Real-time research is the part of a meeting where the answer arrives inside the call, not as homework after it. The pattern shows up across sales, strategy, design, and hiring. Below are twelve examples teams have run with voice AI sitting in the room, each one ending a question that would otherwise have rolled into next week.
What real-time research actually is
Real-time research is the act of pulling the evidence your team needs while the meeting is still in progress. A claim gets made, a number gets half-remembered, a competitor gets mentioned, and instead of someone writing "look this up" on a sticky note, the answer lands on the shared screen within seconds.
The pattern is older than AI. Good chiefs of staff have been doing it for years, quietly opening laptops and pasting links into the chat. What is new is that a voice AI teammate can do it for every meeting, for every team, without needing to be addressed. The room keeps talking. The work shows up anyway.
The twelve examples below come from teams using relly across product, sales, design, and ops. Names are changed. The patterns are not.
1. The competitor question that ends the call
A founder is pitching to a series A investor. Fifteen minutes in, the investor asks how the product compares to a startup that just raised. The founder has read the TechCrunch piece but has never seen the product. Before she answers, the competitor's landing page, pricing, and a side-by-side feature read are on the shared screen. She speaks to the comparison, not around it. The check closes two weeks later.
2. The "what did we decide last quarter" moment
A product manager is in a roadmap review. Someone says, "We already debated this in Q4, what was the call?" Without real-time research, the meeting either stalls or doubles back on a decision it already made. With it, the meeting note from October is pulled, the decision line is highlighted, and the team confirms or revisits with full context inside the same hour.
3. The pricing question in a customer call
An account executive is on a renewal call. The customer asks about a tier that was introduced last month. The AE has not been briefed yet. The voice AI surfaces the latest pricing one-pager and reads the relevant line into the chat for the AE to glance at. The customer hears a confident answer instead of "let me get back to you," and the renewal stays in the call rather than slipping to a follow-up email chain.
4. The design critique that needs a comp
A designer presents a new dashboard. A reviewer says, "This reminds me of how Linear handles empty states." Instead of pulling up Linear later, the room sees four Linear empty-state screens within ten seconds. The critique sharpens. The next iteration is informed, not vibes-based.
5. The hiring debrief that catches a contradiction
A panel is debriefing on a senior engineer. One interviewer says the candidate "struggled with system design." Another says they "carried the system design loop." The voice AI pulls the live notes from both interviews side by side. The contradiction turns out to be about two different rounds. The panel makes a clearer call. A bad hire, or a missed great hire, gets avoided.
6. The compliance flag inside a strategy meeting
An exec team is sketching a launch plan for a new region. Someone says, "We can ship by August." The voice AI flags that the privacy review for that region is still open, surfacing the open ticket from the internal tracker. The team adjusts the date inside the meeting rather than committing to one that legal will reject the following Monday.
7. The "remind me what this customer said" loop
A success manager is in an account review. The team asks, "What did this customer flag in their last QBR?" Three sentences from the previous QBR transcript appear, with timestamps and speaker labels. The room sees the actual quote, not someone's memory of it. The account plan that comes out of the meeting is grounded in what the customer actually wants.
8. The market sizing rough cut
A founder is debating whether a wedge segment is large enough to bother with. Real-time research pulls three rough proxies: a public report, a competitor's investor deck slide, and a Crunchbase headcount roll-up across companies in the segment. None of them are perfect. Together they are enough to decide whether to spend a week on a deeper study or kill the wedge in the meeting.
9. The technical claim that needs a citation
An engineer says, "Postgres can't handle this many writes per second on a single node." A teammate asks for a source. Real-time research surfaces the Postgres docs page, a recent benchmark, and a counter-example from the team's own load tests. The room argues with evidence on screen rather than memory in the air. The decision lands faster and sticks better.
10. The slide that needs to exist in five minutes
A leader is presenting to the board and realizes mid-call that a chart they need is missing. They name what they want out loud. The voice AI pulls the underlying metric from the analytics tool, draws a clean cohort chart, and drops it into the deck. The board sees the answer instead of "I'll follow up." This used to require a designer and a half day. Now it requires a sentence.
11. The interview reference check
A founder is meeting a potential first hire. The candidate name-drops a former CEO. Inside the call, the voice AI checks LinkedIn, finds the tenure overlap is real, and surfaces the CEO's public posts that mention the candidate by name. The founder asks better questions in the second half of the meeting. The hire signs two days later.
12. The "is anyone else doing this" sanity check
A product team is about to commit to a build. Before they do, real-time research scans for existing tools that solve the same problem, summarizes the top five, and highlights where they fall short. The team either builds with more confidence or pivots to integrate instead of rebuilding. Either way, the meeting ends with a clearer call than it would have made twenty minutes ago.
The pattern under the examples
Every example above is a meeting that used to need a follow-up. With real-time research, the follow-up happens inside the call, and the call ends one decision further along.
Three things make this work in practice. First, the AI has to listen continuously, not wait for a prompt. The moment someone says "let me get back to you" is the moment you have already lost the round. Second, it has to have access to your team's actual context: docs, transcripts, tickets, dashboards. Generic web search is not enough. Third, it has to know when to stay quiet. Half the value is restraint, because a teammate that interrupts every minute is worse than no teammate.
For more on that last point, see when AI should stay quiet in a meeting. For a wider look at the design philosophy, see how AI should behave in a real meeting.
Where this fits in your week
The teams getting the most out of real-time research are not running it on every meeting. Standups and one-on-ones rarely need it. The high-leverage slots are decision meetings: strategy, customer-facing calls, hiring debriefs, design reviews, pricing discussions. Anywhere a single fact or comp would otherwise turn the meeting into a follow-up.
Start with one recurring meeting on your calendar where the same blockers keep showing up. Try it for two weeks. Track how many follow-up tasks shrink or vanish. If the answer is "most of them," the case is made.
How relly does it
relly joins your meeting on Zoom, Google Meet, or Microsoft Teams, listens to the conversation, and pulls the evidence your team needs without anyone having to type a prompt. It reads your team's docs, transcripts, and tools, cites its sources, and stays quiet when the room does not need it.
If your team is losing meetings to "let me get back to you," early access is open. First year is 50% off, no card needed until launch.
Common questions
What is real-time research in a meeting?
Real-time research in a meeting is when an AI teammate listens to the live conversation, identifies the question or claim that needs evidence, and pulls the answer from internal docs or the web before the room moves on. It happens during the call, not after.
How is real-time research different from a meeting recap?
A recap summarizes what was said after the meeting ends. Real-time research delivers the missing fact, chart, or document while the meeting is still going, so the team can act on it inside the same call instead of scheduling a follow-up.
What kinds of meetings benefit most from real-time research?
Decision-heavy meetings benefit most: strategy reviews, customer calls, design critiques, hiring debriefs, and pricing discussions. Anywhere a single missing data point would otherwise stall the conversation or push it into a follow-up.
Does real-time research replace the prep work before a meeting?
No. Good prep still sets the agenda and frames the decision. Real-time research fills the gaps you could not predict: the off-topic question, the surprise objection, the half-remembered number. The two work together.
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