Mondrio Raises $3.75 Million to Link AI Pricing and Sales

By Sophie Lindqvist

Mondrio has raised $3.75 million in seed funding to build an AI pricing platform that links pricing research with quote creation, discounts and approvals. The Amsterdam- and San Francisco-based company says its agentic software turns customer conversations, product changes and deal outcomes into pricing recommendations, then pushes approved prices into individual sales deals.

The round was led by Heartcore Capital, with Pretiosum VC, Hersir Ventures, Cambrena Capital, Momo VC and angel investors with backgrounds in pricing, configure-price-quote software, AI and payments taking part. Mondrio plans to use the money to expand its AI and engineering teams, add pricing experts and strengthen its commercial presence in the United States and Europe.

The company targets a costly break between two systems: businesses set prices in one and negotiate them in another. If pricing does not learn from what happens in a deal, each new quote can repeat the same mistakes.

Why link pricing decisions to live sales?

Pricing research can look solid in a spreadsheet but fail in a negotiation. Salespeople meet objections the original study missed, customers assign different value to features than product teams expect, and discounts become routine for reasons scattered across calls, CRM records and approval requests.

Mondrio’s approach treats those signals as one continuous process. The platform analyzes quantitative data with qualitative inputs such as customer conversations and market feedback, identifies opportunities and recommends changes. Lessons from quotes, negotiations and renewals feed into the next pricing decision.

CEO and co-founder Gregor Biljardt said “the opportunity lies in agents combining quantitative information with insights from conversations and markets.” He argues the combination fits a commercial workflow where one small pricing decision can affect many later deals.

That matters as AI changes software economics. A feature that invokes models or automates work can add variable costs each time a customer uses it. Packaging, usage allowances and discounts affect both the offer’s appeal and the cost of delivering it. Faster release cycles give companies more reasons to revisit what they charge.

Mondrio treats pricing as an operating loop, not a document that stops at approval.

How does Pricing Studio turn research into an offer?

Mondrio’s Pricing Studio handles package design, pricing models and price levels. Its published workflow runs through research, design, simulation, decision-making and rollout. Teams can test an offer before putting it in front of customers.

Research can draw on customer surveys, willingness-to-pay information and AI-generated personas. The platform also lists feature benchmarking, pricing simulations, experiments and customer migrations. Together, these tools ask which features belong together, which customer groups value them and how a proposed change might affect existing customers.

The company draws a firm line around its synthetic customers: “AI personas provide a way to explore hypotheses, but their responses are simulated evidence rather than observed customer behavior.” The feedback loop becomes useful when teams compare those hypotheses with research and real commercial results after the offer reaches the market.

That boundary matters because simulated preferences can look like data without carrying the weight of a purchase, a renewal or a failed negotiation. Mondrio’s design makes the distinction explicit, even as it automates more of the research stage.

Pricing Studio treats AI personas as one test among several, not a substitute for customer evidence.

What happens after a price is approved?

Approved decisions can pass to Mondrio’s Deal Engine, which covers quote creation, discount guidance, approvals and information gathered as a deal progresses. The handoff defines the product: a new price list should shape the quotes customers receive instead of leaving sales teams to translate an analysis by hand.

In the workflow Mondrio describes, an agent builds a quote from the current price list, checks it against company policy and routes exceptions for approval. Quotes that meet the rules can move through automated approvals. Exceptions can include supporting context, such as the effect of a discount on margins.

The listed features extend past quote generation. They include a customer portal, quote engagement tracking, deal insights and electronic signatures. Deal Engine also gives pricing teams a trail of objections, unsuccessful offers and discount patterns that might point to a weak package rather than an isolated negotiation.

A company could use that evidence to change its pricing strategy while retaining controls at the individual deal level. The mechanism closes a loop that many pricing projects leave open: actual sales performance can shape the next price, discount or package decision.

Deal Engine makes approval rules part of the feedback path, so execution can improve the next pricing choice.

Which systems supply the context?

Mondrio’s integration catalog lists Salesforce and HubSpot for CRM data, Gong for customer conversations, Amplitude and Mixpanel for product usage, and Chargebee, Zuora and Stripe Billing for billing. It also lists DocuSign for Deal Engine’s signing workflow.

Each system records a different part of the commercial relationship. The CRM holds the opportunity, the conversation platform captures what the buyer said, product analytics show usage, and billing records show what the customer pays. Mondrio says combining those views can help teams judge whether an offer matches customer value and delivery economics.

The catalog assigns each integration to a particular module, so a listed connection does not guarantee identical functions across every part of the platform. That distinction matters for buyers comparing tools: a usable data source is not the same thing as a fully supported workflow.

Mondrio’s integrations gather the evidence around a deal, but module-specific support sets the practical limits.

Who founded Mondrio, and what does the seed money fund?

CEO and co-founder Gregor Biljardt founded Mondrio in 2026 with Joep van den Nieuwenhoff, the company’s chief commercial officer. Customers can use Mondrio’s AI capabilities with their existing pricing teams, or bring in Mondrio’s pricing experts for additional support.

The seed funding will support further development of agents, synthetic customer capabilities and the link between pricing strategy and deal execution. Mondrio also plans to hire pricing architects, develop relationships with B2B technology companies and private equity firms, and prepare for expansion into manufacturing and professional services.

Its central bet is that pricing becomes more valuable when it runs continuously rather than as an occasional project. The technical challenge is equally concrete: turn scattered commercial evidence into changes that teams can understand, approve and execute, then use the results to improve the next decision.

For buyers, the practical test is whether an approved price can flow into a live quote without losing policy checks or human review for exceptions. Mondrio plans to fund more agent development, synthetic customer tools and tighter links between pricing strategy and deal execution with its $3.75 million seed round.

The milestone to watch is a repeatable path from research to approved quote, and from that quote’s result back to the next pricing decision.