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Marketing intelligence, made legible.

Know where your next marketing pound should go, and have the evidence to defend it.

Your brand tracker, creative tests and media model each tell part of the story, from different suppliers, on different timelines. naras replaces them with one provider and one workspace: how strong your brand is, whether people, search and AI find and recommend you, and what your media earns. Every recommendation comes with a confidence. Risk and reward are automatically balanced.

The tools you’d buy separately, working from the same evidence.

naras replaces brand tracking, creative pre-testing, marketing mix modelling and incrementality testing, and adds a campaign advisor that turns the evidence into better briefs. An assistant explains every part to you, your team and your agency.

Brand strength, availability & AI presence

Your brand funnel, from first awareness to purchase, and four pillars of brand strength, read from people, search and AI. Each reading comes with its confidence level.

Resonance turns the survey, search and AI evidence about your brand into a seven-stage funnel and four strength pillars: market fit, reputation, advocacy and attraction. Most funnel stages pair what people say with what AI models say, so you see where you're losing ground and what to fix first. Each reading opens into its evidence and ends in a recommended action, with the assistant on hand. You re-run the audit whenever you need fresh numbers, rather than keeping a tracker running.

Fig. 1aBehind Differentiation · the trait landscape
Every brand in the category, plotted on how distinctive and how salient it is. Yours sits past the category median, in the top right.
See it in the demo
narasAIthe desk · one assistant for every moduleAsk it to remix your media plan, walk you through running a test, or explain what a diagnostic means. Its answers come from your workspace's numbers and sources.Fig. 1 · Illustrative: five modules, one workspace.

A tracker measures your brand. A mix model measures your spend. naras puts both in one workspace, links what people think of you to what your media earns, and weighs each recommendation by how sure the evidence is.

The best result isn’t always the best bet.

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. A plan built on midpoints alone would pick A.

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.

Fig. 2Return on spend · two strategies · range, not midpoint
Two strategies, each shown as its full range with the mean marked. A's average is higher; B's worst case still clears break-even, so B is the one to back.

One connected picture

Brand and spend, side by side.

The Overview sets what your customers think against what search and AI say. Each signal shows its trend and confidence, the weakest is flagged, and the moves to make are ranked.
Fig. 3The Overview · signals, impact, moves
Signals with their ranges and confidence, the weakest flagged. Media impact sits beneath, and the ranked moves alongside, each with its confidence.

How you run it

Read. Plan. Act. Measure. React.

Strategy runs in a loop. naras handles every stage of it in one workspace.

Fig. 4The lifecycle · one continuous loop
ONEWORKSPACEREADPLANACTMEASUREREACT
  1. 01

    Read

    The brand funnel and four strength pillars, each with its confidence, measure the brand against its competitors. The weakest is flagged first.

  2. 02

    Plan

    The advisor turns the evidence into a working brief, and scenarios play the plan forward with the range shown.

  3. 03

    Act

    You make the move: budgets shift, creative runs. naras proposes and you decide; if you overrule it, that's recorded.

  4. 04

    Measure

    Impact models what the spend earned, channel by channel, as ranges against break-even, and experiments measure what a new strategy adds.

  5. 05

    React

    As new survey waves and results come in, plans re-score and the next move is ranked. Then the loop starts again.

Open a reading

Each reading is a row on one board, with its trend, rank and confidence. Open one to see the detail: Reputation at 86, high confidence, #5 of 29 in the set.

Fig. 5The board · one row opened
One row per reading. Open it to see the score, the rank in the set, and the confidence behind it.

Ask for the move

Ask the desk and it proposes a move: shift about £20k a quarter from Meta (returning under 1×) into Google Search, for an estimated 2–4% uplift.

Fig. 6The desk · ask, and the move composes
Ask, and the desk builds the move: the shift, its cost, the estimated uplift as a range, and the records behind it.

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

Five tools. One workspace.

Five suppliers, five sets of numbers, and nobody joining them up. naras replaces the separate tools for brand and media effectiveness, and each one maps to a naras module.

  • Replaces

    Brand tracking

    An always-on tracker and a quarterly deck.

    naras Resonance

    A seven-stage funnel and four strength pillars, each with its confidence, linked to its evidence and a recommended action. Re-run it when you need fresh numbers.

  • Replaces

    AI visibility monitoring

    A point tool watching one assistant.

    naras Resonance · AI signals

    Weekly audits of the assistants buyers ask, across three vendors and per market, read into the same board as the survey.

  • Replaces

    Creative pre-testing

    Panels, fieldwork and weeks of lead time.

    naras Synthetic Research

    Test concepts and messages on a simulated population across three AI models. Results come back as ranges in minutes, and validation limits what they can claim.

  • Replaces

    Econometric modelling

    A consultancy engagement and a static report.

    naras Impact

    Channel returns with credible ranges, calibrated against lift tests and held-out periods, explained in plain language.

  • Replaces

    Incrementality testing

    Lift studies scattered across ad platforms, agencies and slide decks.

    naras Impact · Experiments

    Every experiment in one library: platform lift studies you add, and geo tests naras designs and analyses. Each keeps its range and a quality grade, and feeds the model once you approve it.

All five use the same method: start from the evidence, show the range, and weigh each recommendation by its confidence.

How it works

Bring your context. naras does the work. You read, ask, act.

  1. 01

    Bring your context

    A brand and category for Resonance; a spreadsheet of media and sales history for Impact. Setup is a short conversation. We do most of the work, and your data stays yours.

    Already measuring? Tracker history and audience definitions carry over, and past lift tests sharpen the model's starting point.

  2. 02

    naras gathers and models

    Resonance collects live evidence and audits the AI systems buyers ask; Impact models what's driving your results. The search and AI audit is quick; a commissioned survey takes weeks, not the months a tracker takes to stand up.

  3. 03

    Read, ask, act

    Explore clear reports, ask the assistant anything, and export decision-ready summaries for the people who need them.

Resonance · the method

Evidence first. Scores second.

naras Resonance reads four kinds of evidence about your brand, then turns them into scores you can act on. Every score traces back to a record you can open.

Four kinds of evidence

  • SurveyCommissioned · real respondentsBrand-health and perception data from a survey commissioned for your brand (synthetic in the demo).New waves take weeks to field, and you choose when to run them.
  • SearchGathered liveWhat organic results, Knowledge Graph and Google Trends say about you.
  • AI OverviewGathered liveHow Google’s AI summary treats you in category searches.
  • AI auditsGathered liveWhat OpenAI’s, Anthropic’s and Google’s models say when asked to recommend in your category, refreshed weekly.

Brand funnel · seven stages

  1. 01Spontaneous awareness
  2. 02Ad awareness
  3. 03Prompted awareness
  4. 04Unprompted consideration
  5. 05First choice
  6. 06Purchase intent
  7. 07Customer

Brand strength · four pillars

  1. 01Market Fit
  2. 02Reputation
  3. 03Advocacy
  4. 04Attraction

It tells you which way to move and how hard to push.

Four things true of every module.

Evidence you can see

Open any score to see the data, sources and reasoning behind it.

Honest about uncertainty

Every result comes with its range and its confidence.

An assistant that explains

Ask in plain English. Answers come only from your own data.

Designed to be read

Clear, uncluttered pages that work on any device and meet WCAG AA accessibility.

Common questions

The things buyers ask us first.

How rigorous is this, and how far should I trust the numbers?

We report every result with its margin. Each signal carries a confidence rating based on the sample size, recency and consistency of the evidence behind it. Impact's Bayesian model goes further, reporting the estimate, its range, and how much of that range your data supports. A wider range calls for a more cautious move, and that is reflected in every recommendation and in scenario planning. Where the evidence is thin, we flag it.

Is “AI visibility” actually a proven driver of growth?

We treat it as an emerging signal rather than an established KPI. naras shows whether ChatGPT, Claude and Google’s AI mention, rank and recommend you when buyers ask, since more buyers now start there. We don’t claim a fixed link to sales, and AI answers change with each model update, so it works best as an early-warning indicator. It sits alongside funnel measures such as awareness and consideration, which have decades of evidence behind them.

Where does the survey data come from, and will it clash with my existing tracker?

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.)

What is a simulated panel? Is Synthetic Research real research?

Synthetic Research is a tool for testing hypotheses. Its respondents are modelled on your workspace’s survey data rather than invented personas, and each question is run across three AI model families so that no single vendor’s biases determine the answer. Results are reported as ranges, and a published validation against held-out human survey data determines what each cut may claim; anything not yet checked is withheld, and we name it. Use it to narrow the options in minutes, then confirm the winner with real research. It does not replace fieldwork, and it does not produce brand-health figures.

How long does it take, and how much of my team’s time?

Setup takes a short conversation, and we do most of the work. The search and AI audit 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.

How is our data handled, and kept separate from your other clients?

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.

Can my agency run naras across a client roster?

Yes. naras is built for it: each client brand gets its own workspace, kept 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.

What does it cost?

Pricing depends on scope: which modules, how many markets, and how often you run. Tell us what you need and we will give you a clear price up front.

Do you have clients and case studies?

We’re early, and working with our first cohort of brands. We don’t borrow logos or invent results: the figures on this page are drawings of the product, and their numbers are illustrative. Ask us what we can show you.

Something we haven’t answered? hello@naras.ai, or ask in the form below.

Get in touch

Bring us one decision.

Tell us the marketing decision you’re facing next. We’ll show you, on your own brand and spend, what the evidence says and how sure it is.

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