AI Automation

Your team is burning hours on repetitive tasks AI agents can handle in seconds

We build custom AI agents on LangGraph and MCP that orchestrate complex workflows, call your APIs, and act autonomously — so your team focuses on high-value work.

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The challenges you're facing

Manual multi-step processes consuming 40+ hours of senior staff time every week

Disconnected tools that require humans to relay data between systems all day

No audit trail or visibility into which workflows are bottlenecks until they break

Autonomous AI Agents Built for Your Workflows

We design and deploy purpose-built AI agents using LangGraph for stateful orchestration and Model Context Protocol for tool connectivity. Each agent is scoped to a real business process — not a generic chatbot. We integrate with your existing APIs, databases, and SaaS platforms, then deploy with full observability so you can trust what the agent is doing.

What you get

1

Workflow Audit

Map your highest-cost repetitive workflows and identify which are best suited for autonomous agent handling.

2

Agent Architecture Design

Design the agent graph, tool connections, memory strategy, and human-in-the-loop escalation points.

3

Build & Integration

Implement the agent with full API integration, error handling, retries, and LangSmith observability.

4

Monitoring & Iteration

Deploy to production with dashboards, alerts, and a scheduled review cycle to expand agent coverage.

Technologies & tools

LangGraphModel Context ProtocolOpenAI GPT-4oAnthropic ClaudeLangSmithFastAPIPostgreSQLRedis

Case study — anonymised

B2B SaaS — Customer Success

Before

4 customer success managers spending 3 hours/day manually triaging support tickets, pulling account data, and drafting responses.

After

AI agent handles tier-1 triage, enriches tickets with CRM data, and drafts responses for human approval — 90% automatically.

11 hours/day reclaimed across the team, response time cut from 4 hours to 18 minutes

Further reading

AI Agent Frameworks Compared: LangGraph vs. AutoGen vs. CrewAI

Every agent framework claims to be the easiest way to build autonomous AI. The honest differences are in how they handle state, retries, and the messy multi-step workflows real businesses actually run.

Building an Internal AI Helpdesk: Architecture & ROI

Most IT helpdesk volume is the same handful of requests repeated hundreds of times a month. Here's the architecture for actually resolving them with AI, not just routing them faster.

What Is Model Context Protocol (MCP)? A Guide for Enterprises

Every AI assistant your team uses is only as useful as the data it can actually see. MCP is the standard closing that gap — and it comes with its own security questions.

Frequently Asked Questions

Common questions from enterprise and mid-market teams across India and internationally.

What is LangGraph and why is it better than basic LLM chains?
LangGraph is a stateful orchestration framework for AI agents that supports cycles, conditional branching, and persistent memory. Unlike linear chains, LangGraph agents can loop, retry, ask clarifying questions, and maintain context across long workflows — making them suitable for complex real-world processes.
What is Model Context Protocol (MCP)?
MCP is Anthropic's open standard for connecting AI models to external tools and data sources. It lets an AI agent call your APIs, read databases, and interact with services in a standardized way. We use MCP to make agents genuinely connected to your business systems, not just text generators.
How long does it take to build and deploy a custom AI agent?
Simple single-tool agents take 2–3 weeks. Multi-step agents connecting 5+ systems typically take 4–8 weeks including QA and staging validation. We always start with a scoped pilot before expanding.
How do you ensure the agent doesn't make mistakes on critical tasks?
We design human-in-the-loop checkpoints for any action with financial, legal, or external customer impact. The agent proposes; a human approves. We also implement confidence thresholds — if the agent is uncertain, it escalates rather than guessing.
Can AI agents integrate with our existing tools like Salesforce, Slack, or Jira?
Yes. We build MCP connectors for standard SaaS APIs and custom REST integrations for bespoke systems. Common integrations include Salesforce, HubSpot, Slack, Jira, ServiceNow, Google Workspace, and any REST or GraphQL API you expose.

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