Asthra AI LabsASTHRAAI Labs
Labs

Research, prototypes and future systems

Asthra Labs is where we explore ideas that may become internal tools, open research or future products. Consulting and engineering execution remain the firm’s primary focus.

Asthra MailGuard

Prototype

Privacy-conscious email intelligence using classification, confidence-aware routing, feedback and retraining workflows.

  • Text classification
  • Confidence gating
  • Feedback capture
  • Local-model options

Private exploration

Asthra Sage

Research

Persistent, local-first development assistance with project context, checkpoints, error memory and agentic execution.

  • Agent orchestration
  • Project memory
  • Checkpoints
  • Error recovery

Research prototype

VAT-Optimized

Research

High-performance implementations of Visual Assessment of Tendency methods for exploratory cluster analysis.

  • Clustering visualisation
  • Distance-matrix handling
  • Performance engineering
  • Scientific Python

Research engineering

Asthra MailGuard

Prototype

Problem. Email organisation tools often trade privacy for convenience and lack clear confidence handling when automation is uncertain.

Direction. Privacy-conscious email intelligence using classification, confidence-aware routing, feedback and retraining workflows.

  • Text classification
  • Confidence gating
  • Feedback capture
  • Local-model options
  • Retraining loops

Private exploration · Updated 2026-02-10

Asthra Sage

Research

Problem. Development assistance tools often lose project context, forget previous failures and struggle to recover from incomplete agent runs.

Direction. Persistent, local-first development assistance with project context, checkpoints, error memory and agentic execution.

  • Agent orchestration
  • Project memory
  • Checkpoints
  • Error recovery
  • Local-first tooling

Research prototype · Updated 2026-03-01

VAT-Optimized

Research

Problem. Visual Assessment of Tendency methods are analytically useful but can become computationally expensive for practical exploratory use.

Direction. High-performance implementations of Visual Assessment of Tendency methods for exploratory cluster analysis.

  • Clustering visualisation
  • Distance-matrix handling
  • Performance engineering
  • Scientific Python

Research engineering · Updated 2026-01-20

Next step

Have a difficult AI problem?

Bring the domain, constraints and current system. We will help determine what is feasible, what should be measured and what is worth building.

Initial conversations are exploratory and confidential.