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EndorsedendorsedOngoing

Platform Modernization & AI Development for NatureFootage

Services Covered:

AI and Machine Learning • Custom Software Development • Legacy Application Modernization
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Industry
Industry

Media

Duration
Duration

8 months

Budget
Budget

200K - 999K

Start Date
Start Date

1 November 2025

Team Size
Team Size

6-10

Engagement Model
Engagement Model

Onshore

Project Capability Score (PCS)

gauge meter
78/100
Good

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

NatureFootage

mapMonterey, United States of America

Project Summary

Armakuni is rebuilding NatureFootage's legacy platform into an AWS-native, AI-powered Media Asset Management (MAM) SaaS product, providing architecture design and AI pipeline development. The engagement includes serverless architecture design, AI-driven metadata and species identification workflows, and GPU-based transcoding and storage proposals. Armakuni has delivered a robust, cost-effective transcoding solution architecture, a demo site for pipeline evaluation, and exceeded expectations on website and business-logic implementation; the project is still in active development.

Key Challenges

  • Rebuild legacy platform into a modular AWS-native AI-powered MAM SaaS
  • Preserve value of existing clip catalog while replacing aging infrastructure
  • Achieve AWS Marketplace readiness and secure MAP/EBA funding for a scalable SaaS launch
  • Drive long-term AWS infrastructure spend growth

Project Deliverables

  • Serverless Bedrock-based architecture blueprint
  • AI-powered metadata and species identification pipeline
  • GPU-based video transcoding and S3 Intelligent Tiering storage design
  • RFP documentation and AWS Marketplace deployment specifications
  • Contributor upload workflows (Dropbox integration)

Project Solution

Armakuni rebuilt NatureFootage’s legacy stock-footage platform into a modular, AWS-native, AI-powered MAM SaaS design by producing RFP documentation, a serverless Bedrock-based architecture blueprint, and implementation planning for production. They designed AI-powered metadata generation and species-identification workflows (using AWS models) and a hybrid semantic search approach, and proposed a GPU-based video transcoding architecture with S3 Intelligent Tiering for master/proxy storage. The team also specified AWS Marketplace SaaS deployment requirements, contributor upload workflows, and participated in ongoing architecture reviews and technical governance.

Project Outcome

  • Project is ongoing and in active development
  • Delivered a robust, cost-effective, fault-tolerant transcoding solution architecture
  • Built a demo site to evaluate pipeline results with successful testing so far
  • Website design and business-logic implementations exceeded expectations
  • Established thorough project management with timely delivery and high responsiveness

Platforms

  • CloudCloud
  • SaaSSaaS

Tech Stack

  • Amazon Web Services (AWS)Amazon Web Services (AWS)
  • PostgreSQLPostgreSQL
  • Amazon DynamoDBAmazon DynamoDB
  • S3S3

Client Endorsement

Overall Review Rating

5star

Timeliness

Cost Rating

Willing to Refer

Quality of Deliverables

“Armakuni has delivered a robust, cost-effective, and fault-tolerant transcoding solution architecture. The team's project management is thorough, organized, and communicative, and they're highly responsive, adapting designs quickly. Armakuni's competitive pricing and deep AWS expertise stand out.”

Dan Baron

CTO

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

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