Aiden, a London-based startup building a machine learning-powered personal assistant to save mobile marketers time and money, closed a $750,000 seed round today from Kima Ventures and a number of angels, including Nicolas Pinto, Pierre Valade and Jonathan Wolf. The team first demoed the capabilities of its service on the stage of TechCrunch Disrupt as a Battlefield finalist.
In recent years, marketers have begun to suffer information paralysis from the flood of new monitoring platforms on the market. Rather than create an entirely new end-to-end solution, Aiden merely wants to help marketers get at the data they need faster.
The Aiden conversational assistant aims to make querying for key performance metrics as easy as checking the weather on Amazon Alexa. From a technical perspective, the team is focusing their efforts in two places — natural language processing and expert systems.
The natural language processing work is intuitive; 90 percent of the challenge in building a system like Aiden is deriving the intent of any given user query. The time spent on NLP helps Aiden respond appropriately, no matter how a question is phrased.
The emphasis on expert systems, on the other hand, is a bit less textbook. Expert systems have been around in some form or another since the 1970s. The idea is that humans can encode their knowledge into computers that can later be recalled — creating the illusion of intelligence. But while most consider them to be out of date, expert systems embody the present ethos of human and machine collaboration.
Aiden co-founders Marie Outtier and Pierre-Jean Camillieri
Marie Outtier, co-founder of Aiden, is a marketer by trade. Her co-founder, Pierre-Jean Camillieri, built Aiden around Outtier’s knowledge of the industry. Because Aiden possesses baseline expectations for key performance indicators, it can proactively alert marketers when something seems out of balance.
When the company comes out of beta, it plans to target businesses with relatively high mobile marketing ad-spend — this tends to include a lot of startups. If it can straddle enough of the long-tail of marketing anomalies, this should allow companies to identify mismatches in ad-spend more quickly to more efficiently allocate limited resources.
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