Frankly, my dear, we've got a better DAM

The Challenge

Kablamo observed ongoing customer frustration with existing Digital Asset Management software offerings: large up-front license costs, overselling of basic features, inflexible storage solutions and "feature-waste." So Kablamo committed to developing something better: a 100% AWS-native digital asset management solution designed as a centralised platform for enterprises to store, enrich, search, and distribute digital assets.

Traditional DAM platforms suffer from common frustrations: they try to do everything, become bloated with unused features, and lock customers into expensive licensing models. Implementation takes months or years, requires domain-specific languages, and creates vendor lock-in. Organisations that outgrow their initial DAM setup often face a painful migration to a new vendor, losing metadata and workflow configurations in the process.

Kablamo identified three target markets. First, streaming and broadcast: consolidating multiple DAM/MAM systems, digitising physical assets, enriching with metadata, and distributing through many channels with licensing compliance. Second, corporate enterprises: centralising data types from many years of cataloguing including paper, video, streaming, and digital marketing assets. Third, law enforcement: aggregating digital and physical media from streaming video, body cameras, drones, tapes, and paper, with digitisation, normalisation, role-based security, and AI metadata enrichment. Each of these markets shares a common pain point: assets trapped in silos, searched manually, and distributed through brittle, hand-wired integrations that break whenever a downstream system changes.

The Approach

DAME was built as a 100% transparent (white box) solution using AWS best practices, lean and extensible across AWS cloud applications. The React front end was paired with serverless technology for performance and low costs. The UI is white-label and customisable to enterprise branding. The platform is PII and GDPR compliant with CI/CD and test automation.

Core capabilities include a central repository for all digital files (videos, images, documents); RESTful serverless APIs to aggregate from existing repositories; normalisation via AWS MediaConvert to transcode to consistent formats while maintaining originals; automated metadata creation and tagging for structure and enrichment; distribution to push assets to CMS, VMS, OTT, OVP, or any platform via APIs; bulk storage with S3 and Glacier (currently storing petabytes across customers); and context-specific workflows customisable for media request and fulfillment, approval, video editing, transcription editing, language conversion, and facial detection. The platform can also run in headless mode, completely without a UI, with all workflows via RESTful APIs.

The architecture separates compute from storage so that ingestion, transcoding, and ML enrichment run independently and scale on demand. When a new asset is uploaded, an event pipeline triggers the relevant processing steps: MediaConvert generates preview renditions, Rekognition scans for faces and objects, Transcribe produces a text track for audio and video files, and Comprehend extracts key phrases and sentiment. All generated metadata lands in a search index within seconds, making the asset discoverable before the operator has closed the upload dialog. Because each ML step is an independent Lambda function behind a feature flag, customers can enable only the enrichments they need and add others later without re-deploying the platform.

The AWS services include S3 for centralised storage with 11 nines durability, Glacier for long-term archival, MediaConvert for transcoding, Rekognition for facial and object detection, Transcribe for speech-to-text, Comprehend for text analysis and object detection, Textract for document transcription, AppSync for API distribution, and Cognito with Auth0 as authentication options.

Key differentiators over competitors: implementation measured in days or weeks instead of months or years; very low cost to start with enterprise-capable DAM; no vendor lock-in thanks to common market skills (React, AWS) and no domain-specific languages; built-in expansion and integration as a primary feature; designed to scale from small teams to enterprise organisations without re-architecting; and full CI/CD with no manual chokepoints. Because every component communicates through well-documented REST endpoints, customer engineering teams can integrate DAME into their existing toolchain without learning a proprietary SDK.

The Results

The pre-built software platform enables Kablamo to accelerate time to market and reduce costs for customers' digital product development journeys. DAME supports files up to 5TB, with modular ML-powered tagging and analysis and enterprise-grade security and governance. The platform is currently storing petabytes of data across customers and scales from small teams to millions of users without architectural changes.

For broadcast customers, DAME replaced multiple legacy MAM systems with a single searchable catalogue, cutting the time needed to locate and clear an archive clip from hours to seconds. Corporate enterprise customers consolidated decades of marketing collateral, legal documents, and training videos into one governed repository with consistent access controls. In the law enforcement space, DAME's role-based security model ensures that sensitive evidentiary material is accessible only to authorised personnel, with a full audit trail of every view, download, and share event. Across all three sectors, customers reported that onboarding new teams onto the platform took days rather than the weeks or months typical of competing products.

Looking Forward

DAME continues to evolve with ML features tracked and developed across customers in streaming, broadcast, corporate enterprise, and law enforcement sectors.

The roadmap includes expanded ML capabilities such as automatic content moderation, deeper video scene segmentation, and multi-language transcription support. As AWS releases new AI services, DAME's modular plugin architecture means they can be wired in behind the same feature-flag system without touching the core platform. The goal remains straightforward: give organisations a DAM that grows with them instead of one they will need to replace in three years.