AI Agents That Work in Production, Not Just in Demos

We build autonomous agents that handle multi-step business workflows - processing documents, managing orders, automating compliance - with the guardrails and reliability that production environments demand.

Why We Build Agents Differently

The gap between a working demo and a production agent is where most AI projects stall. We close that gap because we build and operate agents for our own teams first.

Demo Agent

Clean Input, Happy Path

Agent works perfectly with well-structured test data. Demos impressively. Stakeholders approve the concept.

Linear Workflow

Single path from input to output. No branching logic for exceptions. No recovery when something unexpected happens.

Basic Prompt Engineering

System prompts tuned for ideal inputs. No handling for ambiguous, incomplete, or adversarial user messages.

No Cost Controls

Token usage unchecked. No limits on API calls. Fine in a demo, unsustainable at scale with real traffic.

Silent Failures

When the agent fails, it fails quietly. No escalation path. No monitoring. No way to know it broke until a user complains.

Engineers Validated

Production Agent (How We Build)

Input Validation & Guardrails

Every input validated before processing. Guardrails prevent hallucination, scope drift, and unsafe outputs. Built from real-world edge cases.

Branching Logic & Error Recovery

Agents handle exceptions, retry with backoff, and degrade gracefully. When they cannot complete a task, they escalate — not fail silently.

Human-in-the-Loop Escalation

Transparent escalation paths when confidence is low. Full context preserved so the human picks up where the agent left off.

Cost Controls & Rate Limiting

Token budgets, API rate limits, and cost monitoring built in from day one. Production-ready means financially sustainable at scale.

Continuous Monitoring & Improvement

Every agent action is logged and monitored. Performance tracked, failures surfaced, and the agent improves based on real production data.

What We Build

Workflow Automation Agents

Agents that execute multi-step processes across systems - triggering from events, pulling data, processing it, updating records, and notifying stakeholders.

Document Intelligence Agents

Extract, classify, validate, and route information from documents - invoices, contracts, compliance filings - into structured, system-ready output.

Conversational Commerce Agents

AI agents on WhatsApp, web, and voice that take orders, confirm transactions, check availability, and escalate to humans when needed.

Multi-Agent Systems

Workflows where specialized agents collaborate - one researches, another analyzes, a third drafts, a supervisor validates. For tasks too complex for a single agent.

Internal Operations Agents

Agents for your team: meeting summarizers, sales prep assistants, reporting automation, knowledge base search. Built from patterns we run internally.

Agent Monitoring & Optimization

Post-deployment tracking of accuracy, cost, latency, and satisfaction. Continuous improvement loops that refine agent behavior from real usage data.

Technology We Work With

Agent Frameworks

LLM Providers

Vector Databases

Integration & Automation

Channels

Monitoring

Our Work

AI Demo / Cross-Industry

Built an AI agent that processes unstructured documents with 85% extraction accuracy

Processes invoices, contracts, and reports with intelligent field extraction and confidence scoring

Validates extracted data against configurable business rules before downstream push

Handles multiple document formats (PDF, scanned images, emails) with OCR integration

Provides audit trail and exception queue for human review of low-confidence extractions

85%

Extraction Accuracy

70%

Faster Document Processing

90%

Reduction in Manual Review

View Case Study

AI Demo / B2B Commerce

Deployed a WhatsApp agent that processes B2B orders 60% faster across 3 languages

Processes orders via text and voice messages in English, Hindi, and Spanish

Confirms line items, quantities, and pricing with the buyer before submission

Checks real-time inventory availability and suggests alternatives for out-of-stock items

Routes confirmed orders to fulfillment and sends delivery tracking updates

60%

Faster Order Processing

40%

Reduction in Order Errors

Languages Supported

View Case Study

AI Demo / Enterprise Operations

Automated 75% of support ticket classification with AI-driven triage and routing

Monitors incoming tickets across email, chat, and web form channels in real time

Classifies tickets by category, urgency, and complexity using trained language models

Assigns priority scores and routes to the correct team based on skill matching

Learns from resolution patterns to continuously improve classification accuracy

75%

Automated extraction and classification

50%

Faster Resolution Time

30%

Fewer Misrouted Tickets

View Case Study

Have a workflow that is too complex for simple rules but too repetitive for your team?

Talk to an Expert

Frequently Asked Questions

What is the difference between an AI agent and a chatbot?

A chatbot follows predefined conversation flows and responds to specific inputs. An AI agent reasons about goals, uses tools, makes decisions, and executes multi-step actions autonomously. A chatbot tells a customer their order status. An agent processes a return, updates inventory, issues a refund, and notifies the warehouse - all without human intervention unless it encounters something outside its defined scope.

How do you ensure AI agents are reliable in production?

Three layers. First, guardrails that define what the agent can and cannot do - boundaries are set before deployment, not discovered after. Second, human-in-the-loop escalation so the agent recognizes uncertainty and routes to a person with full context. Third, continuous monitoring of accuracy, cost, and user satisfaction post-launch. We test extensively before go-live and iterate based on real usage data.

What does it cost to build a custom AI agent?

Cost depends on complexity. A single-purpose document processing agent is a different scope than a multi-agent system orchestrating across five enterprise tools. Our AI Adoption Discovery program (3 weeks) assesses your use case, builds a working proof-of-concept, and gives you a clear picture of scope and investment before you commit to a full build.

Can AI agents integrate with our existing tools and systems?

Yes. Agents connect to your CRM, ERP, databases, communication tools, and internal platforms through APIs. Common integrations include Salesforce, HubSpot, Slack, WhatsApp, Google Workspace, and custom enterprise systems. The agent becomes a layer that operates across your existing tools - not a replacement for any of them.

How long does it take to build and deploy an AI agent?

A focused proof-of-concept for a single use case takes 3-4 weeks. A production-grade agent with full integration, testing, and monitoring runs 8-12 weeks. Multi-agent systems are phased over 3-6 months. Our AI Adoption programs provide structured entry points for assessment and prototyping.

What happens when the AI agent cannot handle something?

Every agent we build includes human-in-the-loop escalation paths. When the agent encounters uncertainty, ambiguous input, or a scenario outside its defined scope, it routes to a human with full context of the conversation and every action attempted. No dead ends for users, no silent failures for your team.

Let's Build The Next Big Thing

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