AI research agent — sourced answers, not link dumps

Most "research" at work is not a literature review — it's an hour of tabs. Who are this prospect's competitors? What does this vendor actually charge? Is this regulation going to touch us? What happened in our space this week? The information exists; the hour doesn't. An AI research agent absorbs exactly that class of work: real questions, worked properly, returned as answers with receipts.

The bar that separates a research agent from a chatbot is sourcing. A chatbot answers from memory, confidently, sometimes wrongly. A research agent goes and looks — and shows you where it looked, so trust never has to be blind.

What Arlo researches

• Prospects and accounts — before the call: what the company does, who's who, recent news, stack hints, the competitor they probably use. Delivered as a brief in your thread, feeding the pipeline directly.
• Markets and competitors — pricing pages, positioning shifts, funding, feature launches. One-off deep dives, or a standing watch that pings your channel when something actually changed.
• Vendors and tools — "compare these three payroll providers for a 12-person US team" returns a table with real prices, real limitations, and a recommendation you can interrogate.
• Questions with stakes — the regulation, the tax rule, the platform policy. Arlo reads the primary source, quotes it, links it, and flags where interpretation ends and a professional should begin.
• Your own tools — half of "research" is internal: what did we say to this customer last quarter, which deals mentioned this feature, what did the retro conclude? Arlo searches your connected tools alongside the web, in one pass.

What a good answer looks like

One message, three parts: the answer (direct, opinionated where you asked for judgment), the evidence (sources linked inline, quoted where wording matters), and the honest edges (what couldn't be verified, what's behind a paywall, what changed recently enough to re-check). If a claim matters to a decision, you can tap through and see it yourself — and because every run keeps its audit trail, the sources are still attached when someone asks "where did this number come from?" three weeks later.

Research that lands somewhere

A research agent that answers in chat and evaporates is half a tool. Arlo files results where your team will find them again — the CRM record, the Notion page, the deal doc — as a side effect of answering. Ask once, keep forever. And for recurring questions ("what changed in our space this week?"), the research becomes a standing brief delivered on schedule, not a task someone has to remember.

Frequently asked questions

What is an AI research agent? An AI that works real questions — markets, prospects, vendors, regulations — across the open web and your connected tools, and returns sourced, checkable answers instead of link lists.

How is it different from asking ChatGPT? Grounding and delivery. Arlo researches live sources and your own tools, cites what it found, flags what it couldn't verify, answers in the thread your team already uses, and files the result where it belongs.

Can it monitor a topic continuously? Yes — standing watches on competitors, keywords, or market topics that report to your channel when something genuinely changed, not on a timer full of nothing.

Does it make things up? It's built not to: answers are grounded in retrieved sources, uncertainty is stated, and unverifiable claims get flagged rather than smoothed over. The sources stay attached for checking.

What does it cost? Arlo starts free — no card needed — with paid usage tiers from $20/month for heavier research loads.