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Challenge

Build an AI SDK ONDC Order Dispatch Agent on Groq Cloud

Open Network for Digital Commerce (ONDC) logistics routing requires millisecond-level routing decisions during peak demand surges. Build a high-speed dispatch management agent using Vercel AI SDK and Groq Cloud fast inference to dynamically negotiate delivery routes and achieve sub-100ms request latency.

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Challenge brief

What you are building

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

Develop a fast ONDC delivery dispatch agent using Vercel AI SDK and Groq Cloud LPU acceleration to optimize fulfillment SLAs.

Delivery guide

How work is evaluated

Evaluation

Evaluates dispatch assignment speed and carrier SLA match accuracy.

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
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

fast_latency_check

Verifies execution under 100ms

Binary check

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

Dimension 2

latency_ms

Inference and response generation latency in milliseconds • target: 78 • range: 0-1000

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

  • Build serverless tool pipelines with Vercel AI SDK and Groq Cloud acceleration

  • Implement route dynamic pricing algorithms for ONDC network nodes

  • Manage delivery SLA negotiation streams across independent logistics buyer apps

  • Achieve end-to-end routing decision latency below 100ms

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic ONDC network order stream containing geolocation pincodes and delivery partner quotes.

Challenge setup

Evaluates dispatch assignment speed and carrier SLA match accuracy.

Challenge input

JSON order request payload with pickup and drop coordinates

AI SDK

TypeScript framework for streaming and tool execution

Groq Cloud

Ultra-low latency LPU model execution

Evaluated output

JSON dispatch plan with carrier_id, fee, and latency_ms

Evaluation checks
  • Verifies execution under 100ms
  • Inference and response generation latency in milliseconds • target: 78 • range: 0-1000
Configured evaluation evidence
  • Benchmark: High-Throughput Logistics Benchmark
  • Latency Ms target: 78
  • 1 public reference case
Configured runtime
  • JavaScript execution harness
  • javascript sandbox (unavailable on Versalist)

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
  • Vercel AI SDK · Optional
  • Hume AI · Optional
Policy Serving
  • Groq Cloud · Required
Orchestration
  • Vercel AI SDK · Optional

Evaluation contract

  • fast_latency_check · Weight 1
  • latency_ms · Weight 1

Metrics

  • latency_ms · Target 78

Benchmarks

  • High-Throughput Logistics Benchmark
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON order request payload with pickup and drop coordinates

    Output: JSON dispatch plan with carrier_id, fee, and latency_ms

    Execution harness: JavaScript

Evaluation tools

  • AI SDK · Agent Framework
  • Groq Cloud · Inference Runtime

Recipe freeze marker

No recipe freeze time is recorded.

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