AI matches top subcontractors to your projects instantly, vetted and ready
Enterprise construction teams suffer delays and cost overruns from inefficient subcontractor bidding and vetting processes on large projects.
MatchBuild uses AI to pair project needs with pre-vetted subcontractors based on skills, location, capacity, and past success. Teams describe projects, get ranked matches with vetting summaries, and initiate bids seamlessly. This bypasses slow sourcing, accelerating project kickoffs.
Enterprise construction teams managing large-scale projects
Predictive matching algorithm using project-sub compatibility scoring beyond basic search
supportive
Input specs for instant sub matches
View ranked list with scores and profiles
All matches include fresh vetting data
One-click bid requests to matches
Track past matches and outcomes
Real-time sub availability
Advanced search filters
Query matches via natural language
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| text | No | |
| type | text | No |
| skills | text[] | Yes |
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| user_id | uuid | No |
| requirements | text | No |
| location | text | Yes |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| project_id | uuid | No |
| sub_id | uuid | No |
| compatibility_score | int | No |
Relationships:
/api/matchesGenerate matches for project
/api/matches/:projectIdGet matches list
/api/projectsCreate project
/api/bid-requestsSend bid request
/api/profiles/:idGet user profile
/api/users/searchSearch subs
1 project
| Month | Users | Conversion | MRR | ARR |
|---|---|---|---|---|
| Month 1 | 60 | 3% | $75 | $900 |
| Month 6 | 450 | 7% | $850 | $10,200 |
AI finds, vets, and connects subcontractors tailored to your project.
Email cold outreach to 50 GC firms from ENR top lists with match demo; join construction Slack/Discord for feedback loops; offer sub-side free access to build network.
Compliance docs
No matching
AI-powered discovery
Task mgmt
Limited subs
Matching focus
Data flywheel from match outcomes improving AI
Skilled labor shortage peaks in 2024, demanding smart matching
Poor AI matches
Feedback loops + manual curation
High AI costs
Usage-based optimization
Sub network growth
Viral invites
Success: 5 pain validations
Success: 70% satisfaction
Success: 10 paying
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