Everyone Is Working Hard. Nobody Is Working in the Same Direction.

01 /The Concept

Brev is an AI platform that lets companies automatically track how effectively they're moving toward their stated goals.

The actual workflow is even simpler than it sounds. A company executive uploads a document describing the organization's objectives. The AI engine converts those objectives into a structured hierarchy of tasks and subtasks, then monitors execution automatically.

Example: leadership sets a goal of growing Net Revenue Retention (NRR) to 120%. Brev's AI suggests accelerating renewal cycles as a contributing initiative, then breaks that down into specific workstreams – all the way down to concrete, measurable tasks assigned to departments, teams, and individual contributors, each tracked by the platform.

To do that tracking, Brev connects to the communication tools in use across the organization – video conferencing, email, and messaging – so the AI can pull signals from where work actually happens. It can surface specific figures and milestones from a recorded meeting that demonstrate measurable progress toward a goal – or note that a meeting, while generally productive, moved the company exactly zero steps closer to its stated objective.

Brev generates daily, weekly, and monthly reports for leadership – framed not as activity metrics but as progress narratives: how effectively is the company moving toward its goal, what actions are working, and what's getting in the way.

Importantly, Brev generates these reports not just for the CEO or executive team, but for every department head and team lead – so that every level of management understands its contribution to, and friction in, the company's collective progress.

Pricing is usage-based rather than subscription: customers pay with credits based on AI token consumption. Enterprise tiers exist for bulk credit purchase, SSO integration, and optional on-site implementation support – with details available directly from the startup.

Brev launched its platform in spring of last year and raised its first $1M then. It has now closed a new $3.3M round, still calling it "pre-seed" given how early the product roadmap feels.

02 /Why It Matters

About six months ago, Brev was describing its platform as an "AI Chief Operating Officer" – monitoring everything happening across the company through the same integrations the current platform uses. Several similar products have emerged under that framing.

Some startups prefer the label "AI Chief of Staff" – including Y Combinator graduate Bond ([covered here](/review/vot-chto-i-v-kakoj-moment)), which raised $3M for such a platform in December.

But both "AI COO" and "AI Chief of Staff" platforms share a fundamental limitation: they watch how well projects are being executed and how effectively employees are performing. They track the *doing*.

What Brev captures is something subtler – and arguably more important. Elon Musk articulated it well at the end of 2024: the output of a company is the sum of the vectors of what its employees are working toward.

If employees are all working hard and producing results, but those results point in different directions, the company may be moving very slowly – or not at all. Everyone is straining, and the cart goes nowhere.

What this means: tracking individual productivity and project completion is necessary but not sufficient. The more important question is whether all these efforts are aligned with each other – and whether they all point toward the company's stated goal. That's the question Brev is designed to answer.

Brev isn't alone in this insight. A 2024 review covered Elate ([covered here](/review/jeti-proschjoty-nelzja-kompensirovat-uspehami)), which raised $9.4M for a platform that translates strategy into a full task hierarchy for the organization and then monitors execution at every level – not to track busyness, but to verify alignment with strategy. Elate has since become an "AI-native" platform, but its core purpose remains unchanged.

Brev's blog articulates the problem sharply: "Execution is getting cheaper; coordination is getting more expensive." The implication: AI enables tasks to be completed faster and at lower cost than ever – which shifts the bottleneck from *doing* to *deciding what to focus on* and *ensuring the whole organization is focused on the same thing*.

Modern management platforms need to work at that coordination layer – translating goals into plans, plans into tasks, and then closing the feedback loop between task execution and strategic outcomes.

A recent parallel: Pomo ([covered here](/review/chto-nuzhno-delat)), covered just a couple of weeks ago – though in a completely different category – made the same strategic argument. Pomo claims its platform enables small companies to do marketing at the level of Fortune 500 firms. The insight: Fortune 500 companies outperform smaller ones not through sheer resource advantages but because they start with strategy before they act. Fortune 500 marketing teams have specialists who can "turn market noise into strategy." Small companies have never had that – until platforms like Pomo emerged, where an AI engine first helps the company develop a marketing strategy, then translates it into concrete action plans including ad creation, placement, and performance monitoring.

03 /Opportunities

Much has been said about AI platforms being conceptually different from SaaS. The argument: SaaS is a tool that people use to produce results. An AI platform is something that produces results on its own – potentially replacing human labor.

In reality, the vast majority of AI platforms still operate in "SaaS mode" – as instruments that amplify human execution. The human decides what email to write, what ad to design, what script to produce – and then directs the AI platform to do it.

This works. But if AI is to genuinely replace human roles, it needs to take over the most critical function that humans currently own: deciding *what to do* in the first place.

That idea can be pushed to absurdity – no one is suggesting AI should make every decision. Even in an AI-first world, humans retain at minimum the meta-decision: which decisions do I keep, and which do I delegate to AI?

But the practical direction is clear: the next generation of AI platforms won't just execute – they'll *decide* at some layer of the organization. And that layer of delegated decision-making needs to grow.

As Brev's founders correctly observe, the current bottleneck isn't doing – it's deciding. If all decisions remain with humans, the speed of AI-enabled execution becomes self-defeating: the decision queue becomes the constraint.

The practical questions that follow are concrete: which kinds of platforms could delegate some layer of decision-making to AI, what data sources would make that reliable, and how do you close the feedback loop between decision, execution, and outcome so the system keeps improving.

In effect, every existing platform – SaaS or first-generation AI – is a candidate for replacement by a decision-capable AI successor. That’s a wide-open opportunity space. Where do you want to enter it?