Resolve intent earlier
Start when the customer does not yet know what to search for.
AI PARTS INTELLIGENCE FOR THE FIELD
From equipment photo to the right part—with proof.
HOUSECALL helps a contractor identify unfamiliar equipment, narrow the repair, and prepare a more complete first-time-fix order. When the evidence is not strong enough, it stops.
Working prototype · real OCR and public evidence · human approval before handoff
ONE FIELD INPUT → ONE EXPLAINABLE DECISION
FIELD INPUTBoiler nameplate+ ignition symptom
EXACT EQUIPMENTLaars MFTHW120NA1XN3 public sources attached
LEGACY PART FT1315CURRENT REPLACEMENT FT2118
Grow the complete call
Surface required companions and preparation—not generic upsells.
Protect customer trust
Require evidence and field confirmation before a handoff.
THE PLATFORM VISION
More than a parts finder: an AI front door for the entire service call.
HOUSECALL starts with what a customer actually has—a nameplate photo, a legacy part, or a field symptom—and turns it into an explainable path from equipment identity to a safer, more complete order.
Identify the installed equipment
AI vision grades the photo, reads the rating plate, extracts model and serial identifiers, and asks for a human correction when the image cannot support a reliable match.
Nameplate vision · OCR quality gate · serial and equipment ageBuild an evidence-backed repair path
The system combines exact catalog relationships, OEM literature, public sources, and observed symptoms to verify supersessions, fit requirements, and the next best diagnostic step.
Compatibility fingerprint · symptom navigator · manuals and safety evidencePrepare the whole first-time-fix call
Beyond the primary part, HOUSECALL surfaces required companions and—where service evidence supports it—parts commonly replaced together, plus tools, seals, fluids, installation guidance, and commissioning checks.
Complete-the-call brief · tools and consumables · human-approved handoffTurn service intent into durable growth
Age, repair economics, incentives, and service outcomes reveal when a repair should become a replacement project while aggregated demand signals expose catalog gaps and emerging failure patterns.
Repair-or-replace · incentive finder · outcome learning and demand radarOne natural field input replaces repeated searches and support handoffs. Customers see why a recommendation is credible and what remains to verify.
SupplyHouse can capture demand before the SKU is known, improve conversion, build complete baskets, and uncover qualified replacement opportunities.
Every reviewed outcome can improve future routing, assortment decisions, demand planning, and the evidence customers receive—without bypassing human judgment.