Munshi: Message Intelligence for Communicators

Grounded in real data

Generic AI makes things up.
Munshi looks them up.

While generic AI hallucinates facts to tell you what you want to hear, Munshi keeps your audience top of mind and grounds every answer in over 20 trusted databases.

Every claim links to its source. Sources are tier-rated, from authoritative .gov records to major journalism, so you know what's solid before you cite it.

Six research modes

Constituency Research How a community thinks, what moves them, and what language resonates. Audience Research Who an audience is, what they value, who they trust, and the language that reaches them. Government Affairs An official's priorities, hot-button issues, and how to approach them. Opposition Research Vulnerabilities, contradictions, donor exposure, and attack lines. People & Organizations Deep, cited background on any person, company, nonprofit, or agency. Media Monitoring Current coverage, key journalists, and the narrative landscape.

And when it's time to ship: Reach the Press finds the journalists most likely to cover your story, drafts a personalized pitch for each, and pulls verified contact emails for any outlet or organization.

Compared to traditional research and consulting

The depth of a research firm. In minutes, not weeks.

Communications advisors charge thousands for research, polling, profiling, and messaging, with turnaround measured in weeks. Munshi delivers comparable depth in minutes, at a fraction of the cost.

Task	Through a consultant	If your staff does it	With Munshi
Profile an elected official or decision-maker	$20,000–$80,000+ per research book, 2–4 weeks^2,3	4–6 hours reading votes, statements, and recent press coverage⁴	Minutes
Research a person, organization, or significant figure	Engaged via $5,000–$30,000+ / month retainer⁵	3–5 hours digging through filings, news, and public profiles⁴	Minutes
Scan the media landscape on an issue	Part of $3,500–$30,000+ / month PR retainer⁵	3–4 hours pulling recent articles and identifying key reporters⁴	Minutes
Research a constituency or district	$30,000+ per original survey, 2–3 weeks of fieldwork¹	4–6 hours pulling Census, FEC, and public polling data⁴	Minutes
Build a media target list with personalized pitches	$3,500–$30,000+ / month PR retainer⁵	6–10 hours sourcing journalists and writing pitch emails⁴	Minutes

Munshi works from existing public sources — Census, FEC, court and agency records, public polling and election returns — not original fieldwork. It replaces the desk research and synthesis a firm bills hours for, not a new survey or investigation.

¹Roll Call, 2026 · ²City & State New York, 2024 · ³FEC filings via OpenSecrets · ⁴Estimated from standard communications research workflow · ⁵Clutch PR pricing benchmarks

See it in action

Every suggestion is backed by real research.

Click any highlighted phrase to see Munshi's suggested edit and the reasoning behind it.

Analyzing draft against district research…

usemunshi.com · Constituency Research · PA-07 Suburban District

Your draft: Campaign statement

Families in this district are being priced out of the homes they grew up in. Housing costs have risen 34% in the last four years while wages have barely moved1. My opponent has voted against the Affordable Housing Expansion Act twice and has taken over $180,000 from real estate developer PACs.

I will fight to build more housing2, protect renters from predatory rent increases3, and ensure that working families can afford to stay in the communities they've built.

This isn't a complicated problem. It's a political will problem. And I have the will to solve it.4

← Click any highlighted phrase
to see Munshi's suggestion

High word choice

"while wages have barely moved"

Suggested edit

"while real wages have risen less than 3%"

You opened with a hard number (34%). A second hard number (same sentence structure, same time window) makes the contrast land. The gap between the two figures is the actual argument.

BLS Real Earnings (real average hourly earnings, 2020–2024): cumulative growth in the low single digits; see bls.gov/news.release/realer.nr0.htm

Edit 1 of 4

Med audience risk

"build more housing"

Suggested edit

"build the right housing in the right places"

Suburban homeowners are a large share of likely voters in this district. "Build more housing" unqualified tends to trigger density and NIMBY concerns with this group. Specificity neutralizes the risk without weakening the commitment.

Backed by national housing-message research patterns (Pew, PerryUndem, Harvard Joint Center for Housing Studies). District-specific testing recommended.

Edit 2 of 4

Low tone

"predatory rent increases"

Suggested edit

"unchecked rent increases"

"Predatory" polarizes: strong with base voters, but reads as overreach to moderates and swing voters. "Unchecked" carries the same policy concern with less rhetorical risk.

Based on word-choice testing patterns from swing-district message research (Lake Research, Hart Research, Public Opinion Strategies).

Edit 3 of 4

High structure

"This isn't a complicated problem. It's a political will problem."

Suggested edit

"In my first 90 days, I'll introduce housing-affordability legislation, and I won't stop until working families can afford to stay."

Diagnosis without commitment is the most common closer mistake in campaign writing. Swing voters consistently rank a specific deliverable as a stronger credibility signal than an abstract call to action.

Pattern consistently observed in published swing-district focus-group research (GQR, GBAO Strategies, Garin-Hart-Yang).

Edit 4 of 4

See all three demos in action →

Know how your message will land as you write it.

Generic AI makes things up.
Munshi looks them up.

The depth of a research firm. In minutes, not weeks.

Every suggestion is backed by real research.

Every word counts. Make them land.

Know how your message will land as you write it.

Generic AI makes things up.Munshi looks them up.

The depth of a research firm. In minutes, not weeks.

Every suggestion is backed by real research.

Every word counts. Make them land.

Generic AI makes things up.
Munshi looks them up.