EndorsedendorsedCompleted

Enterprise Generative AI Integration & MLOps for Automotive Services Provider

Services Covered:

AI and Machine LearningCloud Consulting and ServicesApplication Integration
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Industry
Industry

Automotive

Duration
Duration

5 months

Budget
Budget

200K - 999K

Client Size
Start Date

1 April 2025

Team Size
Team Size

1-5 Employees

Engagement Model
Engagement Model

Offshore

Project Capability Score (PCS)

gauge meter
80/100
Strong

Project Capability Score (PCS) estimates how capable Prakash Software Solutions Pvt. Ltd is of successfully delivering a project like this, based on its past experience, track record, and ability to handle similar work. Learn more about PCS


Client

Cox Automotive Inc.

mapAtlanta, United States of America

Project Summary

Prakash Software Solutions provided enterprise AI consulting and integration for an automotive services provider, building scalable LLM pipelines on Azure OpenAI and automating document-processing workflows. The engagement included MLOps implementation, API integrations, model fine-tuning, and monitoring systems, improving extraction and routing turnaround and reducing manual intervention. The vendor used an agile approach and maintained strong project management and collaboration throughout.

Key Challenges

  • Enterprise generative AI integration and workflow automation
  • MLOps implementation and infrastructure scalability
  • Data governance and security framework alignment

Project Deliverables

  • Secure MLOps framework
  • Scalable LLM pipelines built using Azure OpenAI
  • Automated complex document processing workflows
  • API integrations, prompt engineering, and model fine-tuning
  • MLOps monitoring systems and performance tracking
  • Post-launch support and iterative model adjustments

Project Solution

The vendor designed a secure MLOps framework and built scalable Large Language Model pipelines using Azure OpenAI, integrating them into the client's cloud infrastructure. Work included AI consulting and strategy alignment, API connections, prompt engineering, model fine-tuning, and implementation of monitoring systems to track model performance. They ensured data governance and privacy protocols were maintained, executed rigorous testing, and provided post-launch support to enable scalable, low-latency AI operations.

Project Outcome

  • Reduced data-extraction and document-routing turnaround from 48 hours to approximately 30 minutes
  • Decreased manual back-office intervention by more than 40%
  • Maintained low-latency handling of large request volumes while preserving data privacy
  • Rapid development and production timeline demonstrating strong ROI and operational efficiency gains

Platforms

  • CloudCloud

Tech Stack

  • AzureAzure
  • PythonPython
  • .NET.NET

Client Endorsement

Overall Review Rating

5star5 out of 5 stars

Timeliness

Cost Rating

Willing to Refer

Quality of Deliverables

Prakash Software Solutions' work significantly improved operational efficiency, reducing the turnaround time for data extraction and document routing by more than 40%. The team utilized a structured, agile development methodology and was highly responsive and collaborative.

Josh Horton

Dir & Head of Data

Note: This endorsement is based on publicly available client feedback from external review sources.

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