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 workflow example

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.

Implementation approach

This guide is anchored in OpenAI Responses API guide. Use the official API behavior as the boundary, then design the surrounding product state so the feature can be reviewed, retried, and improved.

Create intake records for each notice with source URL, deadline, buyer, and document status.
Run extraction into structured fields and store the raw response metadata separately.
Score fit with transparent criteria such as industry match, deadline risk, and missing documents.
Show a triage table with status, recommendation, evidence, and owner.
Capture approve, reject, request evidence, and archive actions as explicit state transitions.

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;
};

Field notes

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.

Mistakes to avoid

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.

Ready checklist

Document source is attached
Deadline risk is visible
Recommendation includes evidence
Operator actions are stored
Rejected tenders remain auditable

Practical note

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

Use this as an implementation constraint, not just advice. The interface, server code, and validation path should make the same behavior true.

Apply this to a build