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Challenge

Build a Delegated Voice-Commerce Action Safety Reviewer

Analyze voice assistant transaction execution traces against registered user consent policies. Detect prompt injection, unintended purchases, and voice authorization mismatches.

Workflow AutomationHosted by Vera
Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Analyze voice assistant transaction execution traces against registered user consent policies. Detect prompt injection, unintended purchases, and voice authorization mismatches.

E-Commerce Consumer Safety Officer

Capture raw intent parse outputs from vernacular voice ordering sessions.

Reconcile requested cart actions against the user's active delegation and consent envelope.

Evaluate transcript for potential acoustic ambiguity or coerced command indicators.

Produce risk-scored transaction confirmation tickets requiring secondary tactile approval.

Safety operations queue showing flagged voice transactions, audio confidence scores, and consent scopes.

Generate synthetic voice order intent logs with varying levels of ambiguity and delegation scopes. Starter examples below are synthetic. They are not downloaded records.

Bhashini documentation Utilizes Bhashini ASR pipeline intent representations to assess vernacular speech transaction safety.

dspy, pydantic, fastapi. Optimizes verification prompts to assess command confidence and detect ambiguous speech intent. Local or hosted LLM with strong few-shot logical inference capabilities.

Submit runnable source, pinned dependencies, setup commands, a working interface, example outputs, and a recorded end-to-end demo. Include tests and an action log.

Identifies ambiguous voice commands where product variant or quantity is doubtful.

Cross-checks action parameters against delegated user permission boundaries.

Triggers step-up confirmation whenever confidence falls below established thresholds.

Emits structured safety audit receipts for each processed voice interaction.

```json [ { "input": { "session_id": "VCE-901", "asr_confidence": 0.94, "parsed_intent": "order_item", "item_name": "Basmati Rice 5kg", "consent_limit_inr": 1000, "order_value_inr": 650 }, "expected_output": { "action": "AUTO_APPROVE", "risk_score": 0.05, "requires_secondary_confirmation": false, "reason": "Clear intent, high confidence, within consent spend limits" } }, { "input": { "session_id": "VCE-902", "asr_confidence": 0.61, "parsed_intent": "order_item", "item_name": "Gold Coin 10g", "consent_limit_inr": 500, "order_value_inr": 65000 }, "expected_output": { "action": "HOLD_FOR_CONFIRMATION", "risk_score": 0.95, "requires_secondary_confirmation": true, "reason": "Low acoustic confidence and transaction exceeds consent limit by more than 100x" } } ] ```

Evaluates mock purchasing prompts; all financial transactions are simulated and non-binding. Use mock external writes. Require human review before any real deployment.

Rules

Participation guide

Evaluates mock purchasing prompts; all financial transactions are simulated and non-binding. Use mock external writes and human review.

Delivery guide

How work is evaluated

Evaluation

Identifies ambiguous voice commands where product variant or quantity is doubtful. Cross-checks action parameters against delegated user permission boundaries. Triggers step-up confirmation whenever confidence falls below established thresholds. Emits structured safety audit receipts for each processed voice interaction.

Submission format

Runnable source, pinned dependencies, setup commands, working interface, example outputs, recorded demo, tests, and action logs.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
4 scoring checks
Binary
1 pass or fail dimensions
Ordinal
3 scaled dimensions
Dimension 1

Complete workflow

Capture raw intent parse outputs from vernacular voice ordering sessions. Reconcile requested cart actions against the user's active delegation and consent envelope. Evaluate transcript for potential acoustic ambiguity or coerced command indicators. Produce risk-scored transaction confirmation tickets requiring secondary tactile approval.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 2

Correctness and evidence

Identifies ambiguous voice commands where product variant or quantity is doubtful. Cross-checks action parameters against delegated user permission boundaries. Triggers step-up confirmation whenever confidence falls below established thresholds. Emits structured safety audit receipts for each processed voice interaction.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 3

Review interface

Safety operations queue showing flagged voice transactions, audio confidence scores, and consent scopes.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 4

Safe execution

Evaluates mock purchasing prompts; all financial transactions are simulated and non-binding.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Capture raw intent parse outputs from vernacular voice ordering sessions.

  • Reconcile requested cart actions against the user's active delegation and consent envelope.

  • Evaluate transcript for potential acoustic ambiguity or coerced command indicators.

  • Produce risk-scored transaction confirmation tickets requiring secondary tactile approval.

Resources and assets

Reference links and supporting material

Dataset notes

Generate synthetic voice order intent logs with varying levels of ambiguity and delegation scopes. Supplied starter examples are synthetic, not source extracts.

Challenge setup

Configuration summary for this challenge.

Challenge input

Input details are not configured.

DSPy

Prompt and LM program optimization framework.

Evaluated output

No output checks are configured.

Evaluation checks
  • Capture raw intent parse outputs from vernacular voice ordering sessions. Reconcile requested cart actions...
  • Identifies ambiguous voice commands where product variant or quantity is doubtful. Cross-checks action para...
  • Safety operations queue showing flagged voice transactions, audio confidence scores, and consent scopes.
Configured evaluation evidence
  • 2 public reference cases
Configured runtime
  • 3 configured tools

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • DSPy · Optional
Training Infra
  • DSPy · Optional
Orchestration
  • DSPy · Optional

Evaluation contract

  • Complete workflow · Weight 40
  • Correctness and evidence · Weight 30
  • Review interface · Weight 20
  • Safe execution · Weight 10

Reference cases

  • 2 public reference cases

Workflow tasks

No workflow tasks are configured.

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Build a Delegated Voice-Commerce Action Safety Reviewer