Predicts financial & business risks and trends for financial, pharmaceutical and service industries



  • Defects in a production line process often lead to defective products
  • Sensors collecting data can become defective, making it hard to determine defects


  • Extracts the following with ~99.7% accuracy for 100+ parameters in a cycle:

    Each process cycle and timestamps for the start and end of the cycle

    Each segment of the process cycle and the corresponding functional waveforms

    Anomalous cycles using graphs & relationships among various parameters

  • Identifies in a production line:

    Products that are likely to have faults; tools or equipment that may have issues

    Key attributes of tools and equipment that may be “out of bounds”

    Idle time and change-over time (i.e., moving from one product to another)


  • Technical support centers own vast data in varied formats for past requests & resolutions including voice2text, emails, product manuals, sys-logs & config files
  • Complexity increases due to technological advances as more products & features are introduced & old ones are replaced


  • For a new request, recommends similar past cases & bug-fixes with highest score

    Automatically processes data, removes noise & extracts meaningful information

    Auto-fills tickets and routes incidents & cases based on past information

    Helps engineers find appropriate domain experts to speed up resolution

  • Reinforcement learning on “its own” and via a “human loop”
  • Authentication & Authorization – Ability to manage users, their hierarchy and roles


Brick and Mortar stores have customer service representatives (CSRs) who need to be matched with customers who require help. This is often hard because most customers don’t approach CSRs or they have to wait in line.


  • Connects CSRs & customers using 30+ proprietary knowledge graph algorithms
  • Identifies CSRs & provides their behavior patterns, e.g., walking, communicating
  • Provides location and duration for each CSR throughout the day
  • Identifies delay in serving customers, e.g., average wait time, max wait time
  • Identifies for each CSR idle time vs time spent with customers
  • Generates alerts for poor customer service, e.g., long wait times


  • Need for more accurate, timely risk assessments, is critically dependent on standardized spread of financial and non-financial risk inputs.
  • Banks are working in diverse portfolios across geographies and hence the operational regulations. Keeping track of everything manually in real time in order to prevent losses could be costly in terms of time as well as money.


  • Provides a network view of your customers
  • Score customers’ business performance as well as of their customers
  • Forecasts customers’ likelihood to default based on their cash flow patterns, spending behavior and financial health
  • 600+ attributes to compute scores
  • Scry’s IP in feature/signal selection & engineering
  • Up to 4 times lift in response rate to marketing campaigns
  • Pre-built and custom APIs & connectors for importing & exporting data
  • Processes more than four million records in an hour

Key Differentiators & Business Benefits


Anomaly Detection

Library of 25+ proprietary supervised & unsupervised algorithms, pre-trained & tested for our products


Knowledge Graphs

30+ probabilistic spatial & temporal graph algorithms to determine links & connected entities


Domain Ontologies

Prebuilt financial ontologies & business rules that each product will improve over time


Data Cleansing & Harmonization

60+ algorithms for cleansing, harmonizing & validating data from internal & external sources in 30+ formats, e.g., JPEG, video, PDFs


External Data Enrichment

40+ scrapers & connectors for ingesting web & paid data subscriptions, e.g., news, social media, traffic, geo-location, sanction lists


Reinforcement Learning

In-built reinforcement learning algorithms that improves our products’ performance over time “with & without a human loop”


User Interface and APIs

Pre-built graphical user interface (GUI) & APIs for quick deployment & integration with clients’ workflows


Deployment & Scalability

Highly scalable with parallel & distributed computing with options to deploy on-premise or consume as SaaS


Real-time Insights

30+ Proprietary time-series algorithms that use parallel & distributed computing to generate real-time insights


Product Accuracy

95% – 99% accuracy using deep domain expertise, which improves over time


Time & Cost

60% – 85% improvement in time & cost over the current manual process


Feature Engineering

Incorporates 1,000+ signal-features & reduce noisy-features to reduce false positives & produce alerts in real-time

For Demo & Additional Information