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# Best AI tools for journalist research in 2026
- URL: https://intelligence.artificialrealitymedia.com/best-ai-tools-journalist-research-2026/
- Published: 2026-09-02T12:09:00.000Z
- Updated: 2026-09-02T12:09:00.000Z
- Description: Propel PRM, Intelligent Relations, Shadow and Pressmaster.ai for media list building, what each does, and the six tests to run before you buy.
- Author: Galina Fendikevich
- Tags: Public relations, #For AI Buyers, #AI tools, #Import 2026-09-03 13:12

Four AI-native tools are worth examining for journalist research in 2026: [Propel](https://propel-ai.com/?ref=intelligence.artificialrealitymedia.com) PRM, [Intelligent Relations](https://intelligentrelations.com/?ref=intelligence.artificialrealitymedia.com), [Shadow](https://shadow.inc/?ref=intelligence.artificialrealitymedia.com) and [Pressmaster.ai](https://pressmaster.ai/?ref=intelligence.artificialrealitymedia.com). Sam Polstein, who leads communications and content for deep tech startups at HAUS, still builds media lists by hand and says a three-week data lag makes a tool useless. Test all four against that standard before you move budget off [Muck Rack](https://muckrack.com/?ref=intelligence.artificialrealitymedia.com) or [Cision](https://www.cision.com/?ref=intelligence.artificialrealitymedia.com).

*These products were identified by Artificial Reality as apparently relevant to requirements this operator stated. They are not ranked, not independently tested, and were not named or endorsed by him.*

## What is journalist intelligence software?

**Journalist intelligence software helps communications professionals decide which reporters to pitch — analyzing recent coverage, beat patterns, outlet moves and past responsiveness to assess whether a specific journalist is likely to be interested in a specific story.**

It differs from the incumbent category by what it does with the data. A media database stores contacts and clips and lets you filter them. Journalist intelligence attempts the judgment: not who has written about this topic, but who will write about it next. The first is a lookup, the second is a prediction, and the prediction is why this category is hard.

## Propel PRM

**Best fit for:** agencies wanting journalist recommendations driven by live response data rather than static clips.

**What it does:** AI-native PR relationship management that recommends journalists using deep learning trained on live pitch, response and coverage data. It markets explicitly against Muck Rack and Cision pricing. Seed, $4.5M, NFX.

**Why it may fit:** training on pitch-and-response data rather than published clips alone is the most relevant design choice in this set. Whether a journalist *replied* is a stronger signal about future interest than what they published.

**What to verify:** whether the response data pool covers deep tech and hardware beats specifically. A model trained mostly on consumer and B2B software placements will be confident and wrong about a semiconductor reporter.

## Intelligent Relations

**Best fit for:** teams wanting story-to-reporter fit scoring at a fraction of incumbent cost.

**What it does:** an AI system named Preston that matches stories to reporters from a database of more than 200,000 journalists, using fit scoring trained on successful placements. Priced at around $250 a month against roughly $20,000 a year for incumbents. Early stage, 60+ team.

**Why it may fit:** training on successful placements is outcome-based rather than keyword-based, which is the right direction, and the pricing makes it testable without a procurement decision.

**What to verify:** how the fit scoring handles the recency inversion — whether a story a journalist covered last week counts as a strong match or a disqualifier. Ask directly. That single behaviour separates a real product from a similarity search.

## Shadow

**Best fit for:** agencies looking to consolidate a sprawling tool stack around an intelligence layer.

**What it does:** a "PR operating system" with an intelligence layer and AI agents, positioned to replace the eight-to-twelve tool agency stack, working on strategic fit and narrative tracking rather than keyword lists. Early stage.

**Why it may fit:** that stated positioning is the closest in this set to the tangential thinking Sam Polstein describes — laddering up to a broader theme rather than matching keywords.

**What to verify:** positioning is easier to write than to build. Ask for a live demonstration against a named reporter on a beat you know well, and judge whether the reasoning is something you would accept from a colleague.

## [Pressmaster.ai](https://pressmaster.ai/?ref=intelligence.artificialrealitymedia.com)

**Best fit for:** founders and small teams doing PR without an agency.

**What it does:** an AI PR platform for founders, SMEs and agencies covering strategy, article creation and targeted press distribution at a fraction of enterprise PR stack cost. Pre-seed, $2M.

**Why it may fit:** it is included for completeness on the distribution side, and it is aimed at a different buyer than an established agency.

**What to verify:** this sits closer to the do-it-yourself tier Polstein describes — clients plugging a brief into [ChatGPT](https://chatgpt.com/?ref=intelligence.artificialrealitymedia.com) and getting, in his words, "like, I don't know, around the mean, like maybe around fifty percent" — than to a professional journalist intelligence tool.

## How should you test these before buying?

Run the same trial on each, using accounts you already understand. Six checks, in order:

- **Test on a beat you know cold.** Pick five reporters you have strong opinions about and see whether the tool's assessment matches yours. Where it disagrees, ask why. That answer is the product.
- **Run the recency test explicitly.** Give it a story a target journalist covered eight days ago. A tool that scores that as a strong match has built a similarity engine, not a media list tool.
- **Ask where the data comes from and how fresh it is.** Polstein's threshold is that a three-week lag makes output useless. Get a specific answer, not "real time."
- **Ask whether it learns from your outcomes.** Your own pitch results are the most valuable signal in this problem and the one no vendor starts with.
- **Ask whether it will disagree with you.** Present a bad idea and see what happens. A system that validates a poor target is worse than a spreadsheet.
- **Check that your private notes are exportable.** The judgment your team accumulates about individual reporters is the agency's asset, not the vendor's.

## What none of them does yet

None is documented as arguing back. Every product here, incumbent and AI-native, outputs a list or a score, and the operator is asking for a colleague who says *no, they just covered that.* Price the cheapest option against what you spend now — several entrants position directly against roughly $20,000-a-year incumbents — and run one account through it in parallel for a month before you cancel anything.

## Where this comes from

S1E4: He runs PR for deep tech startups — the full interview with Sam Polstein. Listen or watch: [YouTube](https://www.youtube.com/watch?v=Oo5rT9aS2RQ&ref=intelligence.artificialrealitymedia.com), [Spotify](https://open.spotify.com/episode/3QYcgc5ezkSMvH36WJd7qv?ref=intelligence.artificialrealitymedia.com) or [Apple Podcasts](https://podcasts.apple.com/us/podcast/he-runs-pr-for-deep-tech-startups-if-your-agency-isnt/id6780699025?i=1000773200320&ref=intelligence.artificialrealitymedia.com).