STRUCTIFY raises $ 4.1 million seeds to turn unstructured web data into data -ready -to -bus

Bruklin-based Startu strives for one of the most famous pain points in the world of artificial intelligence and data analysis: the diligent process of data preparation.

Structify Set out of Stealth mode today by announcing its public launch along with $ 4.1 million in funding of seeds led by Bain Capital Ventureswith participation of 8vc., Integral and strategic angel investors.

The company’s platform uses its own visual language model called Dora To automate the collection, cleaning and structuring of data, a process that usually consumes up to 80% of scientists’ time, according to industry studies.

“The volume of information available today is absolutely exploded,” says Ronak Gandhi, co -founder of Structify, in an exclusive interview with Venturebeat. “We hit a major moment of folding in the presence of data, which is both a blessing and a curse. As long as we have unprecedented access to information, it remains largely inaccessible because it is so difficult to turn into the right format for making meaningful business decisions.”

Structify’s approach reflects the growing focus throughout the industry to solve what data experts call “data preparation preparation”. Gartner Research shows that Inadequate data preparation One of the main obstacles to the successful implementation of AI remains, with four of the five businesses having no basic data needed for the entire benefit of the generative AI.

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At its core, Structform allows users to create custom data sets by indicating the data scheme, selecting sources and having AI agents to extract this data. The platform can handle everything – from SEC documents and LinkedIn profiles to news articles and specialized industrial documents.

What distinguishes the structure, according to Gandhi, is their internal model Dora, who navigates the net as a person.

“It’s super high quality. He navigates and interacts with things, just like a person, Gandhi explained.” So we’re talking about human quality – it’s the first and most important center of principles behind Dora. He reads the Internet the way a person would. “

This approach allows Structwork to maintain a free level, which Gandhi believes will help democratizing access to structured data.

“The way you think about data now, this is this truly precious object,” Gandhi said. “This really valuable thing you spend so much time ending and receiving and fighting around, and when you have it, you are like,” Oh, if someone deletes it, I’ll cry. “

Structify’s vision is to “comoditize data” – which does something that can easily be recreated if lost.

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The company has already seen acceptance in many sectors. Financial teams use it to retrieve dough information, construction companies turn complex geotechnical documents into read tables, and commercial teams collect organizational diagrams in real time for their accounts.

Slater StichBain Capital Ventures partner emphasized this flexibility in the funding message: “Every company I have worked with has a handful of data sources that are extremely important and huge pain to work with, whether these are numbers buried in PDFS scattered on hundreds of web pages hidden behind API

The variety of Structify’s early client base reflects the universal nature of the challenges of data preparation. According to Techtarget ResearchData preparation usually includes a series of labor-intensive steps: collecting, detecting, profiling, cleaning, structuring, transformation and validation-all before the actual analysis begins.

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A key differentiator for Structify is his “four -stroke” process, which combines AI with human supervision. This approach refers to critical concern in the development of AI: providing accuracy.

“Every time the user sees something that is suspicious, or we identify some data as potentially suspicious, we can send it to an expert in this particular case of use,” Gandhi explained. “This expert can act in the same way as (Dora), move to the right information, extract it, keep it, and then check that it is correct.”

Not only does this process correct the data, it also creates examples of training that improve the work of the model over time, especially in specialized domains such as construction or pharmaceutical studies.

“These things are so messy,” Gandhi noted. “I never thought in my life that I would have a strong understanding of geology. But there we are, in my opinion, it is a huge power – to be able to learn from these experts and bring it directly into Dora.”

As data retrieval tools become powerful, fears of confidentiality inevitably arise. Structify implemented protective measures to deal with these problems.

“We do not do any authentication, everything that required an entrance, everything that required you to fall behind any sense of information – our agent does not do this because it is a problem for confidentiality,” Gandhi said.

The company also prioritizes transparency by providing direct supply information. “If you are interested in learning more about certain information, you will go directly to this content and see it, as opposed to the form of inherited suppliers where this is this black box.”

Structify enters a competitive landscape that includes both established players and other startups aimed at various aspects of the challenge of data preparation. Companies like Alteryx., Informatica., Microsoftand Tableau They all offer data preparation opportunities, while several specialists have been acquired in recent years.

What distinguishes the structure, according to CEO Alex Reichenbach, is its combination of speed and accuracy. Recent LinkedIn Post by Reichenbach said they accelerated their agent “10x while reducing costs ~ 16x” by optimizing model and infrastructure improvements.

The launch of the company comes against the background of the increasing interest in automation of AI data. According to a Techtarget reportAutomation of data preparation “is often cited as one of the main investment areas for data and analysis teams”, and the possibilities for preparing data increases are becoming increasingly important.

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For Gandhi, she structures the problems she faced first -hand in previous roles.

“The big thing in Structify’s justified story is that it is a personal and professional thing at the same time,” Gandhi recalled. “I told (Alex) about the time that I worked as a data analyst and I do operations and consultations, preparing these really niches, on customer data – lists of all influencing fitness and their subsequent indicators, lists of companies and what jobs they publish, museums on the east coast … I spent a long time.

The inability to quickly repeat from the idea to the set of data was particularly disappointing. “What he received was that you couldn’t repeat and move from the idea to a set of data set quickly,” Gandhi said.

His co -founder Alex Reichenbach has faced similar challenges while working at an investment bank, where data quality problems make it difficult for the efforts to build models on structured data sets.

How it structures plans to use its $ 4.1 million seed funding to transform business data preparation

With the new funding, it structures to plan its technical team and to establish itself as a “data tool in industries”. The company is currently offering both free and paid levels, with options for enterprises for those who need advanced functions such as local implementation or highly specialized data retrieval.

As more and more companies are investing in and initiatives, the importance of high quality, structured data will only increase. Recently MIT Technological Review of the Reading Report It found that four of five businesses were not ready to take advantage of generative AI due to poor data foundations.

For Gandhi and the Structify team, solving this main challenge can unlock considerable value in different industries.

“The fact that you can even imagine a world that is the creation of data sets is iterative is somehow stunning for many of our users,” Gandhi said. “At the end of the day, the terrain is about having this control and customization.”