Home energy rebate programs increasingly rely on photo evidence to qualify customer eligibility and to verify new installations. Many jurisdictions now impose an eligibility condition on existing homes where the replaced heating system must be an old non-condensing style furnace. This condition must be qualified for the customer to become eligible for a replacement rebate. After the replacement system has been installed, the program must now verify the type and location of the new system to confirm they've captured the intended benefit.
We see this heightened need for transparency in both electric and gas programs. Many electric utilities are now promoting dual fuel systems as they seek to grow their electric heat business and/or offload to fuel during winter peak. Gas efficiency has nearly run its course except for the opportunity to early replace old inefficient furnaces. None of these goals can be met if the replaced and replacement system are not as claimed.
The challenge for program administrators is to process that photo evidence at scale. Putting a set of eyes on each photo is not cost effective or even realistic. Automation is a must. Fortunately, AI has arrived.
Leveraging Trade Allies & Mobile Technology
Many of us remember the army of home auditors that assessed existing home conditions and pre-qualified their eligibility for rebates. That gave way to simple prescriptive rebates with elevated baselines. Now, as the industry transitions to targeted efficiency and strategic electrification, we find ourselves without a third-party network to perform assessments and verifications. What we can do is apply technology and that is far more cost effective when compared to field trips.
Qualification and verification starts by leveraging trade allies, equipping them with a mobile app, and utilizing the camera and GPS features from their mobile device. Published apps already exist including DSMally for U.S. programs and DSMally Canada for Canadian programs. Contractors already use their mobile device to register equipment, record service calls, and take payment, so it's a simple ask of them to submit pre-qualifications and post install notifications with geo-tagged photo evidence.
In addition to the geo-tagged photo evidence, the app can also capture customer info, claimed location, equipment details, analytics, photos of documentation, etc. All of this can be done conveniently and accurately on-site.
AI Analysis to Verify Trade Ally Claims
While there are many well intended contractors but there are still some that lack the desired intent. There's also the potential conflict where satisfying rebate program policy is subordinate to the contractor's need to close the sale. Therefore, guardrails must be installed to mitigate installer intervention and to ensure their claims can be trusted.
To verify location, the app automatically embeds the GPS coordinates and timestamp into each photo at the moment of capture. This happens seamlessly — the contractor simply takes the photo and the location data is recorded. No extra steps, no manual entry. Through the embedded GPS metadata, the system calculates exactly how far the photo was taken from the claimed address and builds an acceptable rule such as must be within 3 miles. If the rule is violated, the submission is badged and that alerts the quality assurance professional of the violation. A secondary rule can also be built comparing the submission time to the image timestamp.
To verify the equipment type, the app uses generative AI image recognition trained specifically on HVAC equipment to identify what's truly in the photo. Categories include heat pump outdoor unit, inefficient non-condensing furnace, efficient condensing furnace, or none-of-the-above. Once again, if the classification does not match the contractor claim, then the submission is badged to alert the quality assurance professional of the violation.
Once the installer submits, their claims are firm and the submission can no longer be edited. Advanced rules are also possible such as building an audit trail on the image files and asking the GenAI model to alert the program if a photo has already been processed through the model.
The Complete Workflow
Here's how it comes together:
- Trade Ally Enrollment: Trade ally registers for the program and sets up user account for the app. Then downloads DSMally from the App or Android store.
- In the field: Contractor opens DSMally, enters details, and captures equipment photos on-site. This can be for a pre-qualification, new install notification, or both.
- Automatic tagging: The app embeds GPS coordinates and timestamp into each photo.
- Submission: Contractor submits with photos.
- AI analysis: Our system classifies the equipment type and calculates distance from the claimed location.
- Admin review: QA staff see the installation in their queue with the AI verification results.
- Decision: QA staff bulk approves clean records and reviews badged records for accept or deny.
- Result: A PDF file is sent to the contractor and/or customer. (Optional) Records can be fed by API to the rebate administration system for a spiff request and/or contractor entered rebate.
Benefits for Energy Program Managers
Strengthen Quality Assurance
Add an automated verification layer to your QA/QC process. AI screening catches issues that manual review might miss.
Support Compliance Requirements
Home Energy Rebate programs require documentation of installations. AI-verified, geo-tagged photos provide auditable evidence for reporting and program evaluation.
Scale Without Adding Staff
As rebate volumes increase, AI verification handles the growth without proportional staff increases.
Reduce Improper Payments
Catch equipment mismatches and location discrepancies before approving payments. Protect ratepayer and taxpayer funds.
Maintain Contractor Accountability
When trade allies know photos are verified for location and content, documentation quality improves across your program.
Transparent AI You Can Trust
We designed this feature with program administrators in mind. The AI doesn't make approval decisions — it provides information to support your team's judgment. Every classification includes an explanation of what the system detected, so reviewers understand the reasoning.
When the AI is uncertain, it says so. Ambiguous images are flagged for human review rather than forced into a classification.
Available Now
AI-powered photo verification is available for Incentit energy services clients. The feature integrates with:
- DSMally and DSMally Canada mobile apps for trade allies.
- Web portal custom rebate applications and pre-applications.
- Admin interface for reviewing and acting on AI findings, can extended into full program management, tracking, and reporting.
Whether you're a cooperative network, municipal utility, investor-owned utility, or state energy office, this capability can strengthen your program integrity.
Contact us to schedule a demonstration and see how AI photo verification works with your rebate workflow.