WANIWANI raises $8 million: After comparison platforms, AI agents open a new battle for intermediation

Waniwani has raised $8 million from Seedcamp, Redstone, Plug and Play and several business angels to develop what it describes as revenue and compliance infrastructure for the agentic distribution of financial services. Behind this relatively modest funding round lies a far broader ambition. The company is not seeking to build another conversational agent. It is betting on the emergence of a new distribution channel in which purchasing decisions are no longer made directly by consumers, but are increasingly influenced, prepared or executed by artificial intelligence systems.

Since the emergence of the commercial web, every major technological shift has created a new generation of intermediaries. Search engines captured access to information. Marketplaces captured access to products. Platforms captured access to audiences. AI agents may now seek to capture access to decision-making.

For years, digital technology was presented as a tool of disintermediation. The reality was often different. The internet did not eliminate intermediaries, but displaced them. Travel agencies gave way to Booking.com, classified advertisements to Leboncoin, and traditional brokers to specialised comparison platforms. In each case, value gradually became concentrated in the hands of those capable of organising the encounter between supply and demand.

Financial services illustrate this evolution perfectly. Over the years, insurers, banks and lending companies have seen a multitude of digital intermediaries emerge to guide consumer choices. Comparison platforms, online brokers, aggregators and affiliate networks have become essential distribution players. Their role is based on a simple promise: simplifying a complex decision.

This is precisely the territory on which AI agents are beginning to emerge. When users ask ChatGPT or Claude to explain the differences between several insurance policies, identify the best accounting software or compare different credit offers, they are already delegating part of their decision-making process. The capabilities remain imperfect, and transactions are still largely completed elsewhere. But the economic logic is already visible. The more agents become capable of understanding users’ needs, comparing available offers and supporting transactions, the more central their position in the value chain will become.

For twenty years, companies have learned to optimise their visibility for search engines. An increasing share of their marketing budgets has been devoted to search optimisation, advertising and traffic acquisition. In an agentic environment, however, the objective is no longer simply to be visible, but to be recommended.

The consequences could be particularly significant for traditional intermediaries. The historical value proposition of a comparison platform is to aggregate information, compare offers and guide a decision. These are precisely the tasks that artificial intelligence models are becoming capable of performing at scale. If a conversational agent can analyse hundreds of insurance offers, filter the most relevant and provide a personalised recommendation directly to the user, the position currently occupied by some comparison platforms could be weakened.

This evolution explains the emergence of a new category of companies, of which Waniwani is one of the first representatives. Their objective is to become the infrastructure that enables businesses to operate within these new distribution environments.

In practical terms, this means making products accessible to agents, managing regulatory requirements, ensuring that recommendations can be traced, attributing revenue and measuring commercial performance. In some respects, this function resembles the role played by Salesforce in customer relationship management or Stripe in payments, but applied this time to distribution driven by artificial intelligence systems.

The decision to target financial services as the first market is no coincidence. Insurance, lending and real estate share several particularly attractive characteristics: complex products, high commissions, extensive regulation and relatively long decision-making cycles. Each recommendation can generate significant economic value. Each mistake can also carry major regulatory consequences. These sectors therefore provide an ideal testing ground for the first agentic distribution models.

Yet the opportunity extends far beyond finance. The same mechanisms could gradually spread to B2B software, recruitment, legal services, home services and healthcare. Wherever a decision currently requires qualification, comparison or recommendation, AI agents could potentially insert themselves into the value chain.

The question, then, becomes less technological than economic. Who will control these new gateways? AI labs such as OpenAI or Anthropic? A new generation of specialised infrastructure providers? Established comparison platforms that successfully manage the transition? Or the providers themselves, seeking to preserve a direct relationship with their customers?