Priya — AI Research Analyst
Meet Priya, the AI research assistant who keeps tabs on your market so you don't have to.
Priya is an AI teammate for the research that's always worth doing and never gets done — watching competitors, scanning your market, and digging into the topics a decision depends on. Tell her what to track and where to look, and she gathers, reads, and synthesises from primary sources, keeping a living research doc up to date and citing every finding. She works on a schedule and shows her sources, so what you get is briefed and checkable — not a confident guess.
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Weekly competitor scan — 3 competitors tracked
Every change cited to its source
Competitor A changed pricing
Entry tier up 20% — source linked
Competitor B shipped a new feature
Announced on their changelog — cited
Competitor C — no material change
Checked; positioning and pricing steady
What it means for you — 3 bullets
Synthesised takeaways, each traceable
The living competitor doc is updated and the week's changes are summarised with sources — you stay current in a two-minute read.
What does an AI research assistant actually do?
An AI research assistant is an AI teammate that gathers, reads, and synthesises information on a topic or target — competitors, a market, a prospect, a question — from primary sources, and keeps the findings current and cited, so you get a briefed answer instead of a search-results page.
Priya is Kuvai's AI research analyst. She is not a chatbot that answers from whatever it half-remembers, and not a one-off search. You tell her what to monitor and what questions matter — a set of competitors, a market trend, a recurring intelligence need — and she does the legwork: pulling from primary sources across the web and the tools you connect, reading and comparing them, and synthesising a clear answer with every claim linked to where it came from. She keeps a living research document up to date as things change, so you're not re-running the same search next month. Because she cites her sources and shows her work, you can trust the findings or check them in a click — and she stays in research, never giving financial or buy-sell advice.
What problem does an AI research analyst solve?
Research is the work that always loses to whatever's on fire. Keeping up with competitors, tracking your market, briefing a decision properly — it's valuable, it's never urgent, and so it slides. The result is decisions made on stale information and surprises you should have seen coming. That's the job Priya owns.
Competitors change and you find out late
A rival drops their price, ships a feature, or shifts their positioning — and you hear about it from a customer weeks later. Manually checking competitors is the definition of important-but-never-urgent. Priya watches the targets you name on a schedule and flags what actually changed, so you're informed when it happens, not after it costs you a deal.
Proper research takes hours nobody has
Briefing a decision well means reading widely, comparing sources, and separating signal from noise. Done right it's half a day; done in the gaps it's a rushed skim. Priya does the gathering and the first-pass synthesis from primary sources, so you start from a briefed summary with citations rather than a blank tab and a deadline.
The same questions get researched from scratch
Market sizing, a vendor comparison, the state of a trend — these get re-researched every time they come up because last time's work wasn't kept. Priya maintains a living research doc that updates as things change, so the knowledge compounds instead of evaporating after each project.
AI answers sound confident but you can't check them
Generic AI will happily summarise a market without telling you where any of it came from, which is worse than useless for a real decision. Priya cites every finding to its primary source and shows what she read, so a claim is something you can verify, not just trust.
How does an AI research analyst work — and can I trust what it finds?
Priya works from primary sources and shows you all of them. You define what to track; she gathers, synthesises, and cites. Here is the full loop.
Tell her what to track and where to look
Name your competitors, the market or topics you care about, and the recurring questions that matter. Connect the research tools and document sources you want her to draw on. She grounds her work in what you point her at, not the open guess.
She gathers from primary sources
Priya pulls from the web and your connected sources — competitor sites, search results, the tools you've connected — reading the originals rather than summarising summaries. This is what makes a finding traceable instead of hearsay.
She synthesises and cites
She compares and condenses what she found into a clear answer — what changed, what it means, what's worth your attention — with every claim linked to its source. You get the briefing and the receipts together.
She keeps a living research doc current
Rather than a one-off report, Priya maintains a living document that updates as things change — a competitor tracker, a market brief, a topic file — so the research compounds and you're never starting over.
You review, and she watches on a schedule
Read the synthesis, follow any citation you want to check, and act. Set her to monitor on a cadence — weekly competitor scan, monthly market update — and she flags what's new each time. She researches and reports; the decisions, and any advice, stay yours.
Priya cites primary sources and shows her work, so findings are checkable. She stays in research — she never gives financial or buy-sell advice — and every action she takes is logged with its reason.
Where does an AI research analyst fit across different businesses?
Priya owns the same cycle — gather from primary sources, synthesise, cite, keep current — but the research looks different in every business. A few concrete situations:
Always-on competitor monitoring
A small business can't spare anyone to watch the competition, so it flies blind. Priya tracks the named rivals' pricing, features, and messaging on a weekly cadence and flags what changed — keeping a living competitor doc current — so the team reacts to moves in days, not when a customer points them out.
Competitor moves caught in days, with the source linked — no dedicated analyst needed.
Briefing content and campaigns
A content lead needs the landscape before writing — what's been said, what the data shows, where the gaps are. Priya gathers and synthesises the research with citations, so the writer starts from a briefed, sourced foundation instead of an afternoon of open tabs.
Hours of pre-writing research reduced to a cited brief to start from.
Sizing a market or vetting a direction
A founder weighing a new segment needs a fast, honest read on the market and the players. Priya assembles a market brief from primary sources — size signals, key players, trends — every claim checkable, so the decision rests on something more than a hunch.
A sourced market brief in hours, not a week of part-time digging.
Topic and background research
A firm needs background on a company, a sector, or a question for client work. Priya does the primary-source gathering and first-pass synthesis with full citations, so the professional spends their time on judgement and analysis rather than the legwork of finding and reading sources.
The research legwork handled and cited; expert time spent on analysis.
What does an AI research analyst connect to?
Priya draws on Kuvai's research and search tools and writes her findings into the documents your team already uses — always with your explicit approval and only where you allow.
Research & search
Where she writes findings
Your own sources
Notifications
Connecting any tool requires explicit OAuth approval, and Priya only acts within the scopes you grant. She works from primary sources and the tools in Kuvai's catalog, and cites everything she reports.
Can I trust an AI research assistant's findings?
Research is only useful if you can rely on it, so Priya is built to be checkable rather than merely confident. The model is the same as every Kuvai teammate: she shows her work, and you decide what to do with it.
Every finding is cited
Priya links each claim to the primary source she read it in, so you can verify anything in a click. A finding is something you can check, not a black-box assertion you have to take on faith.
She stays in research
Priya gathers and synthesises information — she does not give financial, legal, or buy-sell advice. The judgement calls and the decisions stay with you; she makes sure they're well-informed.
Everything is on the record
Every scan she runs and source she reads is logged with its reason, and findings live in a document you own. You always know what she checked, when, and against which sources — and your data stays yours.
How is an AI research analyst different from ChatGPT, Perplexity, or doing it yourself?
Doing it yourself gives you trustworthy research and costs you the hours you don't have, so it's the work that never happens. A general chatbot like ChatGPT will answer instantly but often without telling you where anything came from — fine for a first pass, dangerous for a decision. A search tool like Perplexity is great for a one-off question but doesn't watch your competitors next week or keep a living brief current.
Priya combines what each does well: she gathers from primary sources and cites everything like a careful analyst, she runs on a schedule so monitoring actually happens, and she keeps a living research doc so the knowledge compounds instead of resetting each time. She accumulates your context — who you track, what you care about — so the research gets sharper, and she works alongside the rest of your Kuvai AI team, with you deciding what the findings mean.
Frequently asked questions
It does the research that's always worth doing and never gets done: monitoring competitors, scanning your market, and digging into the topics a decision depends on. Kuvai's research analyst, Priya, gathers from primary sources, synthesises a briefed answer with citations, and keeps a living research doc current — so a small team stays informed without a dedicated analyst.
More of your AI team
Dakota — AI Knowledge Manager
Dakota is an AI knowledge manager who keeps your SOPs and internal docs current, flags stale content, and answers questions from your own sources — so the team's knowledge stays usable, not buried.
Mia — Inbox Coordinator
Forward Mia any email and its attachments. She reads it against your documents, does the work, and sends back a structured reply — for your review or auto-send.
Know what's happening in your market — without doing the digging.
Start free and put Priya on your competitors and your research today. She gathers, cites, and keeps it current; you decide what it means.