Take risks with confidence.

DataFill is the only tool that can find your data gaps - the blind spots between untapped opportunities and unforeseen risks.

Explore our Datafill tool

Find

Identify key opportunities for innovation or risk by generating data points that fill your data gaps. New data in gold, original in grey.

Filter

Prioritize opportunities that are more relatable or more innovative with the slider.

Explain

Characterize opportunities by comparing clicked selections of new data and original data.

This is not Generative AI.

Gen AI can only give you more of what you already have, by emulating trends in historical data.

Datafill generates novel data that fills the gaps in those trends.

Solutions

  • Customer review sentiment analysis can help businesses retain clients, capitalize on market trends and mitigate risk. We analyzed a dataset 1000 customers restaurant reviews.

    ✅ Datafill’s insights:

    1. Model performance dropped by 2% on reviews made by more educated, higher-end clientele, and

    2. High-end clientele were underrepresented in the reviews, and could represent an untapped market segment.

    ❌ ChatGPT’s hallucinations:

    The largest gap was that dietary restrictions were not mentioned, but this insight is not actionable because we cannot predict whether such reviews would be positive or negative.

  • With a market cap of USD$66 billion, there is both a huge opportunities and huge costs in trying to develop new drugs. Part of the R&D phase of development is screening a chemical library for hits - compounds with measured/predicted target interaction, for further modification. We analyzed a SARS-CoV-2 screening library of 1,400 small molecules with 90 initial hits.

    ✅ DataFill’s insights:

    258 novel chemically-valid hit compounds (a 295% increase).

    The new compounds exhibited properties more similar to popular mRNA vaccines compared to the original compounds, including an increase in:

    1. TPSA,

    2. H-bond donors and acceptors,

    3. aliphatic rings, and

    4. carboxylic groups.

    There was also a decrease in aromatic rings.

    ❌ ChatGPT’s hallucinations:

    ChatGPT was unable to process the data (SMILES) to extract insights.

  • The US market for luxury SUVs in the early 2000’s boomed with a growth of USD $5.85 billion in sales. We analyzed a dataset of 400 cars, their prices and features, from the late 90’s to see if Datafill or ChatGPT could predict this market gap.

    ✅ Datafill’s insights:

    Correctly predicting the market gap for luxury SUVs.

    ❌ ChatGPT’s hallucinations:

    Incorrectly predicting that compact all-wheel drive sports cars were the biggest opportunity, when rear-wheel drive was optimal design for sports cars. The logical fallacy was that because there were few cars of this type that they must represent a significant market gap.

  • Let us know in the contact form below how we can help you take risks with confidence!

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