Project 07 · Case Study
Aklara Nexus — Agentic AI for intelligent supply chain orchestration.
- Agentic Workflow Engineering
- Complex Data Processing
- Monitoring & Optimization
This project deploys an autonomous layer of specialized AI agents to modernize enterprise procurement, automating document extraction, vendor compliance grading, and the orchestration of complex corporate acquisition pipelines.
Client
Aklara
Industry
Supply Chain / B2B
Timeline
3-phase execution
Core Benefit
Monolith Evolution & Intelligent Automation
Description · 01 / Context
Evolving legacy procurement systems into autonomous workflows.
The Challenge
Overcoming rigid monolithic architectures and manual supplier validation.
The client needed to modernize a rigid ASP.NET procurement monolith that limited operational scaling. The challenge lay in migrating this legacy backend into an intelligent network to automate supplier orchestration, streamline unstructured document onboarding, and ensure strict corporate governance.
- Modernizing a legacy ASP.NET monolithic platform to support operational scaling.
- Eliminating manual bottlenecks in supplier onboarding and document ingestion.
- Overcoming inconsistent evaluation criteria to ensure objective purchasing decisions.
Our Approach
Multi-agent orchestration with human-in-the-loop governance.
The project was executed through an initial discovery phase to map measurable outcomes, followed by UX prototyping and technical deployment. The solution replaces manual workflows with autonomous AI Agents that manage, guide, and optimize procurement data while ensuring a human-in-the-loop validation layer for high level corporate decisions.
- Executing a product discovery phase focused on prioritizing measurable outcomes.
- Engineering an Agentic AI architecture with built-in vector database retrieval.
- Deploying a low code framework equipped with human-in-the-loop validation checkpoints.
Description · 02 / Technological Solution
A microservices ecosystem engineered for autonomous procurement.
System Components
Multi agent backend with vector retrieval.
We established a secure, decoupled microservices platform on GCP that orchestrates multi agent tasks, processes corporate documents natively, and integrates with relational and vector databases.
Autonomous Agent Orchestration Engine
A backend controller that manages specialized AI Skills and routes tasks between supply operators and suppliers, automating multi step validation processes.
RAG Document Processing Core
An intelligent ingestion system utilizing large language models and Vector Databases to extract, cross reference, and validate structured supplier data automatically.
Low Code Governance & Compliance
A modular microservices design featuring separate operator, supplier, and admin interfaces to enforce strict data compliance and rapid workflow customization.
Description · 03 / Business Impact
Intelligent supply orchestration and accelerated onboarding timelines.
The transition to an intelligent agent architecture drastically reduced operational friction, eliminated manual data aggregation, and provided full visibility over corporate acquisitions.
Low
Onboarding Time
Supplier data ingestion and profile creation are handled via automated extraction, minimizing platform setup delays.
Optimal
Process Consistency
Evaluation criteria became standardized through AI assisted data retrieval, ensuring objective supplier comparisons.
100%
Operational Traceability
Every action automated by AI Agents remains logged and backed by human-in-the-loop validation for complete governance.
Evolving the platform toward an Agentic AI model allowed us to automate, guide, and optimize our acquisition workflows while securing faster and fully traceable decisions.
Project Media
Product Views, Journeys and Assets
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