Correctness changes with context
The right behavior depends on shipment and service level, capacity and route state, cost and time tradeoffs, exceptions and downstream effects. Generic scoring misses those interactions.
Solutions · Logistics
Logistics AI that handles the exception, not only the happy path
Benchmark decisions against network reality, operating constraints, and customer commitments.
Logistics performance depends on interacting constraints and constant exceptions. Teammately helps operators specify the cases, tradeoffs, and escalation decisions AI must handle correctly.
The domain reality
Generic quality is not enough for consequential logistics behavior.
The standard lives across specialist teams
Network planning, Dispatch operations, Exception management, Customer commitments 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 logistics path.
Priority workflows
Start where expert judgment materially changes the answer.
Planning copilots
Test recommendations across capacity, service, cost, and network constraints.
Exception agents
Benchmark diagnosis, option generation, escalation, and customer impact.
Dispatch assistance
Evaluate time-sensitive actions against local operating rules and live conditions.
Customer operations
Keep commitments and explanations consistent with operational truth.
Correctness blueprint
Connect specialist judgment to the benchmark and the build.
Your experts define
- Network planning
- Dispatch operations
- Exception management
- Customer commitments
The benchmark covers
- Shipment and service level
- Capacity and route state
- Cost and time tradeoffs
- Exceptions and downstream effects
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 shipment and service level, capacity and route state, cost and time tradeoffs, exceptions and downstream effects the benchmark must represent.
Correctness Elicitation
Turn judgment from network planning, dispatch operations, exception management, customer commitments into policies, applicability conditions, and binary rubrics.
Weave
Create targeted logistics 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 logistics 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 supports evaluation of logistics decisions; authoritative planning, safety, and execution systems remain the source of operational control.
Build a benchmark around how your logistics experts actually judge quality.
Start with a consequential workflow and the specialists already accountable for it. Teammately turns their judgment into reusable development infrastructure.