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Personalized sales documents that match each prospect's exact situation used to take hours – Uman automates the research, templating, and argument selection.
ENTRY ANGLES
AI-powered signal extraction combined with proactive lead discovery · Pre-built pitch generation triggered by buying signals · AI matching buyer needs to seller assets with precision
VERTICALS
CAPABILITIES
Lead discovery and ICP matching, Buying signal detection, Sales collateral generation
UMAN FOUNDER
“What worked with companies like this one?”
Uman built an AI assistant for complex B2B sales – specifically for creating highly personalized sales documents that match a prospect's specific situation to the seller's most relevant offerings.
Preparing one of these documents manually is a serious time investment: a sales rep needs to research the prospect online, pull up the history of prior interactions, form a hypothesis about what to pitch, find an appropriate template, select supporting arguments from previous decks, consult subject matter experts for relevant product facts, and then assemble everything into something coherent and compelling. That process typically takes several hours per document.
Uman claims this can be reduced to a single click. Here's how the process works.
First, a company connects its knowledge sources: corporate documents, presentations, product descriptions, CRM data, customer communications, call recordings. Uman ingests all of it into a unified knowledge base.
What's distinctive about how it structures that knowledge base is that it isolates and organizes the offers, arguments, and case studies that have been used in past sales engagements. The result is a searchable library of sales assets organized around actual buyer situations and outcomes – not just product specs.
Sales reps can then query this library as a structured resource: "What worked with companies like this one?" "What objections did we face in similar deals?"
But the real play is that Uman's AI can do this matching automatically. Given a new prospect, it pulls external signals – LinkedIn profile, recent news, annual reports – and combines them with internal context from the CRM and communication history to select the most relevant assets and pre-populate a presentation. Reps can trigger standard workflows from a menu: "Prepare me for a meeting with [client]," "Answer this prospect's question," or "Draft a proposal" – and receive a tailored output they can review and send.
Uman has already signed clients including PwC. The company is based in Belgium and has raised €1.9M in a new round, bringing total funding to €2.5M.
Effective B2B selling isn't about listing features. It's about diagnosing a buyer's specific problem, connecting it to the right capability, and backing that connection with credible evidence. That's the craft – and it requires experience, preparation, and time.
The broader trend is that AI is increasingly capable of doing exactly this. Several startups are approaching the problem from different angles.
AutogenAI ([related review](/review/prostoj-sposob-ubedit)) was one of the first in this space, originally built for writing responses to enterprise and government tenders, but has since expanded into commercial proposals and presentations broadly. It has raised $65.3M.
Fluint ([related review](/review/prezentacija-prodazham-ne-pomoshhnik)) argues that slide decks are the wrong format for B2B sales entirely. Its platform helps reps produce short, single-page documents tailored to each stakeholder in a buying group – with each document framing the value proposition from that specific person's perspective. It raised $1.9M.
Cuvama ([related review](/review/chtoby-bolshe-prodavat-nuzhno-perestat-delat-jeto)) focuses on value-based selling: mapping a prospect's stated pain points to documented case studies, then building each won deal into the knowledge base for future use. It raised $4.2M.
Symbe ([related review](/review/samyj-prostoj-sposob-prodat)) similarly centers on case studies, using AI to match relevant examples from a library and format them into prospect-specific presentations. It raised £1.2M.
Uman's differentiation is that it merges two capabilities that have typically lived in separate products: the intelligence layer that scans for buying signals – funding announcements, leadership changes, expansion news – and the content generation layer that converts those signals into a tailored pitch. Most competitors do one or the other.
Uman doesn't search for leads – but it applies the same signal-extraction logic to the leads a rep already has, and immediately converts those signals into a ready-to-send document. Whether it will eventually add proactive lead discovery is an interesting question.
Sam Altman wrote a few years ago: "I think AI will have superhuman persuasion before it has general intelligence, which may lead to some very strange outcomes."
One of those outcomes is already unfolding in B2B sales: AI that can match a buyer's needs to a seller's assets faster and more precisely than any human rep.
That makes this a highly timely category. The B2B sales market is enormous, perpetually undersupplied with skilled reps, and expensive to scale. AI that does more than fire off generic cold emails – and instead produces genuinely relevant, well-argued sales materials – addresses a real structural problem.
The examples in this review show several ways to approach the space. The clearest gap is a platform that combines Uman's signal-extraction logic with proactive lead discovery – surfacing companies that fit a seller's ICP before the seller knows they exist, then delivering a pre-built pitch the moment a buying signal fires.