How our AI search helps service businesses match prospects to proven solutions

TL;DR

We replaced the standard “browse our services” experience on ibsolutions.dev with a search that accepts plain language problem descriptions and returns matching proven solutions in seconds. The result: faster discovery, better-qualified conversations, and a built-in feedback loop that sharpens how we position our services.

Challenge

Buyers visiting a service business website face the same friction everywhere. Proof and practical examples are buried inside long pages. There is no easy way to check whether a provider has solved something similar to their specific problem. Most give up before they find a match, which slows evaluation and means good-fit prospects leave without starting a conversation.

Solution

You describe your problem in a sentence. The page shows you the three most relevant solutions we have delivered, with links to real client work and a way to start a conversation. No browsing, no guessing which menu item might lead to something relevant.

Impact

In early testing, visitors reached a relevant solution in under 10 seconds instead of clicking through multiple pages and service descriptions. But the more surprising outcome was what the search revealed about our own content. Prospects describe their problems differently from how we write about our services. Every query exposes those gaps, which means the search doesn’t just help visitors find answers; it helps us write better, position more clearly, and spot opportunities we would have missed.

Where this is useful for marketing, sales and operations teams

The pattern behind this search is not specific to our website. Any team that needs to connect people with the right information based on a problem description can use the same approach.

Marketing teams can replace vague “we do X” pages with a single entry point where visitors describe their situation and see relevant examples. Campaigns become simpler because every ad, email, or post can point to one discovery page instead of trying to guess which service page fits which audience segment. Over time, the logged queries show exactly how your market talks about their problems, which feeds directly into messaging and positioning work.

Sales teams can let prospects type their use case and jump straight to proof that matches their industry, system stack, or deal stage. Instead of sending a generic capabilities deck, a salesperson can share a link where the prospect finds their own match. The search queries also feed into CRM as qualification signals, so follow-up can be tailored to what the prospect actually cares about rather than what the salesperson assumed.

Customer success and support teams can use the same pattern to help existing customers find how-to guides, playbooks, or feature examples by describing their issue in plain language. This reduces “I could not find it” tickets and makes self-serve support feel natural instead of frustrating.

Operations, HR, and internal teams can apply this to internal portals. Staff describe the task they are trying to complete and the system surfaces the right process doc, SOP, or internal tool. This removes the guesswork of navigating folder structures or remembering which label a document was filed under.

Technical details

This experiment is documented as a Lab project with the full technical breakdown: how the hybrid scoring works, the serverless architecture, and what we learned building it.

What this means

The pattern is simple: let people describe their problem instead of making them navigate your catalog. When the system returns relevant proof in seconds, visitors move faster from browsing to a real conversation, and you learn exactly how prospects frame their challenges. Over time that compounds. Dead-end visits drop, conversations arrive better qualified, and your content gets sharper with every query.