Clare golden

america’s most trusted team

Rushmore A.i


Machine Learning has become more than a buzz word and an integral part to the operational and tactical success of many organizations. In view of foregoing, there are many impediments to streamlining the needs and use cases of companies wanting to employ ML and A.I based services within scopes of BAU. This conundrum is quite evident as teams struggle to prepare large datasets for analytics, manage the proliferation of machine learning frameworks, and move models in development to production. We endeavor via our current Rushmore A.I Labs, platforms and collaboration to maximizing operational efficiency via the following:

  • Predictive maintenance algorithms.
  • Delivering innovation with new discovery or new business models.
  • improving customer experience and creating new engaging personas
  • Consultation and on-premise scaffolding of ML toolkits, bleeding edge infrastructure, including seamless migration to the public cloud.



Develop pipeline and workflow for Natural Language Processing ingestion and abstraction. Develop applicable predictive models for predictive intelligence, analysis and insights.

E.g – CRF and Linear Regression.

1. Document Ingestion
Use case – identifying and indexing sections, headings, paragraphs in machine readily document.

2. Information & Contract Extraction
Use case – extracting key pieces of info from different document types including machine readable and non-machine readable formats (OCRs).

3. Automated Writing Assessments
Use case – score, analysis and feedback.

4. Document Classification
Use case – classifying text as one of k or n attribute types.

5. Relevance Assessment
Use case – determining the degree to which piece of text is relevant to another.


Develop pipeline & workflow for ML algorithms to find hidden patterns and detect unforeseen anomalies and deliver business Innovation, or new models to sustain growth in face of disruption.

1. Retail Commerce

Use case – to aggregate online and offline data and to recognize patterns in the data that could positively influence pricing, inventory, customer experience, and profitability.


Develop pipeline and workflow facilitating predictive and applicable learning models.

1.  IoT/IIoT Predictive maintenance
Use case – determine condition of in-service equipment & predict timely maintenance performance.

2.  Equipment Preventive Maintenance
Use case – enhance accuracy of failure prevention and predict maintenance scheduling

Use case – train and improve corrective actions

3. Inbound Logistics Planning
Use case – management of suppliers and goods delivery to business (who’s receiving what at rightful time/place)

Use case – gathering and feeding data on existing planning (complex process of orders management, shipping, warehousing, inventory control) into model, thus predicting & driving recommended future processes.

4. Medical Diagnosis
Use case – detection/prediction of Cancer, Alzheimer’s, Covid19, Pneumonia

5. Transportation & Delivery Services
Use case – predicting arrival times, pick up locations, food delivery.

6. Weather Forecast 
Use case – predicting Weather Forecasts, storms, floods, etc.

7. Energy Sector 
Use case – Gas & Oil Location.

8. Trader Markets Prediction 
use case – Buyer/Trader Action
Stock Market Prediction.

Use case – Loan Risks Analysis

9. Customer Centric Modeling 
Use case – making recommendation to customers on basis of past interactions (asset purchases, account use over time)


Develop pipeline and workflow for Time Series analysis and predictions models about the future based on sequence of past/previous events. Capability to predict a single entity like revenues for a company, or vectors of entities. Create Data Classification Models.

1. Time Series Prediction 
Use case – given sequence of observed values (including whole actors of values), predict next observed value(s) as well as provision of variance on the estimate.

2. Field & Classification
use case – classification that assigns labels or predictions at a single point of time, which can be achieved via suggestion of classes (automatic clustering).

E.g – medical diagnosis, image categorization & clients segmentation.

3. Fraud Detection 
Use case – implement time series & deep-learning techniques to find anomalies (Anomaly Detection) in massive customer data sets.

E.g – Credit Cards, Car Loans, Merchant Fraud Detection.

4. Clustering 
Use case – Dividing data collection into groups that are similar.

Use case – Identify and manually relocate items from one cluster to another to fix errors.

Voice Analytics

Develop pipeline and workflow for extracting information from spoken communications.

Ability to render output amenable to NLP analysis as well as analyzing tune and rhythm (prosody), tone and emotion, speaking rate for understanding intent, meaning & user state.

1. Speech Recognition
Use case – take an audio and convert to text stream for both streaming continuous transcription of conversation (online) and offline (file) prior to processing

Use case – acoustic & language models adaptation/recognizer to audio from particular source/domain

2. Keyword Search
Use case – indexing of audio files and streams for locating specific keywords.

Use case – phonetic on-demand indexing of new keywords.

3. Emotion Detection
Use case – predicting emotions of speakers from given streams, file of audio recording

Deterministic High Quality Data Sets

We offer services to help organization create/generate range of logistic regression problem to study effect of varying amount of data.
The amount and quality of training data is often the single most dominant factor that determines the performance of a model. Once you have the training data angle covered, the rest usually follows. But exactly how much training data do you need?

See where your data classification needs fit:

New Data
Label Data – for companies that have never dealt with ML in their businesses, to provide services for Supervised Learning. We provide tools to help or offer services for effective data labeling.

Clean Data – cleaning unstructured data

Enough Data – given varying tasks, desire performance, complexities and input features, need to build models to determine how much more data is needed

Visualization Data & Tools, MLAAS

Creating real time data visualizations tools for companies with constant data streams. See what’s happening at all points of production chain, recognize anomalies and how new factor affects existing ones.

E.g – tableau, Google Chart

We also offer full range of services for Machine Learning As a Service (MLAAS) on our associate Goji Platform for Data Analytics and key Data Insights.


Diversified products

Nationwide Warehouses

Daily Access Code Sales

Contact us today with a business consultation and see where you fit!