Evidence you can see
Nothing is a black box. Open any score and you'll find the data, sources and reasoning underneath it.
Marketing intelligence, made legible.
naras reads how strong your brand is, and whether you’re found and recommended by people, by search and by AI. Then it connects that to what your media earns: return on spend, cost of acquisition, growth. One picture, honest about what it doesn’t know.
naras replaces brand tracking, creative pre-testing and marketing mix modelling, then combines them with a campaign advisor that turns the evidence into better briefs. Every part explained by an assistant that helps you, your team and your agency understand, and act.
Salience, distinctiveness, perception and reputation, plus whether people, search and AI recommend you. Every signal shown with its confidence.
Resonance turns the survey, search and AI evidence about your brand into ten clear signals: where you're losing ground, and what to fix first. Each signal opens into its evidence and ends in a recommended action, with the assistant on hand. It's an audit you re-run when you need a read, not an always-on tracker.
The econometric model of what each channel earns and how that changes over time: return on spend, and where the next pound grows you.
Upload your media and sales data. Impact models what drove your results, channel by channel: what your spend earns, where returns diminish, and where the next pound grows you. Estimates are calibrated against real-world lift tests and checked on held-out periods, and the assistant explains it all in plain language.
Pre-test creative and messages, or field your own questions, on a simulated population read across three AI models. Ranged, validation-gated, minutes not weeks.
Two instruments on one engine: pre-test creative and messages, or field your own question battery, on a simulated population built from your real survey data. Every read comes back as ranges across three AI models, and a published validation gate decides what each cut may claim. A hypothesis tool before validated research, never a replacement for it.
Turns the evidence in your workspace into a campaign brief: objectives, audiences, channel roles and a ranged forecast. It proposes; you decide.
The advisor reads your brand's signals and what your spend has earned, then drafts a working brief: objectives, audiences, channel roles, messages and a ranged forecast. Your team shapes, challenges and approves it, and an overrule is a recorded decision, not a silent change. Strategy stays yours.
Model the move before you make it: shift spend, change the mix, and read the projected effect on brand and sales, with the range shown.
Take what your workspace already knows and play the plan forward: shift spend, change the mix, and read the projected effect on brand and sales with its range. Where the evidence is strong it backs the move; where it's thin, it recommends a tentative test, not a bet. Plans re-score as new evidence lands.
A tracker reads the brand. A mix model reads the spend. The naras workspace joins them, what the market thinks to what your spend earns, and prices confidence into every move it recommends.
Two strategies. A promises the higher return, on a range so wide it dips below break-even. B promises a little less, on evidence you can bank. Most tools hand you the midpoints and call it a plan.
naras shows the range behind every number, and builds that confidence into scenario planning. We don’t chase the best-looking result: we back the results we’re confident in, and scale tentatively into the ones we’re not.
One connected picture
How you run it
Strategy is not a report, it’s a loop. naras runs the whole cycle on one bench, and every turn of it is priced with confidence.
Ten signals with confidence read the brand against its competitor set, and the weakest is flagged first.
The advisor turns the evidence into a working brief, and scenarios play the plan forward with the range shown.
You make the move: budgets shift, creative runs. It proposes; you decide, and an overrule is a recorded decision.
Impact models what the spend earned, channel by channel, as ranges against break-even.
New waves and results land, plans re-score, and the next move ranks itself. The loop turns again.
Every signal is a row on one board: its trend, its rank, its confidence. Open one and the read unfolds in place: Brand Salience at 86, high confidence, #5 of 29 in the set.
Ask the desk and get the move: shift about £20k a quarter from Meta (returning under 1×) into Google Search, for an estimated 2–4% uplift given as a range, never a promise.
The figures on this page are drawings of the product, and the worked numbers are illustrative. Your workspace runs the loop on your own evidence: a survey commissioned for your brand, plus live search and AI audits.
The stack, consolidated
naras replaces the point tools around brand and media effectiveness, and joins what they never could: what the market thinks to what your spend earns.
An always-on tracker and a quarterly deck.
Ten signals with confidence, each opening into its evidence and ending in a recommended action. An audit you re-run when you need a read.
A point tool watching one assistant.
Weekly audits of the assistants buyers actually ask, across three vendors and per market, read into the same board as the survey.
Panels, fieldwork and weeks of lead time.
Concepts and messages read as ranges across three AI models on a simulated population, validation-gated, in minutes not weeks.
A consultancy engagement and a static report.
Channel returns with credible ranges, calibrated against lift tests and held-out periods, explained in plain language.
One method under all four: evidence first, ranges always, and confidence priced into every move it recommends.
How it works
A brand and category for Resonance; a spreadsheet of media and sales history for Impact. A short setup conversation, not a project. Most of the work is ours, and your data stays yours.
Already measuring? Tracker history and audience definitions carry over, and past lift tests sharpen the model's starting point.
Resonance collects live evidence and audits the AI systems buyers ask; Impact models what's driving your results. The search-and-AI read is quick; a commissioned survey takes weeks, not the months a tracker takes to stand up.
Explore clear reports, ask the assistant anything, and export decision-ready summaries for the people who need them.
Resonance · the method
naras Resonance reads four kinds of evidence about your brand, then turns them into signals you can act on. Nothing is invented: every score traces back to a record you can open.
Four evidence pillars
Ten signals
Built to tell you which way to move and how hard to lean on it: the range, not just the number, priced into every recommendation.
Nothing is a black box. Open any score and you'll find the data, sources and reasoning underneath it.
Every result comes with its range and its confidence: we show the spread, not just the number.
Ask in plain English and get grounded answers, drawn only from your real data, never invented.
Editorial, legible, uncluttered, on any device, and built to WCAG AA accessibility. Intelligence you actually want to open.
Common questions
We never collapse a result to one number and hide its margin. Every signal carries its confidence: the sample, recency and consistency of the evidence behind it. Impact's Bayesian model goes further and gives you the estimate, the range, and how much of that range your data actually supports. A wider range means a more cautious move, and that's priced into every recommendation and into scenario planning. Thin evidence is flagged, never smoothed over.
We treat it as an emerging signal, not a settled KPI, and we say so. We show where AI represents you: whether ChatGPT, Claude and Google’s AI mention, rank and recommend you when buyers ask, because more of them now start there. We don’t claim a fixed link to sales, and LLM answers shift with each model update, so it’s an early-warning indicator to watch, not a number to game. It sits alongside salience and distinctiveness, which have decades of evidence behind them.
Surveys are commissioned with real respondents through Cint, a major respondent marketplace, with questions customised to your brand and category. If you already run a tracker we can take it over and onboard your history, keeping your audience definitions, segmentation and markets so your trends stay consistent. It replaces the tracker rather than running beside it, so there’s no “paying twice” and no conflicting numbers. (The demo uses synthetic data, clearly marked.)
It’s a hypothesis tool, and we’re precise about that. Respondents are synthesized from your workspace’s real survey data, never invented personas, and every question runs across three AI model families so no single vendor’s biases become the answer. Results come back as ranges, and a published validation against held-out human survey data decides what each cut may claim; anything unchecked is withheld and named. Use it to narrow the field in minutes, then validate the winner with real research. It never replaces fieldwork, and it never produces a brand-health number.
Setup is a short conversation, not a project. Most of the work is ours. The search-and-AI read is quick; a commissioned survey takes weeks (versus the months a tracker takes to stand up). For Impact, a spreadsheet of media and sales history is enough to begin.
Your data stays yours, encrypted in transit and at rest, including the sales data Impact ingests. Every brand gets its own isolated workspace, unreachable from another’s by construction: the application only resolves data for the workspace you’re in, and the database enforces the boundary again with row-level security, so even a coding mistake can’t return another client’s data. We’re glad to take your security or procurement team through the architecture before anything moves.
Yes. It’s a first-class shape, not an afterthought: every client brand gets its own workspace, kept cleanly separate, with its own evidence, signals and assistant. Exports are client-ready, so your team can drop them straight into a plan, a QBR or a pitch. Co-branded and white-label outputs are available on request. Talk to us about agency access.
Pricing depends on scope: which modules, how many markets, and how often you run. Tell us what you need and we’ll be straight about the cost; no surprises.
We’re early, and working with our first cohort of brands. Rather than borrow logos or invent numbers, the worked example uses a fictional brand on clearly-marked synthetic data, so what you see is the real instrument, honestly labelled. Ask us what we can show you.
Something we haven’t answered? hello@naras.ai, or ask in the form below.
Get in touch
A short, tailored walkthrough of naras on your brand and your spend. No slides, just the actual instrument.
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