AMove
Enterprise & Business Software · File & Knowledge Management
From product reliability to AI knowledge retrieval inside a live cloud product
AMove was a longitudinal engagement inside an existing cloud and file-management product. Datics first worked on product reliability and regression coverage, then helped introduce AI ingestion, vectorization, retrieval and Slack-based workflow capability over knowledge stored across cloud services.
The starting point
An existing desktop and web product operating across multiple cloud-storage environments.
What had to change
Critical paths needed reliable quality coverage, and later users needed access to knowledge held across those cloud sources.
What couldn't break
Roadmap and product delivery could not stop while the work happened.
Why this was hard
Accumulated product complexity had to be brought under control first, and the later retrieval work had to reconcile heterogeneous cloud access and data models.
What Datics changed
Datics strengthened the reliability foundation first, then added AI ingestion, vector retrieval and a Slack workflow surface on top of it.
Reliability foundation
Structured quality and regression coverage across critical paths.
AI ingestion & vectorization
Knowledge ingested and vectorized for retrieval.
Cross-cloud retrieval
Retrieval across knowledge held in different cloud services.
Slack automation
Retrieval and workflow made available inside Slack.
What changed
A more dependable product, and knowledge held across different cloud services retrievable through the workflows people already use.
- Delivery model
- Reliability added without freezing delivery
- Second phase
- AI retrieval introduced later
- Access surface
- Slack as the workflow surface
Under the hood
- Datics role
- Quality engineering, then AI product engineering.
- Integrations
- Cloud storage services and Slack.
- Deployment
- AWS, inside the customer's production environment.
- Production boundary
- Customer's production cloud environment.
Technology
What this unlocked
A more dependable product foundation became the base for knowledge retrieval and workflow automation rather than requiring AI to be built around unresolved product instability.
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