We've been using machine learning for years, and many of our processes involve a blend of automation and manual curation - the skill is knowing which parts of a task suit each. Here's how that plays out across the business:
Record linking. We use machine learning to match records to the right company. For example, finding the best website for a company, judging whether two filings referring to people with similar names are, in fact, the same person, matching a news article to the right company record, or linking companies to feeds like grants and patents at the top of our data pipeline. These matches are then checked and refined by our Data team rather than taken at face value.
Automated data extraction and enrichment. We use machine learning to extract or generate structured information. For example, categorising companies with an industry or buzzword, finding the right trading address from a company website, categorising news articles into general topics, or writing a description of a business.
Decision support for our Data team. We use machine learning and AI to help our domain experts with curation and quality assurance. For example, we use AI to discard irrelevant news articles from our announced transaction curation process - freeing our analysts to focus on the transactions that matter.
In-product utilities. We use machine learning to help people discover and understand companies more easily on the platform. For example, our similar companies feature is powered by an AI algorithm, and we're experimenting with turning plain-English queries into advanced search queries. We also offer Lenses: AI-generated, evidence-based insights on a company profile, grounded in Beauhurst data with clickable source references so you can verify every answer. Preset lenses (Highlights, Growth Trajectory, Financial Health, Innovation & Impact, People) are available out of the box, or you can create a Custom Lens by typing any company-research question in plain English and saving it for repeated use, including sharing it across your team so everyone screens against the same framework.
Beauhurst are also building a Claude MCP connector, letting your team query Beauhurst data directly from within Claude rather than logging into the platform separately. It's currently in development so watch this space.
As part of our product development, we use traditional machine learning and natural language techniques, simple statistical methods, and generative AI, depending on the problem at hand. We're well-placed to take advantage of these technologies because of our years of experience understanding the intricacies of company data and producing hand-curated datasets - automation speeds up the parts of the process that suit it, while our Data team continues to handle the many parts that need human judgement.
We are enthusiastic but careful users of the technology: we do not train or use external AI systems with sensitive data, including behavioural or usage data belonging to our customers.