Servis

Servis

Predicting equipment and service failure & its potential causes

Features

BACKGROUND

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

KEY FEATURES

  • 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)

BACKGROUND

  • 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

KEY FEATURES

  • 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

BACKGROUND

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.

KEY FEATURES

  • 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

Key Differentiators & Business Benefits

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Anomaly Detection

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

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Knowledge Graphs

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

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Domain Ontologies

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

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Data Cleansing & Harmonization

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

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External Data Enrichment

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

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Reinforcement Learning

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

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User Interface and APIs

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

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Deployment & Scalability

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

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Real-time Insights

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

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Product Accuracy

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

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Time & Cost

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

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Feature Engineering

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

For Demo & Additional Information

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