AI that works on governed data. Workflows that keep people in charge.

AI is only as good as the data beneath it. On HASH, assistants, models and approvals all draw from the governed lake — and every action leaves a trail.

AI assistants

Ask questions of your governed data.

Answers come from agreed definitions, not from guesswork — and show where they came from.

  • Governed sources only. Assistants read gold data, using the caller’s own permissions.
  • Shows its working. Every answer names the dataset, the definition used and the full trace.
  • Your languages. Questions can be asked in English and in regional languages.
  • Writes need a human. Any action that changes data waits for an approver who is not the requester.
AI Assistant · Governed data
Synthetic data
AskRunsGuardrailsAudit trail
Which datasets are ready to train a defect-detection model?
3 datasets qualify — all gold layer, AI-ready, permitted for this purpose:
  • Field survey records · quality 94
  • Asset register · quality 98
  • Imagery tiles · quality 96
Gold · Asset registerGold · Survey recordsLineage trace
◆Ask a question about your governed data…
Guardrails
✓Reads governed gold data only
✓Runs with the caller’s own permissions
✓Every prompt, tool call and output logged
✓Any write waits for a different approver
Human in the loop

Models improve under supervision.

Low-confidence AI results never go straight to decisions. They wait at a review gate where people decide.

  • Review gate. Detections below a confidence threshold are queued for a person to accept, correct or reject.
  • Feedback becomes training data. Corrections are versioned into the next training set.
  • No leakage. Training sets are split by location, so a model is never tested where it learned.
  • AI-ready means governed. Only datasets marked AI-ready are offered to learning pipelines.
Human-in-the-loop · Review gate
Synthetic data
QueueModelsTraining sets
Review queue 12 waiting
crack · 0.62
Surface crack · confidence 0.62 · below threshold
AcceptCorrectReject
Feedback loop
Reviewed today
48 accepted · 9 corrected · 3 rejected
Becomes training data
Corrections are versioned into the next training set
Leakage control
Sets are split by location, so a model is never tested where it learned
Workflow

Stage-gated, with maker and checker.

Work moves forward only when the evidence is in place. Rules are applied by an engine, not remembered by people.

  • Gates that hold. A stage advances only when the required documents and approvals exist.
  • Maker–checker approvals. Preparation, checking and approval are separate roles.
  • Rules with a source. Each rule shows its legal basis, marked verified, secondary or assumed.
  • Clocks and escalation. Service levels, deadlines and escalation run on the same record.
Workflow · Stage-gated board
Synthetic data
BoardRulesSLAsAudit trail
Requisition
Item 0142Prepared by maker
Item 0145Prepared by maker
Checked
Item 0139Cannot advance yet
🔒 4 of 5 documents
Item 0140Checker: rules passed
Rules passed
Approved
Item 0136Approver sign-off
Signed
Published
Item 0131Notice issued
Complete
A stage advances only when required documents and approvals are in place. Every step is logged.
Applications on the platform

Ready-made workflows, built on the same foundation.

Each uses the same catalogue, governance and consent layers, so none of them starts from scratch.

e-KYC Platform →

Multi-tenant digital onboarding with policy engine, ledger and audit.

Banking on the Go →

Assisted, offline-capable banking for co-operative and rural banks.

e-Stamping

Digitised contract stamping and execution.

Tender & contract intelligence

AI analysis of tenders, with a stage-gated contract lifecycle.

Asset condition monitoring

Survey, detection and defect tickets for linear assets such as roads.

Data portability

Move data between systems and agencies under agreed, logged terms.

See where AI can safely help.

Bring one workflow you would like to improve. We will show how it would run on governed data.

Talk to usSee our approach