Enosis Outsourcing logo
Free Consultation
EndorsedendorsedOngoing

AI Document Classification & Auto-Indexing for ArchiveIT

Services Covered:

AI and Machine Learning • Custom Software Development
Project hero — replace with case study imagery
Industry
Industry

Business Consulting & Services

Duration
Durationinfo-circle

3 months

Budget
Budget

Confidential

Start Date
Start Date

1 July 2025

Team Size
Team Size

1-5

Engagement Model
Engagement Model

Offshore

Project Capability Score (PCS)

gauge meter
86/100
Strong

Project Capability Score (PCS) estimates how capable Armakuni 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

ArchiveIT

mapLos Angeles, United States of America

Project Summary

Armakuni reviewed a SaaS enterprise content management system for a document processing company and implemented AI classification, auto-indexing, and auto-routing to parse and route documents to S3 buckets. The vendor parsed multi-page PDFs to the document level and delivered the requested features. The team was responsive, timely, cohesive, and is now working to bring the project into production.

Key Challenges

  • Move existing inbox into an AI classification and autofiling tool
  • Extract key data from documents to enable auto routing
  • Build an ongoing secure archive with private LLM capabilities

Project Deliverables

  • AI classification and auto-indexing engine
  • Auto-routing to appropriate S3 buckets
  • Review and enhancement of existing SaaS ECM intake engine

Project Solution

Armakuni reviewed the client's existing SaaS ECM intake engine and implemented AI-driven classification, auto-indexing, and auto-routing so inbound documents are parsed, classified, indexed, and routed to appropriate S3 buckets. The system was built to learn from the client's dataset over time and reduce manual filing and indexing. The deliverables were completed and the team is working on bringing the solution into production.

Project Outcome

  • Parsed multi-page PDFs into individual document-level items
  • Classified, indexed, and routed documents to appropriate S3 buckets
  • Delivered work on time with responsive project management and cohesive team collaboration

Platforms

  • CloudCloud
  • SaaSSaaS

Tech Stack

  • Amazon Web Services (AWS)Amazon Web Services (AWS)
  • S3S3

Client Endorsement

Overall Review Rating

5star

Timeliness

Cost Rating

Willing to Refer

Quality of Deliverables

Documents are now classified, indexed, and sent to the right S3 buckets, all thanks to Armakuni, and it's exactly what we wanted. They've been very attentive, quick to answer questions and concerns, and on time with deliverables. How well they work together and how professional they are stand out.

Guy Puckett

President

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

More Projects by Armakuni

See All Projects by Armakuni