Building the Future of Search

We're helping an NYC startup turn unstructured text in enterprises into value and insights.


An NYC-based AI startup was struggling to accelerate their product development after having raised over $3 million in seed funding. The client needed to carry out multiple R&D activities at once and get the results fast. The scope of work included information extraction from large volumes of unstructured enterprise data, such as legal documents, SEC filings, and emails.


We got on board in early 2018 as a "parallel" R&D team and since then we have delivered multiple projects including: implementation of NLP research papers, optimization of data pipelines for large volumes of data, parallelization to handle millions of documents, and support with acquiring training data.

Looking for something similar?



  • XGBoost, LambdaMART
  • spaCy, NLTK
  • large-scale scraping
  • mining data from various formats (e.g. MS Word, email threads, PDF attachments).

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