Primer: Automated Measures of Effectiveness for Information Operations Effects

Primer builds machines that can read and write with human-level precision with AI Natural Language Processing (NLP) technology deployed by some of the world’s largest government agencies, financial institutions, and Fortune 50 companies like WalMart and Procter & Gamble. Customers have realized significant value from techniques to summarize unstructured data across many different challenges and job functions. Primer’s NLP engine model achieved an industry-leading 95.6% F1 score on the benchmark CoNLL dataset taking on average 50ms to process a 2,000 word document saving knowledge workers enormous sums of time and money.   As seen in Ukraine, Information Operations (IO) are key and critical to any level of warfare, but conducting Measures of Effectiveness (MOE) aka, Battle Damage Assessment (BDA), for IO is difficult. Altering or changing target’s behavior is not easily seen in images or heard over signals, the traditional method for BDA.  Primer proposes to overturn the way Air Force Information Operators (14Fs) conduct MOE for IO by proposing a revolutionary advanced AI to automate the understanding of “effects” in the IO sphere.  Additionally, understanding when an enemy force is employing IO against you is exceedingly human intensive and prone to error.  Primer will bring their commercially best-in-world NLP to commercial/DoD data sets to automate the indicators of IO MOE and provide command SA on enemy use or IO against friendly forces, reducing human time, cost, and increasing efficiency of follow-on IO “re-attacks” and friendly OPSEC.