Can Voksha's intake data be used by our financing partners like Sunlight, Mosaic, or GoodLeap?
For Solar Installers
Voksha captures the qualification data these financing partners need at the first point of contact, but it does not submit financing applications directly, since that step involves the homeowner's formal consent and identity verification that has to happen through your financing partner's own secure application flow. What Voksha does is ask the homeowner the pre-qualification questions your sales team would ask anyway: do you own the home, what is your approximate monthly utility bill, and a self-reported estimate of credit standing (commonly framed around the 650-plus threshold most solar lenders like Sunlight Financial, Mosaic, or GoodLeap use as a soft cutoff for their standard loan products). That data is captured as structured JSON in your CRM, so when your sales rep follows up, they already know whether the homeowner is a likely fit for a standard loan product, a lease or PPA structure, or a cash purchase, and can route the conversation and financing paperwork accordingly instead of starting the qualification conversation from zero. This shortens the gap between initial contact and a submitted financing application, which matters because financing approval delays are a common point where deals stall or homeowners get cold feet. It also means your sales team is not wasting a site visit on a homeowner whose self-reported credit and income profile make approval unlikely, since that mismatch surfaces during the AI-handled intake call rather than after a design consultation and proposal have already been produced.
Voksha captures the qualification data these financing partners need at the first point of contact, but it does not submit financing applications directly, since that step involves the homeowner's formal consent and identity verification that has to happen through your financing partner's own secure application flow. What Voksha does is ask the homeowner the pre-qualification questions your sales team would ask anyway: do you own the home, what is your approximate monthly utility bill, and a self-reported estimate of credit standing (commonly framed around the 650-plus threshold most solar lenders like Sunlight Financial, Mosaic, or GoodLeap use as a soft cutoff for their standard loan products). That data is captured as structured JSON in your CRM, so when your sales rep follows up, they already know whether the homeowner is a likely fit for a standard loan product, a lease or PPA structure, or a cash purchase, and can route the conversation and financing paperwork accordingly instead of starting the qualification conversation from zero. This shortens the gap between initial contact and a submitted financing application, which matters because financing approval delays are a common point where deals stall or homeowners get cold feet. It also means your sales team is not wasting a site visit on a homeowner whose self-reported credit and income profile make approval unlikely, since that mismatch surfaces during the AI-handled intake call rather than after a design consultation and proposal have already been produced.
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