Logo

Custom ERP System with AI Integrations

Industry:
B2B
Type:
Artificial Intelligence Internal ERP
Timeline:
In development
Platform:
Web

An operational management tool built on custom architecture and AI-powered decision support modules

[client]

/01

The client is a company with a complex hierarchy, multiple levels of responsibility, and flexible decision-making logic.

Standard ERP systems did not allow implementing the required approval chain, formalizing routes, or ensuring execution control.

A solution was needed that accounts for the internal structure, supports adaptive scenarios, and is complemented by an intelligent layer to improve manageability.

[product features]

/02
End-to-End Process Management

End-to-End Process Management

Routes, roles, statuses, approvals, and execution control are unified in a single system. All information is structured, all actions are tracked.

Intelligent User Support

Intelligent User Support

The AI module analyzes task states, recommends actions, forecasts risks and deviations. Integration is implemented at the interface and business logic level.

Business Logic Adaptation

Business Logic Adaptation

The system does not require fitting processes to templates. Instead, the architecture is shaped based on the current organizational model and inter-department interaction scenarios.

[our responsibility]

/03
Business process analysis
Architecture of roles, routes, permissions, and entities
Interface design
Backend, frontend, and AI integration development
Post-implementation support and maintenance

[the client had]

/04
An existing organizational structure and set of regulations
A list of target scenarios
A description of current tools' issues
Requirements for access control and permission levels
ERP AI dashboard

[team]

/05
1 Product Manager
2 UX/UI Designer
3 Backend Engineer
4 Frontend Engineer
5 AI Engineer
6 QA Specialist
[tech stack]
/06
React
React
Next.js
Next.js
TypeScript
TypeScript
OpenAI API
OpenAI API
Python
Python
FastAPI
FastAPI
PostgreSQL
PostgreSQL
Redis
Redis
Docker
Docker

[key features]

/07

Decision Support (AI)

The AI module identifies bottlenecks, suggests optimizations, and explains the reasons for deviations and delays

Route and Status Builder

Flexible approval route modeling with conditions, triggers, and visual diagrams

Context-Based Access Control

A role model that accounts for departments, task types, work phases, and nesting

SLA Breach and Failure Prediction

Based on history and workload, the team receives predictive signals for tasks

Template Operation Automation

Template recommendations when recurring actions are detected in the team's workflow

Dashboards and Analytics by Responsibility Areas

Multiple overview levels: employee, manager, department, entire system

[Development process]

/08

Studying the business and designing scenarios

[01]

Understanding how the client enters your process and what steps they need to go through.

[02]

Identifying key touchpoints: first contact, registration, order, payment.

[03]

Determining which steps can be automated, where logic is needed, and where follow-up is required.

[04]

Building the structure of the future funnel — from entry to result.

[technical challenges and solutions]

/09
01
Challenge

Implement permissions across three levels (role, department, entity type)

Solution

Custom ACL module with caching and flexible configuration

02
Challenge

Improve relevance of AI hints and reactions

Solution

Building an intermediate layer with enriched context — routes, roles, statuses, task parameters

03
Challenge

Prevent "stuck" tasks and implicit blockers

Solution

AI generates text comments explaining reasons, suggests actions (e.g., "To proceed, you need to...")

[results]

/10
  • Reduction in operational workload
    40 %
  • time for standard scenario execution
    35 %
  • SLA compliance accuracy
    60 %
  • Increased process transparency within teams

    Confirmed by post-implementation research

Next project Automation of a country estate