The Egyptian Plover: Rhymes with "over". An African waterbird that maintains a (dubious) symbiotic relationship with crocodies, feeding on decaying meat lodged between their teeth.

Find, explain, and fix bad data & the processes behind them.

From detecting data errors and anomalies to diagnosing process inefficiencies, Plover continuously monitors your business operations via your data systems to automatically identify issues, and improve and maintain the health of your business.

Data aren't messy, processes are.

So-called "messy data" are evidence of messy processes. Plover automatically identifies and explains erroneous and anomalous data to help you find opportunities for organizational effeciency gains.

Automatically identify erroneous values

Protect your dataset from future erroneous values

Help diagnose the cause of errors

From deployment to action in a few lines of code

1. Plover builds a model of your data

        from plover.source import ImmutableSqlSource
        from plover.store import AwsS3Store
        from plover.backend import AwsBackend
        from plover.engine import DatabaseEngine

        conn = connect_to_sql_database()

        db_engine = (
        DatabaseEngine(
        source=ImmutableSqlSource(conn, "SATELLITES_WH"),
        store=AwsS3Store("myorg-plover", "SATELLITES_WH"),
        backend=AwsBackend("myorg-plover"),
        )
        .fit()
        .persist()
        .metalearn()
        .persist()
        )
        ```

Holistic modeling and metalearning

Plover builds a holistic model of the world as defined by your data, then learns a metamodel to further refine its understanding of the information in your data.

2. Identify Likely Errors

        top_5_errors = (
        db_engine
        .metrics
        .errorness
        .sort(by=['Confusion'], descending=True)
        .head(5)
        )
        ```

Row	Column	Confusion	Observed	Predicted
Intelsat 903	Eccentricity	10.514456	0.793069999999…	0.000335826120…
Intelsat 902	Inclination_ra…	10.036763	25.06467339	0.002274957131…
Intelsat 903	Apogee_km	7.853968	358802	35792.92656399…
DSP 20 (USA 14…	Period_minutes…	6.695287	142.08	1436.095724064…
SDS III-6 (Sat…	Source_Used_fo…	4.93515	JM/5_11	ZARYA

Inconsistency is key

Plover finds errors by identifying data that are inconsistent with its model of your data, and shows you the observed value and its predicted value, which you may use to overwrite erroneous values.

3. Find similar errors

          errs_like = db_engine.errors_like(
          "DSP 20 (USA 149) (Defense Support Program)",
          "Period_minutes"
          )
          ```

row	rowsim	Observed	Predicted
SDS III-7 (Sat…	0.988281	23.94	1436.088006
SDS III-6 (Sat…	0.964844	14.36	1436.113453
Advanced Orion…	0.9453125	23.94	1436.105354

Metareasoning

After identifying an error, Plover can identify similar errors by finding data that have similar information profiles.

4. Identify errors in incoming data

        err = db_engine.detect(new_satellite_record)
        err[0]
        ```

Row	Column	Incon Quantile	Observed	Predicted
Satmex 8	Users	0.99998	Commecial	Commercial

Protect Prod

Quantify how inconsistent data are before they make it into your production data systems

Machine learning + human learning + engineering

Baxter Eaves

CEO

Baxter is a US Navy veteran and holds a PhD in Experimental Psychology from the University of Louisville where he developed computational models of human trust and social learning. He has led a number of DARPA projects and brings 13 years of experience deploying human-inspired AI tech in high-risk industries.

Patrick Shafto

Scientist at large

Patrick is a program manager at DARPA under the Information Innovation office (I20) and professor of Data Sciences at Rutgers University - Newark. He has led a number of projects for agencies including DARPA, DOD, and NSF, and his publications have appeared in top journals of machine and human learning.

Michael Schmidt

Principal ML Engineer

Michael has 14 years of research and engineering experience. He has built production models for healthcare, agronomy, finance, and law; and has conducted research in the areas of high-energy physics, differential geometry, plasma physics, and high-performance computing.

Frequently asked questions

Is Plover another wrapper around Chat GPT, Claude, llama, etc?

No. Plover is proprietary probabilitstic machine learning model designed for semi structured relational data. Plover is designed to be explainable and to properly quantify uncertainty.

What data is Plover trained on?

Your data and only your data. Plover is hyperfocused on your unique business.

Is Plover secure?

Plover is installed on your infrastructure whether that be in the cloud or in an air gapped electromagnetically secure environment. Plover is exactly as secure as your systems — no more, no less.

Does Plover transmit my data externally?

No.

Is Plover expensive to train? Does it require special hardware like GPUs?

No. You can train and query a simple Plover model in a couple of minutes on an old iPhone. Don't believe us? Hop over to the demo on your smartphone, download one of the provided datasets, put your smartphone in airplane mode, and test it out.

Get in touch

Provide your information here and we will reach out to you promptly.