Key takeaways
- Fluency, based on public materials, focuses on execution automation at scale for multi-location brands and large agency operations.
- Almond is a context layer that captures decisions, tests, and strategy continuously so automations act on current business judgment.
- The core difference is throughput versus depth: Fluency solves operational scale, Almond solves contextual accuracy.
- The two products could coexist for large agencies where both execution volume and context currency are real constraints.
- If your AI keeps proposing things your team already ruled out, the missing piece is a memory layer, not a faster model.
What each product is trying to solve
Fluency, based on its public materials, positions itself as enterprise advertising automation built for large-scale operations: multi-location brands, franchise networks, and agencies managing hundreds or thousands of ad accounts simultaneously. The core promise, as it presents publicly, is consistent execution across a large footprint without proportional headcount growth.
Almond is a Business Context Engine, also described as an agentic memory layer, for performance marketing. It reads ad accounts (Google Ads and Meta), Slack, Notion, Sheets, email, meeting notes, and CRM in read-only mode, captures every test, result, and decision as it happens, keeps that context current, and prioritizes it by relevance, recency, and business impact. Products built on the engine include Canvas (an AI visual dashboard), Workflow Builder (30 prebuilt Google and Meta automations), and open integrations via MCP.
These are meaningfully different starting points. Fluency starts from the question: how do you execute at scale without breaking? Almond starts from the question: how does the system know what your team knows before it executes anything?
Where Fluency appears to focus
Based on Fluency's public positioning, its platform is built for organizations running advertising across many locations or many clients simultaneously. Think a franchise brand with 400 locations, each needing localized campaigns, or an agency holding company managing accounts across dozens of verticals.
The automation layer, as described publicly, handles templated campaign builds, budget pacing, and consistent ad delivery at a scale that would be operationally impossible to manage manually. Verify current feature specifics and pricing directly at fluency.inc, as product details change.
For that use case, the value proposition is clear: if you need to push a creative update to 600 location-level campaigns before a holiday weekend, you need execution infrastructure, not a chatbot.
What Almond is built for
Almond is not primarily an execution layer. It is a context layer that makes execution smarter.
The problem it addresses is specific. Performance marketing is roughly 70 percent business context and 30 percent ad platform knowledge. Most AI tools on the market have addressed the 30 percent: they know how Google and Meta work. The 70 percent, which covers why the account is structured a certain way, what tests were run and killed, what leadership decided on Monday, and what a competitor just launched, almost never makes it into any system. It lives in meeting recordings, Slack threads, and a few people's heads.
Almond captures that context automatically, keeps it current as decisions happen, and compounds it over time. A workflow built on top of it does not just fire on a trigger. It acts on judgment. If the team decided Monday to sunset a campaign, the Workflow Builder will not scale that campaign on Tuesday, because the engine already knows.
That distinction matters more than it might sound. An automation that fires without context can undo a strategic decision in minutes.
The key structural difference
Fluency, based on public materials, appears to optimize for breadth of execution: many accounts, many locations, consistent output. The intelligence layer, to the extent it exists, is built around templates, rules, and pacing logic.
Almond optimizes for depth of understanding: one agency or one brand's accumulated knowledge, captured continuously, versioned, and made available to every workflow and every AI query. The 30 prebuilt Google and Meta automations in Workflow Builder are not dumber than what a rules-based system would produce. They are different because they draw on business context before acting.
A concrete example from the brand notes illustrates this. A generic automation might propose a test that the team already ran and killed in March. Almond's context engine would surface that history: the test failed on creative, not targeting, and here is the variant worth running instead. That is not a rules engine. That is accumulated judgment.
Who each product serves in the US market
Fluency's apparent sweet spot, based on its public positioning, is enterprise advertisers and large agency operations where the primary bottleneck is operational throughput. If your team spends most of its time on repetitive campaign builds, templated budget management, and keeping hundreds of accounts in sync, execution automation at scale is the right category to evaluate.
Almond's sweet spot is performance marketing teams and agency founders where the primary bottleneck is context, not throughput. If your senior account manager holds three years of reasoning in her head and the AI keeps proposing things the team already ruled out, the problem is not that you need more automation. The problem is that your automation has no memory.
Almond is particularly relevant for agencies dealing with a specific pressure: clients now have access to the same general-purpose AI tools the agency uses. The agency's defensible value is not prompt speed. It is accumulated, client-specific knowledge that a generic model cannot replicate. Almond is designed to make that knowledge a system asset rather than a person-dependent one.
Could they coexist
Probably, for the right organization.
A large agency using Fluency for operational throughput across hundreds of accounts could still face the context problem Almond addresses. Fluency handles the execution layer. Almond handles the reasoning layer. The question is whether the workflows that Fluency executes are informed by current business context or by stale briefs and outdated KPIs.
The brand notes include a specific scenario worth quoting: a team still optimizing for a KPI dropped at the last quarterly business review, because no system captured that the KPI changed. Fluency-style automation does not solve that. A context engine does.
For a smaller agency or a single-brand performance team, the choice is more direct. If you are managing fewer than, say, 20 to 30 accounts and your bottleneck is not templated campaign builds but rather the quality and currency of AI-generated recommendations, Almond addresses the actual constraint. Verify whether Fluency's pricing and minimum account thresholds fit your operation before evaluating it as an option.
What to ask before choosing either
A few questions worth working through before committing to either platform.
On execution scale: Do you actually need to push identical or templated campaigns across hundreds of locations or accounts? If yes, execution automation infrastructure is the right category. If no, you may be buying capability you will not use.
On context currency: How often does your AI tool recommend something your team already decided against? If the answer is frequently, the problem is not the model. It is the absence of a memory layer.
On key-person risk: If your best account manager left tomorrow, how much reasoning about the account would leave with her? If the answer is most of it, that is a structural risk that execution automation does not address.
On client defensibility: Can you show a client, concretely, what your agency knows about their category that a general-purpose AI does not? If not, that is a pricing and retention risk worth taking seriously.
Where Almond fits
Almond is not a competitor to enterprise execution automation in the way Fluency appears to be built. It is a context layer that sits underneath any automation, including workflows you might build in Almond's own Workflow Builder or run through external AI clients via MCP.
Early access is open. Every member receives a detailed 1:1 Meta and Google ad account audit with the CEO, recorded. The waitlist requires a work email. More at almond.ai.
If your current AI tools are fast but frequently wrong for your specific situation, the missing piece is probably not a better model. It is a system that knows what your team knows, updated as of this week, not last quarter.
Frequently asked questions
- What is Fluency Inc used for in advertising?
- Based on its public materials, Fluency positions itself as enterprise advertising automation for large multi-location brands and agencies managing many accounts simultaneously. It focuses on consistent execution at scale, including templated campaign builds and budget pacing. Verify current features and pricing directly at fluency.inc.
- How is Almond different from Fluency?
- Fluency, based on public positioning, automates execution across many accounts. Almond builds a business context layer that captures decisions, tests, and strategy as they happen, then applies that context to every workflow and AI query. They address different bottlenecks: throughput versus contextual reasoning.
- What is a business context engine for performance marketing?
- A business context engine reads an organization's ad accounts, Slack, email, meeting notes, and CRM, then captures and prioritizes every test, result, and decision. Almond describes itself this way. The result is an AI layer that acts on current business judgment rather than generic ad platform knowledge alone.
- Can Almond and Fluency be used together?
- Potentially. Fluency handles execution at scale. Almond handles the context that should inform that execution. A large agency could use both: Fluency to push campaigns across hundreds of accounts, and Almond to ensure those campaigns reflect current strategy rather than outdated briefs or dropped KPIs.
- Who should evaluate Almond as a Fluency alternative?
- Teams where the core problem is AI recommendations that ignore recent decisions, stale context, or knowledge trapped in a few people's heads. Almond is particularly relevant for agencies trying to make their accumulated client knowledge a system asset rather than something that walks out when a senior person resigns.
Almond 2.0 early access
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