Product Product · In Development
LeadProc
AI-powered missed-call interception for home service contractors — captures and qualifies leads that would otherwise be lost to voicemail.
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AI features
AI that lives inside the product.
Assistants, qualification, and AI-backed workflows built against your content and your application.
Problems we take on
- An AI prototype that never made it into the product
- Support questions already answered in docs, still hitting humans
- Leads arriving after hours with no qualification path
- Internal knowledge scattered across tools
- A chatbot that sounds impressive and cannot be trusted
What you get
- AI feature or assistant grounded in your content and product context
- Ingestion from docs, FAQs, policies, or application data
- Embed or API integration with the existing product
- Escalation when the model should not answer
- Basic conversation monitoring
- Clear scope for what the feature will and will not do
Good fit when
- Products that need an AI feature as part of an MVP or rescue
- Teams with real documentation or domain context to ground against
- Agency partners whose clients need AI execution, not a pitch deck
- Internal tools that should answer from company knowledge
Capability, not the product
AI is not a homepage offer at HPN Studio. It is how some products work — inside MVP development, product rescue, or a partner build.
LeadProc uses an AI conversation layer as part of a telephony product, not as a standalone chatbot page.
What we can build
Product features. Qualification, summarization, assistants, and workflows that sit behind a real application.
Support assistants. Answers grounded in your documentation, with a clean handoff when the model should not guess.
Internal assistants. Faster access to runbooks and company knowledge.
How it works
The useful pattern is retrieval plus generation against your content, or a constrained agent inside an existing workflow. When the source material changes, the answers should follow — without a mysterious retraining ritual.
Providers are chosen for the job (OpenAI, Anthropic, or more constrained options when data sensitivity requires it).
What we will not promise
An assistant will not replace judgment, close every sale, or make an unfinished product look finished. If the use case is not worth the build, we will say so.
Selected work
Where this capability showed up
Bring the actual problem.
If this capability is the missing piece, start with the project — not a capability shopping list.