AI workflowTutorial8 min

Build an AI tender triage queue in Next.js

A full-stack guide for combining document intake, model extraction, eligibility scoring, and human review.

Real example

Example: a triage board for 80 new public tenders

A consulting firm checks public tender portals every morning. The team needs to know which tenders are likely fit, which need missing documents, and which should be ignored.

Build a Next.js route with a server-loaded queue. Each row shows buyer, deadline, extracted category, eligibility status, AI recommendation, evidence count, owner, and next action. Detail pages show source snippets and reviewer controls.

The team starts with the highest-fit opportunities and stops wasting time opening documents that are clearly out of scope.

Queue row shape

type TenderQueueRow = {
  id: string;
  buyer: string;
  deadline: string;
  fit: "high" | "medium" | "low" | "unknown";
  status: "new" | "needs_evidence" | "approved" | "rejected";
  evidenceCount: number;
  nextAction: string;
};

Tutorial path

How to implement it

Step 01

Create intake records for each notice with source URL, deadline, buyer, and document status.

Step 02

Run extraction into structured fields and store the raw response metadata separately.

Step 03

Score fit with transparent criteria such as industry match, deadline risk, and missing documents.

Step 04

Show a triage table with status, recommendation, evidence, and owner.

Step 05

Capture approve, reject, request evidence, and archive actions as explicit state transitions.

Checklist

Ready when these are true

Document source is attached

Deadline risk is visible

Recommendation includes evidence

Operator actions are stored

Rejected tenders remain auditable

Field notes

What matters in practice

The queue is the product surface where AI work becomes inspectable.

Tender triage needs evidence and next actions, not only a score.

Human decisions should feed back into prompts, rules, and eligibility criteria.

Avoid these mistakes

Common failure modes

Do not show only a score; show the reason and source evidence.

Do not hide deadline risk below the fold.

Do not overwrite reviewer decisions when the extraction reruns.

Practical tip

Queue age is a useful metric. If AI creates faster triage but old items still pile up, the workflow is not fixed yet.

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