B2B teams lose 40% of deals in negotiation because sales wants to cut price while finance wants to protect margin – Outlit resolves that standoff automatically.
ENTRY ANGLES
AI negotiation chatbots embedded on product pages for price negotiation · Reverse auction platforms for determining fair purchase prices among suppliers · Dynamic pricing marketplaces where listed prices are opening bids rather than fixed
VERTICALS
CAPABILITIES
AI engine to optimize deal terms (conversion probability vs seller upside), Ability to package solution across multiple formats (decision-support tools, self-service platforms), Vertical-specific tuning and customization
OUTLIT FOUNDER
“talk to us for enterprise pricing.”
B2B sales teams lose more than 40% of deals during the proposal and negotiation phase. One underappreciated reason: sellers can't put together a clean offer. Two internal forces pull in opposite directions. Sales wants to drop the price to maximize the odds of closing; finance wants to hold the line to protect margin.
Outlit built a platform that helps sales teams identify the optimal deal terms for each opportunity – attractive enough for the buyer, profitable enough for the seller.
The engine under the hood ingests and analyzes data from every previous negotiation: the terms offered, whether a deal closed, and whether existing contracts were renewed. From this, it draws conclusions that it turns into actionable guidance.
First, it recommends a pricing model for the specific deal – subscription by seat count, usage-based billing, a revenue-share on outcomes generated for the client, or any combination thereof.
Second, it recommends optimal contract terms, which can be far more complex than just price and billing method. Minimum payment commitments, volume discounts, renewal escalators, and dozens of other variables all come into play.
Of course, any recommendation from the AI still needs sign-off from the humans responsible for the deal – typically across multiple departments. Outlit supports this with configurable approval workflows, and the terms can be revised at any stage in the chain.
Beyond deal recommendations, the platform can also answer natural-language questions about historical deals, generate sales reports for any time period and dimension, summarize non-standard contract terms across the book of business, and more.
Outlit just graduated from Y Combinator, having raised an initial $500K from the accelerator.
Outlit's tagline is "Experience the future of deal-making" – which, as usual, sounds slightly oversold.
But zooming out, something real is happening. Pricing pages increasingly follow the same structure: one or two standard tiers for individuals and small teams, followed by a block that says "talk to us for enterprise pricing." That's not laziness – it's a deliberate signal that every large deal will be negotiated individually, and the standard tiers are just the invitation to start the conversation.
Doing that negotiation well – at scale, consistently, profitably – is exactly what platforms like Outlit are built for. The ability to determine optimal terms for each individual buyer is, in this sense, genuinely becoming the future of B2B sales.
Subskribe ([related review](/review/chtoby-konkurirovat-nuzhno-umet-torgovatsja)) has raised $18.4 million on a similar thesis, purpose-built for cloud service vendors who need both smart deal structuring and the ability to accurately bill against complex custom contracts – the latter being an additional automation challenge Subskribe also addresses.
Y Combinator also produced Dimely ([related review](/review/u-nih-uzhe-est-dengi-no-vot-jetogo-eshhjo-ne-hvataet)), which focuses specifically on billing automation: its AI reads contract text, extracts pricing terms, and invoices clients accordingly on autopilot.
Revolear ([related review](/review/jeti-dva-izmenenija-prinesut-dengi)) raised $6 million in its first round on a platform similar to Subskribe – and then noticed something else: not many prospective buyers actually want to get on a call with a sales team. So Revolear flipped the platform from internal to external. Prospects now navigate terms in self-service mode, with the AI asking them the right questions and generating tailored pricing proposals. When the deal looks large enough to be at risk, the AI hands off to a human sales rep.
Fixed prices may be quietly going the way of the fax machine, across more categories than most people realize. A few examples.
Indigo ([related review](/review/torg-umesten)) and Final Offer are marketplaces for homes and apartments where listed prices are understood to be opening bids rather than final numbers. Indigo raised $8 million in its first round; Final Offer has raised $17.4 million, with some of that coming after its review here.
Crown ([related review](/review/uspej-sdelat-to-chto-i-tak-proizojdjot)) built a corporate procurement platform where the "fair" purchase price is determined via reverse auction among suppliers.
Nibble ([related review](/review/nazovite-svoju-cenu)) started with $3.6 million raised for an AI chatbot that lets online shoppers negotiate prices directly on product pages. That chatbot has since evolved into a full AI negotiation platform for e-commerce, B2B sales, and enterprise procurement.
The direction: platforms that support the trend away from fixed pricing – making dynamic, individualized terms the default rather than the exception.
At the core of any such platform sits an AI engine that can identify the optimal deal terms for each specific buyer: maximizing conversion probability while maximizing seller upside. That engine can then be packaged in many forms – from internal decision-support tools to buyer-facing self-service platforms – and tuned for specific verticals, which significantly sharpens its accuracy.
For what vertical, and in what form, would you build this?