
For developers, ARGOS Identity offers two APIs.
ARGOS ID Check (KYC/eKYC): launch the hosted Liveform from one URL in a browser or an iOS, Android or React Native WebView, or build your own capture UI on the Verify engine APIs. Your backend uses the REST API to fetch submissions, review pending cases, delete or partially delete data for GDPR and CCPA requests and download AML reports, and receives webhooks for created, submit, approved, rejected, updated, delete and AML result events. Each check reads the ID with OCR plus MRZ and PDF417 barcode checks, tests ID and selfie liveness, matches the face to the ID photo and screens the user against sanctions and PEP lists, with ongoing monitoring afterwards. Data is processed and stored in the region chosen for each project (US or South Korea), sensitive fields are encrypted with AES-256 at rest, and payload encryption is optional. A migration API imports existing verification records.
ARGOS Omni (Review Ops Agent): authenticate with an x-api-key header, create a profile under a workflow, upload files, text or JSON as items (PDF, DOCX, XLSX, PPTX, images, CSV, HTML, EML and more) and start an asynchronous analysis. The result follows the output schema you defined, with a verdict of verified, rejected or pending_review and the reasoning and source step for each extracted value, and every completed analysis can be downloaded as a PDF report.
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Mentornity is mentoring and coaching program software for organizations that run structured programs and need to show what came of them.
It covers the full cycle. Participants enrol and are matched by an algorithm that scores every possible pair against weighted criteria the program defines, discarding pairs that fail a mandatory rule; administrators approve suggestions individually or in bulk, assign by hand, or open a mentor pool and let participants choose. Mentors publish availability, mentees book within it, sessions sync to Zoom, Microsoft Teams or Google Meet, and reminders go out automatically before and after each meeting.
Measurement runs alongside rather than after. Surveys and forms can be scheduled at any point in a cycle, and a program health score tracks participation, meeting frequency and completion against each program's own targets, flagging programs that drift before the cycle ends. Certificates are issued automatically on completion.
Each program runs under its own branding on its own domain, with role-based access and six interface languages. Free for up to 10 users with no expiry and every feature enabled; paid plans start at $289 per month and scale by participant count.
Running mentoring programs since 2015.
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Clef-flash
Clef-flash is a rapid 9B multimodal decision-making model that converts a given state and a framework of typed inquiries into organized decisions. It has the capability to interpret state data in various formats such as text, JSON, images, or video, providing a probability for each possible option related to every question posed. The model accommodates a context window of 24,576 tokens and supports visual input, enabling developers to analyze structured application data alongside visual materials seamlessly. Clef Flash features three specific formats for typed questions: "noul" for estimating the likelihood of a proposition being correct, "choice" for opting among a set of predefined alternatives with individual probabilities, and "score" for assessing input against a hierarchy of criteria to generate a probability-weighted score. Applications can pose between 1 and 64 questions in a single API call, with responses linked to the original question IDs. Potential applications include assessing the urgency of a support request, directing it to the appropriate team, and evaluating the severity of customer impact, thus enhancing operational efficiency in decision-making processes. By utilizing this model, organizations can improve their response strategies and streamline workflows further.
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Clef
Clef is a multimodal decision-making model with 27 billion parameters that transforms a given state and a schema of typed inquiries into structured outcomes. This model is capable of interpreting state information presented in various formats, including text, JSON, images, and video, and accordingly generates a probability for each permissible option linked to every question. Supporting a context window of 65,536 tokens along with vision input capabilities, Clef enables developers to analyze both structured application data and visual elements within a unified decision-making process. It accommodates three distinct formats for typed questions: noul, which estimates the likelihood of a proposition being true; choice, which allows for selection among predefined options while providing probabilities for each; and score, which assesses an input against a set of criteria to yield a probability-weighted score. Users can submit anywhere from 1 to 64 questions in a single request, with the corresponding answers returned under the same question identifiers, enhancing the model's efficiency and flexibility. This comprehensive approach ensures that a wide range of applications can leverage Clef's capabilities for diverse decision-making scenarios.
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