Austin Ai: Digital Transformation without the SaaS.  

We write custom, robust and productionized software to solve specific use cases in the financial, industrial, energy, and technology sectors.

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Using Social Media Data to
Give a Hedge Fund an Edge

Business Goals:

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Identify "Meme" stocks frequently mentioned in a positive light on social media platforms.

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Score companies based on ticker frequency and sentiment analysis.

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Identify metrics predicting stock volume and price movements.

Data Sets & Models:

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Data Sources: Historical data scraped from online platforms like Reddit and YouTube, with audio transcribed to text for analysis.

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Custom Finance Sentiment Modeling:

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Utilized BERT Model trained on financial corpus for sentiment analysis.

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Customized phrases for each site and analysis of emojis to capture nuanced sentiments.

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Intelligent Natural Language Processing (NLP) applied for accurate ticker/company extraction.

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Modeling:

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Several models, including neural networks, are trained to predict stock volume and price based on sentiment scores.

Photograph of a person handing over a credit card to another person for payment, symbolizing the evaluation of credit and payment history in a case study on construction payment credit rating.

Outputs & Benefits:

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Stock Rating Calculation:

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Overall stock rating is determined by combining the Ticker Mention Frequency Score and Sentiment Score.

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Portfolio Formation and Trading:

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Actual portfolios are formed and traded based on the ratings derived from sentiment analysis.

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Portfolios sold to retail investors seeking exposure to "Meme" stocks.

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Production Environment:

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Automated scraping and calculations run multiple times per day in a production environment to ensure real-time insights.

Implementation

Results:

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The implementation of sentiment analysis for "Meme" stock rating enabled hedge funds to identify stocks with high social media visibility and positive sentiment.

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Actual portfolios formed based on sentiment analysis ratings yielded favorable returns, attracting retail investors seeking exposure to trending stocks.

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The production environment running automated scraping and calculations multiple times per day ensured timely insights for trading decisions.

Conclusion:

Austin Ai empowered a hedge fund to effectively identify and assess “Meme” stocks using scaping and NLP on social media sites. Our approach broadened the fund’s library of alpha signals in a unique way, allowing them to make better trading decisions which increased return and reduced risk for its customers. The system was fully productionized in an institutional quality manner.

Case Study:
ChatBots & LLMs

We have extensive experience with customizing,
training, and deploying Chat technologies.

Promotional information highlighting 'Succinctly,' a free software tool allowing document upload and ChatGPT interaction for queries in natural language.

Succinctly www.succinctly.io is FREE software from us which allows companies to upload internal & external documents and have ChatGPT answer questions about them in a natural language format.

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It is offered for FREE as an incentive to build a relationship and to bid for customization work.

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Allow employees to ask questions about HR documents / polices, training manuals, product documentation, etc.

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Summarize any documents like news articles, websites, public company filings, like 10-Qs, or research papers.

The more hands-on tools like Rasa & LangChain require heavy customization:

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Must be fed appropriate lists of entities, topics & patterns.

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Must be retrained (Rasa's neural network).

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Must almost always be linked to the client's internal systems.

Illustration of a computer monitor displaying a chatbot interface for 'Succinctly,' a free software service enabling document upload and ChatGPT interaction for natural language processing.

The combination of multiple tools like:

can be far more powerful than any one technology alone.

Case Study:

Automatic Blueprint Reader

Image of a detailed construction blueprint used in a case study for an Automatic Blueprint Reader, highlighting the technology's ability to read, analyze blueprints, and integrate into order management systems.

Outputs & Benefits:

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Reduces a 2-hour manual process to under 2 minutes.

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Saves multiple $MM per year.

Business Goals:

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Automatically read construction blueprints from contractors.

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Figure out what plumbing parts to order.

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Automatically submit the list of parts into the order management system

Data Sets & Models:

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Blueprints from the client's clients.

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OCR.

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NLP on the OCR results.

Case Study:

Solar Panel Soiling Forecaster

Business Goals:

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Forecast how dirty panels on solar farms will get based on surrounding conditions

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Compute optimal time to wash the panels (which is very expensive) versus electricity lost from soiling

Data Sets and Models:​

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Particulate matter trends from EPA​

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Weather history from NOAA interpolated over a grid over the entire USA​

Output and Benefits:​

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Makes $MMs per year per solar farm in reduced washing costs and increased power generation​

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Better for environment in terms of water usage ​

Case Study:

Construction Payment Credit Rating

Business Goals:

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Evaluate the credit & payment history of
general contractors, property owners, and hiring parties.

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Extend the appropriate amount of credit in project financing deals.

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Anticipate & reduce write-offs.

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Augments traditional credit reporting tools/companies.

Photograph of a person handing over a credit card to another person for payment, symbolizing the evaluation of credit and payment history in a case study on construction payment credit rating.
It's the letter A, representing an example company in the case study.

Company A

Payment Risk Score

669

A

6431 jobs in the last 6 months per information available.

Job Growth

27%

Industry Average: 5%.

Payment Speed

66

days

Industry Average: 88 days.

Dispute index

2%

Industry Average: 5%.

4/5

17 Ratings

Social Sentiment

👍Positive

Common Job Types

Residential

Annual Sales

Over $35 B

Employees

5000-9999

123 Sunset Blvd, Hilltown, CA 99922

Data Sets & Models:

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Many large, disparate data sets:

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Large databases of construction project, lien, invoice, company, etc.

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Graph database of network effects / relationships

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User reviews & ratings.

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Extensive decision tree computes hundreds of clean metrics.

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NLP sentiment analysis on customer reviews.

Outputs & Benefits:

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Risk reports sold to third parties as a data product:

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Potential six-figure additional revenue.

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Internal credit rating aids in internal credit decisions:

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Reduces default rates by 30-40%.

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Construction industry statistics for marketing, white papers, etc:

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Bolsters firm reputation.

Case Study:

Web Purchase Forecaster

Business Goals:

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Analyze browsing of retail web users and predict when they are about to purchase something​

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Serve advertisements / discounts at the optimal time​

Data Sets and Models:​

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Time series of domains, search terms, timestamps, geographies of users​

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Intelligent transformation of original inputs​

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Several features using large language models and/or NLP​

Output and Benefits:​

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Predicts 7 out of 10 user purchases.

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Predicts Amazon category the user is interested in.​

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Optimizes advertising spend and timing.​

Case Study:

Real Estate Valuation

Aerial view of a quaint village with houses and autumn trees, representing a real estate valuation case study focusing on investment attractiveness and property value trends.

Outputs & Benefits:

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Composite investment indicator.

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Visualization by zip code on an interactive map.

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Identification of outliers (under- or over-valued locations)

Business Goals:

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Grade physical locations on investment attractiveness.

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Identify trends in demographics & other time series.

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Relate property values to explanatory variables & their rates of change.

Data Sets & Models:

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U.S. Census from 2012 (TB of data).

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Zillow home price estimates.

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Yelp reviews.

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School reviews.

Case Study:

Predictive Maintenance

Business Goals:

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Anticipate equipment failure.

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Increase predictive maintenance.

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Reduce reactive service calls by 30%.

Outputs & Benefits:

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Variables & patterns most related to future failure;

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Provides engineering insight into failure points.

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Probability of failure within various time periods.

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Model statistics like precision, recall, false positive rates, etc.

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Anticipation & reduction of service calls:

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Calls reduced by 30+%.

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Costs reduced by 20+%.

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Many on-demand calls transformed into anticipatory ones.

Photograph of a worker performing maintenance in a high-visibility welding helmet, illustrating predictive maintenance work in a case study.

Data Sets & Models:

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Log data from equipment.

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Sensor readings.

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Error, warning, and status codes.

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Failure flag.

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Machine ID#'s and diagram of manufacturing process.

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Both random forests & neural network models trained on data.

Ready to Get Started?

Contact us for a no-cost assessment which includes a consultative discussion on business needs, an evaluation of data readiness, and initial modeling.

Free Assessment
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