Bullsai

Find the decisions that matter most, and make them measurably better.

Custom models built from your own data, proven on your own history before anything goes live.

Acquiring target

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Two ways in. One target at the center.

Advisory finds and ranks the opportunities. Building narrows from bootcamp to implementation to a running model your team uses. Select a ring to see what each stage delivers.Select a stage to see what it delivers.

Advisory Bootcamp Implementation Optimize
2 to 3 weeks

Advisory

For leaders who want the opportunities found and ranked before committing to a build.

    Case studies

    Demonstrations use synthetic data. We never show client names or client records.

    Prioritization

    More value from the same team

    A client with more work arriving each day than its team could reach wanted the most valuable items worked first. We built a model from its own outcome history and tested it the way it would run live: trained only on earlier months, then scored each later month cold.

    Value in the top 20% of the queue
    About 58%, against about 44% for the ranking already in use
    Retraining
    Once a year, on the latest outcomes

    Share of value captured, by share of queue worked

    Seven-month walk-forward test on the client's history. Values at 20% are measured averages; the curve between points is illustrative. Hover or drag to read values.Drag across the chart to read values.
    Performance analysis

    Project margin analytics

    A synthetic contractor ledger built with known patterns, so the right answers were set before the analysis ran. The method found all three: a manager whose lead survives every control, a manager who looks weak until branch is controlled, and a branch whose margin recovery comes entirely from job mix.

    Planted patterns found
    3 of 3
    Client data used
    None

    Margin versus peers, in points

    Synthetic data. Values are illustrative.
    Discovery

    Voice discovery interviews

    Customized AI-guided information discovery interviews across organizations.

    Topics raised, by role interviewed

    Illustrative preview. Not client data.
    Operations

    Electric utility dashboards

    Operating and rate views built for a utility. Filter outage history by year, time of day and cause to see which feeders need attention first.

    Outage minutes by feeder, 2025

    Year
    Illustrative preview, synthetic data. Feeders are lettered; values are made up.
    Pricing

    Retail operations

    Store-level pricing and margin analytics, down to the individual SKU. Selling fewer units at the right price can earn far more.

    Price
    Up 10%
    Units sold
    Down 20%
    Net profit
    About twice as much

    Units sold and net profit, by price change

    Illustrative example for one SKU, not client data. 10,000 units at $100 with an $85 unit cost and $100,000 fixed cost, against 8,000 units at $110: net profit rises from $50,000 to $100,000.
    1 of 5

    Two operators who have built and sold companies.

    We are building the intelligence we wanted when we ran our own. Both principals are named on every engagement.

    Portrait of Sean Knowles

    Sean Knowles

    Chief Executive Officer

    CEO of McLean Engineering, then VP of Corporate Development at Osmose after it acquired McLean. Leads client relationships and engagement design.

    • MBA, Duke University
    • U.S. Senate staff
    • Presidential Scholar, Texas Tech
    Portrait of Russell Smith

    Russell Smith

    Chief Product Officer

    Founded RTS Associates, which was acquired by private equity in 2024. Designs Bullsai's models and verification methods.

    • Served on the boards of the Georgia Technology Authority, the Georgia Tech Alumni Association and the Northeast Georgia Health System Foundation
    • Served as a trustee of Leadership Georgia
    • President's Scholar, Georgia Tech

    Your data stays yours: nothing, no one, never.

    Your data is becoming your most valuable asset. AI can learn from almost anything public; it can't learn your customers, decisions and results unless someone hands them over. So nothing reaches an outside AI model without your written approval, no one outside the named principals sees it, and it is never used for another client. The commitments below come from our Information Security Policy, version 1.0, effective October 1, 2026.

    Encrypted twice at rest

    Each client gets its own vault, on full-disk-encrypted hardware Bullsai owns.

    No third-party model without your sign-off

    Record-level data enters an outside AI model only if you authorize a named provider in writing. No training on your data.

    Checked before anything leaves

    An automated identifier check and a principal's review clear every result. No statistic describes fewer than 10 records.

    Principals only

    Contractors build against synthetic data and never see client data, systems or keys.

    24-hour notice

    You hear from us within 24 hours of a suspected incident involving your data.

    Provably destroyed

    Cryptographic erase within 30 days of close, with a signed certificate (NIST SP 800-88 Rev. 2).

    Our full Information Security Policy is published. Controls are mapped to NIST CSF 2.0 and the NIST AI RMF. We carry cyber liability and technology E&O insurance. We complete your security questionnaire and demonstrate any control live.

    Everything you need to get started.

    Choose the documents your vendor review needs. A principal reviews each request, and the documents come from security@bullsailabs.com.

    Select documents

    Your details

    Name the decision worth getting right.

    info@bullsailabs.com