THE SITUATION
The client’s dataset and model teams were operating in silos, with no unified platform for collaboration. Dataset creation and model management ran as separate workflows, with no shared system for governance, versioning, or access control.
Without a centralized platform, teams couldn’t work from a common source of truth. There was no consistent way to track dataset versions, manage permissions, or apply data protection standards across both functions, leaving the broader AI initiative without the foundation it needed to scale.
THE SOLUTION
GrowthArc approached this as an architecture problem first. The goal was a system that handled dataset creation, processing, and governance as one connected flow, not separate tools stitched together.
The engagement was sequenced around getting a secure, automated foundation in place first, then integrating it with the client’s existing governance framework. This ensured the new platform inherited the access controls, logging, and deployment standards the client already relied on, rather than introducing a parallel system to manage.
WHAT WE BUILT
GrowthArc built a unified platform for dataset and model creation, using cloud-native infrastructure to automate processing and enforce consistent governance. The platform supports multiple data formats and gives users flexibility in how datasets are structured and split for training.
Comprehensive versioning was built into the metadata layer, giving teams clear visibility into dataset lineage over time. The platform was integrated directly with the client’s existing internal framework, so governance, access controls, and deployment pipelines stayed consistent with how the client already operates, rather than adding a separate system to manage.
THE OUTCOME
The client moved from siloed, manual dataset and model workflows to a single governed platform with built-in versioning and access controls. An added benefit was improved historical data retrieval speed, a result that fell outside the original scope.
Beyond the platform itself, teams that previously worked in disconnected silos are now collaborating on shared infrastructure. The architecture has already started accommodating new use cases beyond what it was originally built for, a sign the foundation was built to last.
60
Days Delivery Timeline
How long it took to go from kickoff to a fully operational backend, despite a compressed schedule.
1.5M+
GBs Data Management
Total data volume the platform now handles across CSV, JSON, Parquet, ZIP, and TAR inputs.
100%
Versioning & History
Built an efficient versioning system that tracks dataset history and enables quick retrieval of past versions.
FUTURE OUTLOOK
The platform was built with scalability in mind from day one, designed to accommodate larger datasets, advanced versioning features, or additional third-party tools as the client’s AI initiatives grow.
