AI & agents · Module

AI & Integrations (MCP Builder)

The MeraUdyog MCP Builder lets you expose your own business data and actions as a Model Context Protocol server, so AI assistants such as Claude, ChatGPT and Cursor can query and act on your leads, employees, calls, attendance and invoices in natural language. With no custom integration project.

Why it matters

This is where MeraUdyog is ahead of every named competitor. Most category leaders are only beginning to open up to agentic AI access. Here it ships today, alongside AI call summarization and tiered call recording storage plans.

Because AI & Integrations (MCP Builder) shares one data core with every other module, the work it produces is immediately usable everywhere else: scoped by branch, center and owner, visible in Analytics, and available to Workflow Automation as a trigger.

> Which telecallers are below target on meaningful calls this week?

meraudyog.get_call_analytics(period="WTD", metric="meaningful_calls", group_by="owner")

  Owner              Dials   Connected   Meaningful   Rate
  Farah Qureshi       412        281          198     48%   above target
  Rahul Menon         388        264          171     44%   above target
  Aditi Sharma        451        196           62     14%   below target
  Nikhil Jain         204         88           41     20%   below target

Pattern: Aditi has the highest dial count and the lowest
meaningful rate: high volume, low depth. Average duration
is 38s against a team median of 4m 12s.

> Summarise her three longest calls yesterday

meraudyog.get_call_summaries(owner="Aditi Sharma", date="yesterday", limit=3)

  1. Fee structure query: asked for EMI options, no follow-up set
  2. Course comparison: competitor pricing raised, unresolved
  3. Callback request: parent wanted evening slot, not scheduled
Feature inventory

Everything in AI & Integrations (MCP Builder), by sub-category.

AI assistant connectivity

MCP (Model Context Protocol) BuilderConnect Claude to MeraUdyogConnect ChatGPT to MeraUdyogConnect Cursor to MeraUdyogNatural-Language Data Queries Across Modules

AI call intelligence

AI Call SummarizationCall Recording Storage PlansSearchable Call Summaries

AI insight

Lead ScoringAI-Driven InsightsAnomaly Flags
Feature detail

What each capability does. And the problem it removes.

Features are only worth listing if the business problem behind them is named.

AI & Integrations (MCP Builder): feature, problem solved, and business benefit
FeatureWhat it doesProblem solvedBusiness benefit
MCP BuilderExposes your MeraUdyog data and actions as an MCP serverAI assistants cannot see or act on business data without custom integration workAny AI assistant becomes a live interface to the business
Connect Claude / ChatGPT / CursorConnects MeraUdyog to leading AI chat and coding assistantsGetting an answer means logging in and navigating dashboardsBusiness questions answered conversationally, in tools staff already use
Natural-Language Data QueriesPlain-English questions against CRM, HR and Finance dataNon-technical users cannot write their own reports or filtersData access democratised beyond dashboard-literate users
AI Call SummarizationAuto-generates a text summary of every recorded callManagers cannot listen to every call to know what was saidFifty calls reviewed in the time it took to hear three
Call Recording Storage PlansTiered storage and retention for call logs and recordingsRecordings pile up with no scalable retention modelStorage cost right-sized to call volume and compliance need
Lead ScoringRanks leads by engagement and fit signalsReps waste time on low-intent leadsPrioritised outreach and higher conversion
AI-Driven InsightsSurfaces patterns and anomalies automaticallyManual analysis misses subtle trendsAn unusual drop in connection rate flagged before it costs a month
Who this is for

Founder / Ops Manager

Wants answers from the business without becoming fluent in its dashboards.

Pain today
Every question requires a login, a filter and a report someone has to build.
What they want
Ask a question in plain English and get an answer from live data.
Features that deliver it
MCP Builder, AI Assistant Connectivity, Natural-Language Queries
Business value
Business data accessible from the tools the team already uses, all day.

Use cases

Three ways teams put AI & Integrations (MCP Builder) to work in week one.

Ask ChatGPT which branch is behind on collections

The assistant queries live receivables through MCP and returns the outlier branch with its aging profile: no dashboard navigation, no export.

Review fifty sales calls in the time it takes to hear three

AI call summarization turns each recording into a scannable summary, which is what makes call QA viable at telecalling volumes.

Surface this week's uncontacted leads conversationally

“Show me this week's uncontacted leads in Nagpur” is answered directly by the connected assistant, including SLA breach counts.

AI & Integrations (MCP Builder) FAQs

MCP stands for Model Context Protocol. An open standard for connecting AI assistants to external data and tools. MeraUdyog's MCP Builder lets an admin expose selected MeraUdyog data and actions (leads, employees, calls, attendance, invoices) as an MCP server, typically in an afternoon rather than a development project. Any MCP-compatible assistant can then read from and act on that data in natural language.

You build an MCP endpoint in the MCP Builder, choosing which objects and actions to expose, then add that endpoint to your assistant's MCP configuration. From there the assistant can answer questions such as “which branch is behind on collections this month?” or “show me this week's uncontacted leads” against live data. No bespoke connector and no intermediary integration platform is required.

Yes. AI Call Summarization generates a text summary of each recorded call automatically, so a manager can scan the substance of fifty calls in the time it would take to listen to three. Recordings themselves are retained under tiered call recording storage plans, priced by retention period and call volume.

Role-based access control, permission management and field-level rules govern the platform today. Extending that model into scoped, role-and-branch-aware permissions for each connected AI assistant is an explicit near-term roadmap priority: we treat it as a requirement of widening the external data-access surface, not an afterthought. Discuss your specific governance requirements with our team during evaluation.

Start with AI & Integrations (MCP Builder). Add the rest when you are ready.

Same data core, no migration, no implementation partner. Most teams are live within days.

No credit card required · Live in days, not months · 10,000+ leads processed monthly