. 30-minute call, actionable results in days.

Talk to an Expert