Correctness changes with context
The right behavior depends on participant and journey context, live supply and demand, safety and fairness, policy, incentives, and exceptions. Generic scoring misses those interactions.
Solutions · Ride sharing & Food delivery
Marketplace AI built for the live, local, human edge cases
Make marketplace judgment testable across riders, drivers, couriers, merchants, and operations.
A decision that helps one side of a marketplace can harm another. Teammately helps specialists define balanced behavior across safety, fairness, service, incentives, and local operating context.
The domain reality
Generic quality is not enough for consequential ride sharing & food delivery behavior.
The standard lives across specialist teams
Marketplace operations, Trust and safety, Customer and partner support, Local policy and compliance each hold part of the judgment the system needs to behave well.
Real work is full of exceptions
A benchmark must represent edge cases, uncertainty, conflicting goals, and escalation—not only the most common ride sharing & food delivery path.
Priority workflows
Start where expert judgment materially changes the answer.
Support agents
Test diagnosis, policy application, empathy, remedies, and escalation across participants.
Safety operations
Benchmark triage, evidence handling, boundaries, and urgent handoffs.
Marketplace copilots
Evaluate tradeoffs and recommendations across service, fairness, and network health.
Merchant and courier tools
Keep assistance grounded in local operations and participant context.
Correctness blueprint
Connect specialist judgment to the benchmark and the build.
Your experts define
- Marketplace operations
- Trust and safety
- Customer and partner support
- Local policy and compliance
The benchmark covers
- Participant and journey context
- Live supply and demand
- Safety and fairness
- Policy, incentives, and exceptions
Your AI team receives
- A deliberate coverage map
- Explicit policies and binary rubrics
- Targeted cases, variants, and exceptions
- Inspectable evaluation and improvement evidence
One connected workflow
Five product capabilities, applied to one domain standard.
Coverage Engineering
Design the combinations of participant and journey context, live supply and demand, safety and fairness, policy, incentives, and exceptions the benchmark must represent.
Correctness Elicitation
Turn judgment from marketplace operations, trust and safety, customer and partner support, local policy and compliance into policies, applicability conditions, and binary rubrics.
Weave
Create targeted ride sharing & food delivery cases, variants, artifacts, and worlds from the coverage plan.
Trialground
Run candidate models and agents in controlled environments and preserve the behavior-level evidence.
Coevolve
Explore parallel improvement directions and return newly discovered gaps to the right specialists.
What good looks like
A development team that can improve behavior without losing domain intent.
Coverage you can defend
Know which ride sharing & food delivery conditions, exceptions, and risks the benchmark represents—and which it does not.
Judgment that scales
Reuse every specialist decision across policies, rubrics, evaluation, and future cases.
Evidence for every iteration
Compare model, prompt, harness, and agent changes against the same domain-grounded standard.
A controlled learning loop
Route unresolved questions and newly discovered gaps back to accountable experts.
Teammately evaluates marketplace AI behavior; safety interventions, payments, and consequential account actions remain subject to authorized controls.
Build a benchmark around how your ride sharing & food delivery experts actually judge quality.
Start with a consequential workflow and the specialists already accountable for it. Teammately turns their judgment into reusable development infrastructure.