Say hello to Drooms Intelligence
your whole data room answerable, extractable, reportable. More information
Say hello to Drooms Intelligenceyour whole data room answerable, extractable, reportable. More information
Tabular Review automatically extracts key data from leases, Energy Performance Certificates and more, turning unstructured PDFs into structured, exportable reports. Powerful and secure AI, inside Drooms.
Real estate and due diligence teams spend up to weeks manually extracting rent rolls, lease terms, energy ratings and covenant data from hundreds of similar documents. It’s slow, error-prone, and impossible to scale under pressure.
Tabular Review does the reading, so your team does the deciding.
Reduce hours of manual document review by transforming unstructured PDFs into structured, searchable, and exportable data. Turn scattered files into clean, reliable data your team can act on right away.
Less digging. Faster answers.
AI automatically extracts key information from large collections of similar documents, dramatically reducing manual data entry while improving consistency across every review.
Review extracted information in one structured table where documents can be sorted, filtered and compared side by side, making it easy to identify trends and differences across portfolios or transactions.
Every extracted value links directly back to its original location within the source document, allowing users to validate AI-generated results quickly and build trust in the extracted data.
Download structured data as XLSX, with future API integrations allowing extracted information to flow seamlessly into ERP systems, asset management platforms and reporting tools.
Select similar document types, such as Energy Performance Certificates and leases.
AI reads every document and structures it into easily readable tables.
Validate results in a spreadsheet-style interface with highlighted sources.
Work together on reports and download in XLSX.
Transaction teams can review hundreds of lease agreements faster by automatically extracting commercial terms into a structured comparison table.
Outcome: Faster reviews and earlier risk identification, saving time and improving deal outcomes.
Asset and portfolio teams replace manual monthly reporting with instant, AI-generated overviews across every property.
Outcome: Management gets the full picture in seconds, managers get their time back, and developers get deals done faster.
ESG teams consolidate energy and carbon data across their portfolio and accelerate regulatory disclosures without manual document work.
Outcome: Cleaner data, faster disclosures, with less manual effort and fewer errors at every step.
See how Tabular Review helps teams process documents faster while reducing manual work and improving accuracy.
Everything you need to know, in one place.
Tabular Review is a feature of Drooms Intelligence that uses artificial intelligence to extract structured information from multiple documents and organise it into a clear, searchable table. It helps deal teams compare key data, identify inconsistencies and review large volumes of due diligence documents more efficiently.
Tabular Review analyses selected documents in the data room, identifies relevant information and extracts structured data into a comparative table based on selected criteria. Instead of reviewing documents individually, users can quickly compare values across multiple files, validate information and identify missing data from a single view.
Tabular Review helps accelerate due diligence by reducing manual document review, improving consistency and making large document collections easier to analyse. It enables teams to compare key information across hundreds or thousands of files, helping them identify risks, validate data and make informed decisions more efficiently.
Tabular Review and AI Assistant are complementary features within Drooms Intelligence.
Together, they help deal teams review, analyse and understand complex due diligence documentation faster and more accurately.
Tabular Review extracts structured data from documents using ready-made templates. Current use cases include: