An AI-powered web platform for the tree service industry that generates price estimates from user-submitted photos and project details. The AI model analyzes images to assess tree size, complexity, and proximity to structures, combining this with form data to provide an instant cost range. The platform also serves as a lead-generation marketplace.
Homeowners who need a tree removed or pruned usually have no idea whether the job will cost a little or a lot, and arborists spend a large part of their week driving to quote jobs that never go ahead. Many enquiries are price shoppers or jobs outside the operator's equipment range. Customers, meanwhile, dislike waiting days for a site visit just to learn whether the work is within budget, and they often contact several businesses at once.
An AI tree removal quote tool asks the homeowner to upload photos and answer a few questions about access, location and nearby structures. A computer vision model assesses visible factors such as approximate height, canopy spread, lean and proximity to buildings or power lines, and combines them with the form data and regional rates to produce an indicative price range. The platform can then pass qualified, better-informed leads to local arborists who confirm with a firm quote. It narrows expectations early; it never replaces an on-site assessment by a qualified arborist.
A React or Next.js web form guides the homeowner to take photos from set angles, including a reference object for scale, and answer questions on access and nearby hazards.
Images are sent to a vision model through OpenAI's vision capability or a model hosted on Replicate, which returns structured estimates of size, lean and proximity to structures.
A pricing model combines these assessments with location, access difficulty, stump removal and waste disposal to produce a low-to-high price range with stated assumptions.
The homeowner sees the range, the factors that influenced it and a clear note that a site inspection is required for a firm quote.
If they proceed, the job summary and photos are sent by email notification to suitable local contractors through the Node.js or Firebase back end, who respond with firm bids.
A homeowner uploads photos of a large oak tree leaning over their house. The AI analyzes the images, identifies it as a high-risk removal, and combines this with the user's location to generate an indicative price range for an arborist to confirm. The user can then request firm bids from local, vetted contractors.
Computer Vision (OpenAI Vision, Replicate), React/Next.js, Node.js/Firebase, Database, Email Notifications
Many councils require a permit before removing or significantly pruning trees, and heritage or significant tree registers may apply, so the platform should remind homeowners to check with their local council. Work near power lines must be done by appropriately authorised people under state electrical safety rules. Estimates must not be misleading under the Australian Consumer Law; present ranges as indicative and explain what could change them. If you describe contractors as vetted, be able to show how. Homeowner photos and addresses are personal information under the Privacy Act, and sharing them with contractors should be disclosed and consented to.
This solution requires careful evaluation of cost-benefit ratio. Consider implementation only after thorough analysis and risk assessment.
They are useful for setting expectations, not for committing to a price. Accuracy depends on photo quality, how well the tree is visible and how much local job data the pricing model was calibrated on. Hidden factors like root systems, internal decay and access restrictions can only be confirmed on site.
Both. A single operator can embed the tool on their website to pre-qualify enquiries and prioritise site visits, which is simpler to build and run. The marketplace version adds contractor onboarding, lead routing and dashboards, which is where much of the two to four month timeline and higher cost comes from.
The most valuable input is past jobs with photos, job notes and final invoiced prices, covering different tree sizes and access conditions. Without it, ranges rely on general assumptions and will be wide. Collecting this data from partner arborists early is usually the first project task.
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