Point your camera at any yard, room, or job site. SnapCost reads the scene and returns a fair-market price range in under thirty seconds — labor, materials, equipment, disposal. A second opinion before the first quote arrives.
About SnapCost
SnapCost is a mobile-first AI application that converts a single photograph into an actionable cost estimate for real-world services. A user photographs an overgrown yard, a damaged fence, a cluttered garage — and within seconds receives a structured price range, a line-item breakdown, and a confidence score calibrated to their location.
The core problem we address is information asymmetry. When a homeowner calls three contractors, they receive three different numbers — often with a wide spread — and no objective way to evaluate fairness. Existing platforms connect consumers to contractors but provide no independent price guidance. The consumer is still negotiating from ignorance.
SnapCost sits at the intersection of two needs: an instant, data-driven pricing layer that serves consumers seeking fairness and contractors seeking quoting efficiency. The long-term ambition is larger — a universal pricing reference for the physical world.
Who it serves
A single photo gets you to your first estimate in under sixty seconds. Every estimate ships with line items and a confidence tier, so you know what you’re paying for and how much the model trusts itself.
Run the same engine from the supply side. Generate consistent quotes in seconds instead of half an hour. Benchmark your rates against the regional market. Receive leads that arrive pre-anchored to a fair price range.
Real estate platforms, home inspection companies, insurance carriers, and property management software can integrate SnapCost estimates directly. Usage-based pricing per estimate call, volume-tiered.
The quote validator
Already got a quote? Photograph the job, enter the number, and SnapCost returns an objective verdict — whether it falls within the fair-market range, and by how much it doesn’t.
“First contractor said $2,400 to pressure-wash the driveway. SnapCost flagged it as three times the fair range. The next quote came in at $680.”— Priya R. — Atlanta, GA
| Service | Lawn restoration · 2,400 sq ft |
| Region | Austin, TX |
| Contractor quote | $420.00 |
| Fair-market low | $185.00 |
| Fair-market high | $240.00 |
Architecture
Each layer is independently scalable and replaceable. Designed for low-latency response. Every production estimate logs its inputs and the engine version that produced it — enabling retrospective accuracy analysis and regression detection across model updates.
A user-submitted image passes through a multimodal vision model configured with structured-output prompting. The model extracts a canonical feature set — visible square footage inferred from reference objects, overgrowth density, obstacles, slope, equipment requirements. Output is a structured JSON object, not free text. A lightweight routing classifier first identifies the service category before the category-specific extraction prompt is applied. Multi-photo input is fused into a more complete scene representation when available.
The pricing engine is deterministic at its core — deliberately not an LLM. This distinction is critical: users must be able to trace how a number was calculated, and the system must produce consistent results for the same input. Regional pricing data is sourced from public labor statistics, contractor invoice data, and industry benchmarking — refreshed quarterly and weighted toward recency. The engine outputs a low–mid–high price range, a confidence score, and a structured list of contributing factors.
The LLM layer does not generate numbers — it interprets them. Its responsibilities: translate the vision JSON and pricing calculation into human-readable explanations, identify the few factors driving price variance, generate targeted follow-up questions when confidence is below threshold, and handle edge cases. This separation of concerns — deterministic pricing engine, LLM as interpreter — prevents the common failure mode of LLMs confidently generating incorrect price figures.
The data layer is the platform’s primary long-term moat. Feedback mechanisms continuously improve accuracy: contractors submit actual job prices against SnapCost estimates, creating a labeled dataset of estimate accuracy; consumers are prompted after a quote match to report the actual price paid; and edge cases flagged by low-confidence scores are reviewed and used to refine category detection and feature-extraction prompts. Model updates are versioned and accuracy is tracked across deploys.
From beta users
“Got quoted eight hundred dollars to mow a small lawn. SnapCost said sixty-five to ninety-five. I showed them the screenshot. They came down to seventy-five.”Marcus T. Austin, TX · Lawn care
“Used to spend Saturdays getting quotes. Now I snap one photo and know if the price is fair before they even show up.”Jordan K. Phoenix, AZ · Fence repair
“Cuts our quoting time from forty minutes to under five. Customers stop arguing because they’ve already seen the range.”Ricardo H. Contractor · Dallas, TX
Free to download. A handful of estimates a month at no cost. Pro unlocks unlimited use, full quote validation, and saved estimate history.