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AI agent system for managing a franchise network
Franchise network · 2025LangGraph · OpenAI · Python · Postgres

AIagentsystemformanagingafranchisenetwork

For a 180-location network we deployed a system of 12 AI agents covering sales, support, procurement and marketing — to cut back-office load and bind the processes into a single, manageable model.

Brief

A 180-location franchise network. Back-office headcount grew faster than revenue and standards drifted between regions. They didn't need "task automation" — they needed a system that holds whole processes end-to-end and doesn't depend on the person on a given site. We shipped an agentic OS: 12 agents, one orchestrator, one panel.

Context

The problem

40 people in the back-office. 70% of their time goes on routine: reconciliations, supplier email threads, report assembly.

Constraints

No breaking existing 1C and SAP integrations. Security — inside the corporate perimeter.

Goal

Cut manual operations 60%+ with no layoffs: free people up for negotiations and growth.

Before → After

Manual operations in the back-office

Before launch
  • 40 people in the back-office
  • 70% of their time on routine and reconciliations
  • Reports stitched by hand over 3 days
  • Every uptick in volume = a new hire
6 weeks
After 6 weeks
  • +40 people working on growth, not routine
  • +12 agents handle the routine
  • +A report assembles in 10 minutes
  • +Growth without new hires
Project timeline

Shipped in 6 weeks

From first call to first production launch. No phase two, no endless iterations.

Week 1—2

Discovery

Mapped 18 processes for agents. Locked metrics and baseline.

Week 3

Design

Architecture: 1 orchestrator + 12 agents. Roles and SLAs defined.

Week 4—5

Build

Built agents and connectors to 1C, SAP, email. Tested on live data.

Week 6

Launch

Shipped to production. Handed the KPI panel to the client team.

What we did

How we built it

01

Discovery

In 2 weeks we identified 18 processes an agent closes faster than a human.

02

Orchestrator

One LangGraph "brain" routes tasks across 12 agents. Every action logged.

03

Integrations

Direct connectors to 1C, SAP, email, Telegram. No middleware, no vendor lock-in.

04

KPI panel

One metric — share of manual operations. The drop is visible in real time.

Orchestrator and agents

The orchestrator reads context, picks an agent, verifies the result. Humans handle exceptions.

01Incoming task — orchestrator picks the agent
02Agent closes the task — automatic log
03KPI panel — manual operations in real time

Solution architecture

One LangGraph orchestrator, 12 narrowly specialised agents, direct connectors to existing systems. No middleware, no vendor lock-in.

01LangGraph orchestrator — 1 brain, 12 agents, shared memory
02Connectors to 1C, SAP, email, Telegram — inside the corporate perimeter
03Every action logged — full audit trail
04Human-in-the-loop — the agent asks only on exceptions
Results

What came out

−68%

fewer manual operations

12

AI agents under one system

6 weeks

from contract to first launch

3× ROI

in the first quarter

“We didn't fire anyone. We just stopped hiring. The back-office didn't grow for two quarters — and revenue went up 40%.”

Maria Goncharova

COO, franchise network

Project team
IG

Igor Golikov

Lead

VK

Vitaly Kust

Tech

JP

James Park

AI

MG

Mariia Golikova

Design

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