Almond vs GoMarble Brand IQ: two takes on giving AI your brand context

If your AI ad tool keeps writing copy around a claim Legal killed, or proposes a test you already ran and killed in Q1, the problem is not the model. The problem is that the model has no memory of your business. Both GoMarble Brand IQ and Almond are trying to solve that, but they start from different definitions of what "context" means.

What GoMarble Brand IQ positions itself as

Based on its public materials, GoMarble Brand IQ positions itself as a brand intelligence layer for ad creative and analysis. The stated idea is that an AI running ad analysis should know your brand guidelines, your tone, your visual identity, and your product positioning before it evaluates creative or makes recommendations. GoMarble's broader product positions itself as an AI performance marketer, and Brand IQ appears to be the component that keeps that AI grounded in who the brand actually is.

That is a real problem worth solving. Generic AI tools have no idea whether a headline fits your brand voice or whether a product claim is still accurate. Brand IQ, based on what GoMarble publishes, is designed to close that gap on the creative and brand side.

Verify current features, pricing and availability directly at GoMarble's site before making any decision.

What Almond focuses on

Almond's scope starts from a different premise. The argument behind it is that great performance marketing is roughly 70 percent business context and 30 percent ad platform knowledge. Most AI tools have solved the 30 percent. The other 70 percent, which includes why the account is structured the way it is, what tests were run and killed, what leadership decided on Monday, and what a competitor just launched, never makes it into any system.

Almond is built as a Business Context Engine, also called an agentic memory layer. It connects to Google Ads, Meta, Slack, Notion, Google Sheets, email, meeting notes and CRM in read-only mode, typically in about four minutes. Everything that comes in is classified by relevance, recency and business impact before an agent ever sees it. The context compounds over time rather than expiring at the edge of a prompt window.

The brand line is specific: World's First Agentic Brain for Autonomous Performance Marketing.

Where the scope differs

Sources of context

GoMarble Brand IQ, based on its public positioning, focuses on brand-level inputs: guidelines, creative assets, product information and positioning. That is a defined and bounded set of sources.

Almond pulls from a wider set of operational sources:

• Ad platform data (Google Ads, Meta)
• Slack threads and channels
• Email
• Meeting recordings and notes
• Notion and Google Sheets
• CRM data (read-only)

The intent is to capture not just what the brand is, but what the team has decided, tested, approved, rejected and discussed. A budget shift in Monday's meeting updates the context on Monday. A creative claim that Legal flagged in an email thread gets captured without anyone writing a brief.

Type of context captured

Brand IQ, based on GoMarble's public materials, is oriented toward brand and creative consistency. The context it stores appears to be relatively stable: who you are as a brand, what your guidelines say, how your products are positioned.

Almond is oriented toward decision history. The context it stores is dynamic and time-stamped: what you tested, what the result was, why you killed it, what the new KPI is after the last QBR, which campaign the team decided to sunset. This is versioned and auditable, meaning you can always answer the question of what the agent knew and when it knew it.

These are genuinely different things. One tells the AI who the brand is. The other tells the AI what the team has been doing and why.

Compounding over time

One of the more concrete problems Almond is built to address is the prompt window ceiling. A standard AI interaction holds weeks of context at best. Last peak season's learnings do not carry into this one. When your best account lead resigns, the reasoning behind every account decision leaves with them.

Almond retains every test, result and decision in a versioned store. The practical difference: instead of an agent proposing a test you already ran and killed in March, it surfaces the specific variant worth running and explains why the original failed on creative rather than targeting. That kind of institutional memory is not something brand guidelines capture.

Agency isolation

For agencies managing multiple clients, context isolation is not optional. One client's strategy surfacing in another client's recommendation is a serious problem, and a prompt-level instruction is not an adequate control.

Almond enforces client-scoped isolation at the data layer. Every context store is isolated per client and per team. GoMarble's public materials do not describe its isolation architecture in comparable detail, so verify that directly with their team if it is a requirement for you.

Bring your own LLM

Almond supports bring-your-own LLM, meaning you can run agents on models your security team has already approved. Your business context, which includes meeting notes, budget decisions and client strategy, never crosses a model boundary that nobody signed off on. This matters for agencies and in-house teams with legal or compliance constraints around where data goes.

GoMarble's public materials do not describe a comparable BYO LLM option. Verify with them directly.

Products built on the context layer

Almond's context engine powers three products:

Canvas is an AI visual dashboard that shows live charts, tables and AI answers grounded in your actual business context. Templates cover audits through ad hoc analysis and output is client-ready on export.

Workflow Builder has 30 prebuilt Google and Meta automations that run on judgment rather than triggers. Because every workflow draws on the context engine, it will not scale a campaign your team decided to sunset on Monday. The people who know the account can build the automation without engineering support.

Integrations are built on open protocols including MCP, so your context works inside Almond's tools or in any AI client your team already uses.

GoMarble positions itself as a more end-to-end AI performance marketer, handling analysis and recommendations within its own interface. The two products reflect different architectural choices: GoMarble appears to be a self-contained system; Almond is designed to be a context layer that other tools, including tools you already use, can draw on.

The honest comparison

These two products are not direct substitutes. They overlap in the sense that both try to give AI more accurate grounding in your specific business rather than generic internet knowledge. But they are solving adjacent problems.

If your primary pain is that your AI writes creative that does not match your brand voice or references outdated positioning, GoMarble Brand IQ is addressing that directly, based on its public materials.

If your primary pain is that your AI has no memory of what your team has decided, tested and killed, keeps proposing work that contradicts last Monday's strategy call, or loses all institutional knowledge when a senior person leaves, that is the problem Almond is built around.

For agencies, the distinction sharpens further. The problem Almond's agency notes describe is that nine years of pattern recognition across forty accounts is the actual product, and it currently lives in people rather than systems. Brand IQ addresses the brand side of that. Almond is trying to address the decision and institutional-knowledge side.

Where Almond fits

Almond is in early access. Every team that joins gets a detailed 1:1 Meta and Google ad account audit with the CEO, recorded. The waitlist takes a work email.

If you are running Google and Meta for multiple clients or a large in-house account, and the specific problem is that your AI keeps operating on stale or incomplete business context, the audit alone is worth the conversation. You can join the waitlist at almond.ai.