Loading…
Loading…
Snaplytics is a South African software company building DataLAB, a desktop-first analytics and machine learning platform with an early web MVP that is still evolving.
The company exists to give finance teams, auditors, analysts, and data-heavy business units a more practical environment for querying, transforming, validating, reconciling, and analyzing business data.

Many organizations still depend on spreadsheets, disconnected scripts, point tools, and manual review processes to answer important business questions. That is inefficient, fragile, and difficult to scale.
Snaplytics is building a more integrated path from raw business data to analysis, validation, reconciliation, and model-driven decision support. We started with DataLAB because it gives us a strong wedge in finance, audit, and analytics-heavy workflows.
DataLAB development begins in South Africa.
Core analytics, ML workflows, and pipeline foundations deepen into a working product base.
Financial testing depth, shared-storage capabilities, and licensing mature while an early web MVP begins taking shape.
The goal is not to look like another abstract software platform. The goal is to solve serious analytical work more cleanly.
We build around real analytical workflows, especially where spreadsheets, manual reconciliation, and tool sprawl slow teams down.
We want advanced analytics to be more reachable for finance teams, analysts, and data-heavy business units that are not staffed like engineering orgs.
We prefer disciplined product claims, visible technical depth, and software that reflects what it actually does rather than inflated marketing language.
Desktop-first delivery, offline-capable workflows, and flexible deployment matter for teams that care about control over data and operating environment.
Snaplytics is led by Aeneid Saranga, whose background spans data analytics consulting, technical delivery, and academic teaching in data systems. That matters because the product is being shaped by direct exposure to real reporting, audit, and analytical workflow problems rather than by generic software positioning alone.
DataLAB already has meaningful depth across analytics, machine learning, financial testing, pipelines, packaging, licensing, and shared storage patterns. The desktop surface is the most mature today, while the web experience remains an MVP and continues to evolve. The next major step is sharper pilot and design-partner validation.
Explore DataLAB, ask about pilots, or start a conversation about the workflows your team needs to improve.