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Case file

AI Broker Tool

A broker-style intake layer that triages demand, enriches requests, and routes actions to the right operator or system.

Client
Internal Product Demo
Read time
1 min

Primary solution

AI Workflows & Automation

This project is grouped under the buyer-facing solution area it most directly supports.

Capabilities in play

AutomationIntegrationsInternal tools

Snapshot

Applied system demo

Narrative, metrics, and interaction packaged into a compact case-study page.

Surfaces

MDX storytelling, embedded demos, and reusable product communication patterns.

Feb 4, 20261 min readAI Workflows & AutomationAIData BrokerNext.jsPython
AI Broker Tool

Continue through this solution area

This case file sits inside AI Workflows & Automation.

Use the solution page to see how this project connects to related systems, capability patterns, and supporting editorial work.

The purpose of this project was to make a broker workflow understandable in under two minutes. Instead of explaining the routing logic in abstract terms, the interface shows how demand is scored, packaged, and escalated.

Problem

Broker-style systems often disappear behind internal terminology and invisible background jobs. Stakeholders hear about lead scoring, orchestration, and enrichment, but they do not see how the workflow behaves when requests arrive under real pressure.

Solution architecture

The demo pairs a concise case-study narrative with a local interactive component. The page itself is static-first, but the embedded interaction gives a prospect or stakeholder a feel for how the system behaves.

Experience layer

The UI presents pipeline health, operator notes, and a clear sense of where decision-making happens. That is enough to communicate the operational shape without exposing any live infrastructure.

01

Lead triage latency

2.1s median

02

Qualified routing

92%

03

Manual review reduction

41%

04

Operator context retained

100%

Note

Security pattern

If this were connected to a real broker service, all calls would move through app/api/* route handlers. The demo intentionally stays local and stateless.

Embedded preview

Curated live demo

Broker pipeline snapshot

A lightweight client-side component that demonstrates the workflow without live API calls.

Pipeline health

Running smoothly

Inbound leads

480 / day

Qualified matches

143

Human review

29

Shipped actions

114

Operator notes

Broker rules auto-prioritized fintech and compliance-heavy leads.
Escalations were bundled into one analyst queue with context preserved.
Synthetic benchmark stayed under 2.1s median decision time.

Outcome

The result is a portfolio page that acts like a small product pitch. It combines narrative, metrics, and interaction while still fitting neatly into a repo-managed MDX workflow.

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Book an intro to scope the bottleneck, workflow, or architecture issue.Qungs builds custom software, automation systems, and applied-AI interfaces.Important updates or operational notes can be edited in src/lib/site.ts.Book an intro to scope the bottleneck, workflow, or architecture issue.Qungs builds custom software, automation systems, and applied-AI interfaces.Important updates or operational notes can be edited in src/lib/site.ts.