Agentic AI Development Services

Overview

From copilots to agents

Most "AI" stops at answering questions. An agent goes further: it breaks a goal into steps, calls your tools and APIs, checks its own work, and gets a task done. We build that second kind.

Our agents are wired into your real systems, commerce, ERP, CRM, and data, through APIs, the Model Context Protocol (MCP), and events. Every engagement is scoped to a workflow a manager would recognize, not a demo that lives in a slide deck.

Task completion, not just conversation
Wired into your real systems via API, MCP, and events
Human-in-the-loop where the stakes justify it

Capabilities

What we deliver

Custom AI Agents

Bespoke autonomous agents scoped to one high-value workflow, grounded in your data and your rules.

Multi-Agent Systems

Orchestrated teams of specialist agents with a supervisor and clean handoffs for complex, multi-step processes.

Tool & System Integration

Agents connected to your ERP, CRM, commerce, and data, including Frappe and ERPNext, via APIs, MCP servers, and webhooks.

Retrieval & Grounding (RAG)

Agents that act on your facts, not guesses, with retrieval over your knowledge bases, catalogs, and documents.

Guardrails & Evaluation

Approval gates, human-in-the-loop, evaluation harnesses, and observability so agent behavior stays inside agreed bounds.

Agent Ops

Deploy on your cloud, monitor cost, latency, and quality, and iterate on the behaviors that prove their value.

Outcomes

Agents that earn their keep

The graveyard of enterprise AI is full of impressive demos that never touched production. We take the opposite path: scope one workflow, ship a working agent to a real task early, keep a person in the loop where it matters, and expand only what proves itself.

Manual, repetitive work handed off to agents that run around the clock
Faster turnaround on tasks that used to queue behind people
A first agent in production in weeks, measured against a real outcome

Use cases

Where agents earn their place

Customer Support

Grounded resolution over your knowledge base, with confident escalation to a person when needed.

Document Processing & Compliance

Extract, classify, and validate claims, contracts, and forms against your policies, flagging exceptions for review, for healthcare, insurance, and legal teams.

Finance & Reconciliation

Match invoices, reconcile accounts, and triage exceptions across your finance systems, with a human on approvals.

Sales & Lead Research

Prospecting, enrichment, and drafted outreach pushed straight into your CRM for a human to approve.

IT & DevOps Triage

First-line triage of alerts and logs, runbook execution, and drafted tickets, escalating whatever needs a human.

HR & Knowledge Ops

Answer policy questions, route requests, and move onboarding steps forward, grounded in your internal knowledge.

Commerce & Order Operations

Catalog, pricing, and order triage, plus RFQ and PO parsing straight into your ERP for approval.

Data Migration & Enrichment

Clean, deduplicate, enrich, and map records between your core systems during migrations and everyday sync.

How we work

A process built for agents

Scope

We pick one high-value workflow and define the task, the tools the agent needs, and the metric that means success.

Design

Agent architecture, tool access, guardrails, and human-in-the-loop points, validated before we build.

Build & Evaluate

We build in releasable increments and test behavior against an evaluation harness, not vibes.

Deploy & Monitor

Ship to production, watch cost, latency, and quality, and expand the agent only where it proves out.

FAQ

Questions, answered

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

A chatbot answers questions. An agent acts. It breaks a goal into steps, calls your tools and APIs, checks its own work, and completes a task, with guardrails around what it is allowed to do. The value is measured in work completed, not conversations held.

How do you stop an agent from doing the wrong thing?

Least-privilege tool access, human-in-the-loop approval gates on consequential actions, evaluation harnesses that test behavior before release, and monitoring in production. The agent can only do what you have explicitly allowed, and a person stays in the loop where the stakes justify it.

Do we have to replace our existing systems to use AI agents?

No. Agents integrate with the systems you already run, your commerce platform, ERP, CRM, and data, through APIs, the Model Context Protocol (MCP), and events. There is no platform replacement; the agent works on top of what you have.

Which models and frameworks do you use?

We are model-agnostic. We select models and orchestration per use case based on cost, quality, latency, and data-residency needs, spanning Anthropic Claude, OpenAI, AWS Bedrock, and Vertex AI, orchestrated and deployed on tooling such as n8n and Cloudflare Workers AI.

How fast can we see value from an AI agent?

We scope the first agent to a single, measurable workflow and ship it to a real task in weeks, not a demo that dies in a slide deck. From there we monitor, prove the outcome, and expand only what earns it.

Agentic AI Development