Trade Triage

For

Property managers at build-to-rent operators and residential block managers

Solves

Tenant maintenance requests are misrouted, causing delays and wasted contractor visits.

Delivers

Classified job list, ranked quotes, confirmed bookings, satisfaction ratings

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$9,000 for the MVP, $36,000 for the full product

Run it

Inside your business, or as part of your offer to clients

For property managers at build-to-rent operators and residential block managers, turn tenant messages and photos into classified, quoted, and booked maintenance jobs.

1. Classify incoming tenant messages by trade and urgency.
2. Extract job details from photos and text.
3. Match with licensed contractors from a database.
4. Send standardized job briefs and parse quotes.
5. Present ranked shortlist for approval.
6. Send tenant confirmation and follow-up survey.

What goes in, what comes out

The workflow

1. In
   Start with

   Tenant messages, photos, property files, contractor database

2. 1

   Tenant submits request

3. 2

   System classifies trade and urgency

4. 3

   Cross-references contractor database

5. 4

   Sends briefs

6. 5

   Parses quotes

7. 6

   Manager approves

8. 7

   Tenant receives slot

9. Out
   Finish with

   Classified job list, ranked quotes, confirmed bookings, satisfaction ratings

AI does the heavy lifting, people stay in charge

Use multimodal models to read text and images for classification and urgency detection. Use language models to parse contractor quotes. A property manager reviews the shortlist and approves the final booking before any commitment.

What your team sees

Key screens: Inbox, Job detail, Quote review. Use a dashboard list of incoming requests with status badges, a detail view showing tenant message, photos, classification, and suggested trade, and a quote comparison panel where contractors' responses are ranked. Allow the manager to approve with one click, triggering tenant confirmation. Display audit trail of classification and approvals.

Accounts and administration

Property manager accounts, tenant data privacy, job versions, approval logs, contractor vetting status, audit trail of all actions.

Integrations and data access

Email inbox, WhatsApp Business API, property management software (e.g., AppFolio), calendar systems.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   5 days

   One buyer segment, one recurring use case; first modules: classification engine and daily digest. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   6 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   2 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will property managers at build-to-rent operators and residential block managers use it to solve "tenant maintenance requests are misrouted, causing delays and wasted contractor visits"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Paid pilot with one firm, baseline: average time-to-fix and misrouting rate.
4. Measure, then decide. Track reduction in average time-to-fix and percentage of jobs correctly classified on first attempt. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. First buyer: one property management firm. Use case: inbound email triage only. Modules: classification engine and daily digest. Manual review of all classifications before any contractor contact.

After the MVP. Automate quote parsing, booking confirmation, and follow-up surveys after the paid pilot.

What the build depends on. Access to a licensed contractor database, multimodal AI model, and property file data.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test pricing at $2 per door per month, with a 500-door pilot generating $1,000 monthly recurring revenue.

Message to test

Stop playing phone tag: route every maintenance request to the right trade, first time.

Where to find buyers

Property management conferences, LinkedIn groups for build-to-rent operators, industry newsletters.

Lead magnet

Free maintenance inbox audit showing misrouting rates and potential time savings.

The first 30 days

1. Week 1: Integrate with one property manager's inbox and build classification model.
2. Week 2: Test with 50 real requests, refine accuracy.
3. Week 3: Build daily digest and dashboard.
4. Week 4: Run manual approval workflow with one contractor.

Paid pilot

Paid pilot with one firm, baseline: average time-to-fix and misrouting rate. Prove reduction in time-to-fix from 5 to 2 days and 90% first-time correct trade. Decision: continue if metrics met.

Success metrics

Reduction in average time-to-fix, percentage of jobs correctly classified on first attempt.

Retention and expansion

Ongoing subscription with usage-based pricing; value grows as model improves and contractor network expands.

Why clients would pick it

Each job improves the classification model with feedback, and the contractor network and property file integration become proprietary assets.

Alternatives and positioning

Manual email and phone coordination, generic facilities management software. This differs by using AI to automate the triage and quote process, not just track tickets.

Main delivery costs

AI model usage, cloud hosting, integration development, contractor database licensing.

Limit to approved contractors, require human approval for all bookings, anonymize tenant data, and must not auto-book without manager sign-off.