Connecty AI Raises $1.8M to Revolutionize Enterprise Data Management with Context-Aware AI

Connecty AI raises $1.8M to Transform Enterprise Intelligence

Connecty AI, an enterprise agent company, announced that it has raised $1.8 million as part of its pre-seed funding round. The round was led by venture capital firm Market One Capital, with participation from Notion Capital and data industry experts including Snowflake co-founder Marcin Żukowski and Piwik PRO founder Maciej Zawadziński. They aim to expand their context engine to support more data sources and offer it via API. The funds raised will help achieve this, and enhance additional data sources to improve contextual understanding. Enterprise data teams today manage complexity across three key areas: multi-source data pipelines (including ingestion, cloud warehousing, lineage, and cataloguing), diverse consumption patterns (such as CRM systems, BI dashboards, and machine learning apps), and distributed knowledge across roles like data engineers, analysts, and managers. When it comes to AI solutions automating data workflows, even 90% accuracy isn't enough -- LLMs need a constantly evolving, unified understanding across systems and teams. Connecty AI aims to transform enterprise intelligence by equipping data practitioners with agents that deeply understand business context and seamlessly integrate across the organisation. It offers a context-aware platform that helps teams unlock hidden insights and reclaim up to 80% of the time spent on manual data tasks. As per the press release, Jacek Łubiński, partner at Market One Capital, expressed excitement about backing Connecty AI, noting its potential to streamline data management and automate workflows with LLMs. "Our experience has shown us that effective data management is about more than just technology -- it's about connecting the dots between data sources, business objectives and the people who use them," said Aish Agarwal, CEO of Connecty AI, on how ad-hoc LLM experimentation may lead to pilot projects, but scaling to production will be much more challenging. Connecty AI extracts contextual data from multiple sources, building an enterprise-specific context graph with real-time feedback. After that, it automates data tasks, generating recommendations, updating documentation, and uncovering key metrics aligned with business goals. Reports estimate that the global AI Analytics market will expand at a compound annual growth rate (CAGR) of 22.6%, reaching $223 billion by 2034. As data complexity rises, organisations face higher costs, with data teams consuming an average of 12.5% of IT budgets -- equating to $5.4 million per year, of which 87% is allocated exclusively to data and platform maintenance.

Data management startup Connecty AI launches with $1.8M - SiliconANGLE

Startup Connecty AI Corp. launched today with $1.8 million in funding and a platform that promises to help companies more easily analyze their data. Market One Capital led the investment, which is described as a preseed round. The fund was joined by Notion Capital and multiple angel investors including Snowflake Inc. co-founder Marcin Zukowski. San Francisco-based Connecty AI is led by co-founders Aish Agarwal and Peter Wisniewski. Agarwal was previously the Chief Executive Officer of Image-Line, a major provider of music production software. Wisniewski, in turn, is an engineering executive who worked on application projects for companies such as hedge fund Point72. Before companies can scan their business data for useful patterns, they have to organize it into a form that lends itself to analysis. Connecty AI's namesake platform uses artificial intelligence to speed up the task. Customers can also use it to query their data once it's ready for processing. According to Connecty AI, its platform retrieves records from an enterprise's systems using prepackaged connectors that don't require custom code to set up. The result is that those connectors take less than five minutes to deploy. They work with data sources such as Snowflake and Google LLC's rival BigQuery service. After Connecty AI retrieves the data needed for an analytics project, it links together related records to ease processing. It then adds information on how the individual files changed over time. This context makes it to verify that no errors found their way into a record being used for analytics tasks. Filtering errors is not the only task involved in preparing a dataset for analysis. Companies must also prepare a schema, a file that defines what data points an information repository contains and how those data points are organized. Connecty AI says that its platform can help data scientists improve their schemes to streamline analysis. The platform also makes it simpler to merge datasets. This task is sometimes made difficult by formatting differences between the individual files: one dataset might organize each record in a separate spreadsheet field, while another may include three entries per field. Such compatibility issues must to be addressed before the files can be merged. Connecty AI makes the data it processes available to users through a chatbot. Workers can ask the chatbot to run queries on the information, as well as visualize the results in graphs. A business analyst at a retailer could, for example, request a pie chart that breaks down last quarter's revenue numbers by store. Workers can also have the chatbot loop in colleagues. Connecty AI could, for example, ask a member of the data science teams to double check that a record is accurate before it's incorporated into an analytics project. There are also features for sharing query results with team members. "The platform's ability to unify and contextualize data across fragmented systems presents a massive opportunity for businesses looking to use LLMs for data workflow automation," said Market One Capital partner Jacek Lubinski. Connecty AI says that its platform has already been deployed by a number of early adopters. It describes those organizations as companies with $5 million to $2 billion in annual recurring revenue.

How Connecty's AI context mapping could end enterprise data pipeline chaos

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Enterprise data stacks are notoriously diverse, chaotic and fragmented. With data flowing from multiple sources into complex, multi-cloud platforms and then distributed across varied AI, BI and chatbot applications, managing these ecosystems has become a formidable and time-consuming challenge. Today, Connecty AI, a startup based in San Francisco, emerged from stealth mode with $1.8 million to simplify this complexity with a context-aware approach. Connecty's core innovation is a context engine that spans enterprises' entire horizontal data pipelines -- actively analyzing and connecting diverse data sources. By linking the data points, the platform captures a nuanced understanding of what's going on in the business in real time. This "contextual awareness" powers automated data tasks and ultimately enables accurate, actionable business insights. While still in its early days, Connecty is already streamlining data tasks for several enterprises. The platform is reducing data teams' work by up to 80%, executing projects that once took weeks in a matter of minutes. Connecty bringing order to 'data chaos' Even before the age of language models, data chaos was a grim reality. With structured and unstructured information growing at an unprecedented pace, teams have continuously struggled to keep their fragmented data architectures in order. This has kept their essential business context scattered and data schemas outdated -- leading to poorly performing downstream applications. Imagine the case of AI chatbots suffering from hallucinations or BI dashboards providing inaccurate business insights. Connecty AI founders Aish Agarwal and Peter Wisniewski saw these challenges firsthand in their respective roles in the data value chain and noted that everything boils down to one major issue: grasping nuances of business data spread across pipelines. Essentially, teams had to do a lot of manual work for data preparation, mapping, exploratory data analysis and data model preparation. To fix this, the duo started working on the startup and the context engine that sits at its heart. "The core of our solution is the proprietary context engine that in real-time extracts, connects, updates, and enriches data from diverse sources (via no-code integrations), which includes human-in-the-loop feedback to fine-tune custom definitions. We do this with a combination of vector databases, graph databases and structured data, constructing a 'context graph' that captures and maintains a nuanced, interconnected view of all information," Agarwal told VentureBeat. Once the enterprise-specific context graph covering all data pipelines is ready, the platform uses it to auto-generate a dynamic personalized semantic layer for each user's persona. This layer runs in the background, proactively generating recommendations within data pipelines, updating documentation and enabling the delivery of contextually relevant insights, tailored instantly to the needs of various stakeholders. "Connecty AI applies deep context learning of disparate datasets and their connections with each object to generate comprehensive documentation and identify business metrics based on business intent. In the data preparation phase, Connecty AI will generate a dynamic semantic layer that helps automate data model generation while highlighting inconsistencies and resolving them with human feedback that further enriches the context learning. Additionally, self-service capabilities for data exploration will empower product managers to perform ad-hoc analyses independently, minimizing their reliance on technical teams and facilitating more agile, data-driven decision-making," Agarwal explained. The insights are delivered via 'data agents' which interact with users in natural language while considering their technical expertise, information access level and permissions. In essence, the founder explains, every user persona gets a customized experience that fits their role and skill set, making it easier to interact with data effectively, boosting productivity and reducing the need for extensive training. Significant results for early partners While a lot of companies, including startups like DataGPT and multi-billion dollar giants like Snowflake, have been promising faster access to accurate insights with large language model-powered interfaces, Connecty claims to stand out with its context graph-based approach that covers the entire stack, not just one or two platforms. According to the company, other organizations automate data workflows by interpreting static schema but the approach falls short in production environments, where the need is to have a continuously evolving, cohesive understanding of data across systems and teams. Currently, Connecty AI is in the pre-revenue stage, although it is working with several partner companies to further improve its product's performance on real-world data and workflows. These include Kittl, Fiege, Mindtickle and Dept. All four organizations are running Connecty POCs in their environments and have been able to optimize data projects, reducing their teams' work by up to 80% and accelerating the time to insights. "Our data complexity is growing fast, and it takes longer to data prep and analyze metrics. We would wait 2-3 weeks on average to prepare data and extract actionable insights from our product usage data and merge them with transactional and marketing data. Now with Connecty AI, it's a matter of minutes," said Nicolas Heymann, the CEO of Kittl. As the next step, Connecty plans to expand its context engine's understanding capabilities by supporting additional data sources. It will also launch the product to a wider set of companies as an API service, charging them on a per-seat or usage-based pricing model.