Agentic Search
We rebuilt search on Slack.com so a buyer could figure out whether Slack fits their team without leaving for Google: AI answers layered over traditional results, never forced on anyone.
Buyers arrive with a question. Slack.com answered with a list of links.
Search on Slack.com was an indexing tool: type keywords, get pages. Meanwhile the site's AI assistant lived somewhere else entirely, as a separate system nobody found. But the people deciding whether Slack gets bought arrive with intent, not well-formed queries, and when the site couldn't answer, they left for Google and often didn't come back.
"Why can't I just google it? It's much easier and it gives me a short summary."
P7 · SMALL BUSINESS OWNERFourteen interviews. Four patterns.
Founders, team leads, and IT decision makers: the people who decide whether Slack gets bought. Four behaviors kept surfacing. The whole design is a response to them. Tap one for the voice behind it.
Our sponsors called the first direction "a magic recipe without evidence."
They were right. Mid-project, the audience got cut down twice: from "people who use websites" to "people deciding whether to buy Slack," then to two specific personas: small business owners and enterprise researchers. Every feature decision got easier after each cut, and the concepts that couldn't survive the narrower audience got dropped.
AI as a layer, never a mode.
A hybrid results page: AI summary on top, traditional results underneath, and a conversation that follows the user across page navigation. Five screens carry the whole system. Click through them. Every AI surface here is optional: the search works end to end if you never touch the assistant.
Every stage had its own test.
Observation, interviews, rapid concept testing, and a custom evaluation framework built with our sponsors. The closer to the final, the more we relied on tools we made ourselves.
We watched people use Slack.com cold, with no task and no script. Most reached for the nav, fell back to search when stuck, and abandoned the site when search didn't help. This is where the real problem got named.
Seven features prototyped in Figma Make and put in front of users and our Salesforce sponsors. Some, like contextually rolling prompts, landed immediately. Others got cut by mid-October.
Three end-to-end concept directions, each tested against the same query set. The split-AI-and-traditional concept advanced. The other two stayed in the deck as foils.
Nielsen wasn't built for hybrid AI-search systems. So we wrote our own twelve heuristics with our sponsors: AI/traditional distinction, source citation, conversation persistence, misspelling recovery, and more. Most ratings landed at 4 or 5 out of 5.