One purpose. Any mix of sources. One understanding.
n+1 is a research and intelligence platform. It takes large volumes of natural consumer language, from ad conversations to survey responses to web posts and reviews, and turns it into analytics you can filter and chart, a browsable record of every document behind them, a scorecard that states the takeaway, and an analyst you can ask in plain language.
Layered data sources. Conversations your ads start, surveys your studies run, and what people already say across the web.
Four tools on one evidence base. Inspect for the analytics, Evidence for the receipts, Understand for the scorecard, and Ask n+1 for the why.
In the platform, or in your own AI tools. Plug your evidence into Claude, ChatGPT, or your own agents via MCP.
Built around your purpose, not your mentions.
A study starts with a purpose in plain language, broad or pointed, and carries a flight window, sources, and up to three custom focus areas. You define it in one conversation with the Study Architect, which proposes focus areas and sources from the purpose you state.
- Purpose. "Understand what's resonating about the 2026 model's upgrades, and what isn't."
- Focus areas. Up to three dimensions that steer both the in-ad agent and the extraction: worth-it drivers, journey stage, price sensitivity.
- Flight. Studies run on media windows: start, end, pause, resume. Campaigns attached to a study serve only while it's live.
Three sources, one evidence base.
Conversations we start, and what the web already says. More sources as they launch.
Guided interviews
Structured, adaptive studies in premium placements. Three or more genuine answers make a completed response.
AI ResearchOpen brand conversations
What people ask a brand, unprompted, mid-content. Branding that doubles as listening.
Conversational AdvertisingThe open web, scoped
Reviews, forums, communities, articles, gathered against your purpose rather than your brand name.
Web ResearchReads everything whole.
Every conversation and page is read as one document, not message by message, through the lens of the study's purpose. The core extracts the same complete signal set from every source and stamps each document with its origin, so nothing loses its receipt.
The platform reads your study so you don't have to watch it.
While a study runs, n+1 writes the narrative for you. An AI digest covers what's happening across your studies, hourly narratives cover each one, all in plain language rather than a dashboard to decode. When something changes, the story says so.
- Hourly narratives per study while live.
- A dashboard digest summarizing everything that moved.
- Context that compounds. The running narrative grounds Understand and Ask n+1, so every answer starts from context, not a cold search.
Every signal, filtered, charted, trended.
Structured analytics across every conversation and page. Sentiment by US state on a live map, topics as a cloud with sentiment splits, intents, competitor preference, and your focus-area breakdowns, all filterable by study, brand, date, sentiment, intent, language, device platform, OS, and source, and all trended over time.
- Sentiment by state, platform, OS, and language.
- Competitors as prefer-us / no-preference / prefer-them, not just mention counts.
- Paid-media metrics alongside: engagement, completion, and exposure for the ad-borne sources.
The full record, kept and browsable.
Every web page, forum post, review, article, and full ad-conversation transcript your studies collect, browsable and filterable by source, sentiment, study, and brand. A finding is only as good as the record behind it, and the record lives here.
- Full transcripts for ad conversations, with the agent's handling visible.
- Source links for web evidence, back to the original page.
- Per-document extraction shown inline, the same full signal set the core pulls from every source.
Your brand scorecard. No queries to build.
The answer already assembled, with an AI headline and takeaway, top likes and dislikes with the quotes behind them, where to act, competitor standing, and a Resonance Index from 0 to 100 built from sentiment, preference, and commitment. Every sentence cites the evidence that produced it, and the scorecard rebuilds on demand as the study collects.
Top likes, ranked, with verbatims.
Top dislikes, ranked, with verbatims.
Recommendations grounded in real signals.
One index drawn from sentiment, preference, and commitment.
Ask n+1 answers the why.
Plain-language questions against your own evidence: charted answers, source-linked quotes, durable conversations, and branded PDF export. Plug it into Claude or your own agents via MCP.
The full Ask n+1 pageThe standard underneath it all.
Every finding traces to the verbatim conversations and pages that produced it. If a number can't show its receipt, it doesn't ship.
Your evidence, inside the AI tools you already use.
n+1 speaks MCP, the open standard for connecting AI agents to data. Plug your study evidence into Claude, ChatGPT, or anything else that speaks it, including your own agents. Your team asks questions where they already work, and the answers stay grounded in your evidence with quotes cited. Access keys stay under your control.
The full MCP pageYour purpose is worth studying.
State it in plain language. The Study Architect takes it from there.
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