Austin Ai offers  
software solutions for specific use cases.

These frameworks are never blindly applied to nor obfuscated from clients, but rather integrated into their own infrastructure using the appropriate customization and attention to detail.

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Case Study:
ChatBots & LLMs

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

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.

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

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

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:

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

Must be retrained (Rasa's neural network).

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

The combination of multiple tools like:

can be far more powerful than any one technology alone.

Case Study:
Automatic Blueprint Reader

Outputs & Benefits:

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

Saves multiple $MM per year.

Business Goals:

Automatically read construction blueprints from contractors.

Figure out what plumbing parts to order.

Automatically submit the list of parts into the order management system

Data Sets & Models:

Blueprints from the client's clients.

OCR.

NLP on the OCR results.

Case Study:
Construction Payment
Credit Rating

Business Goals:

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

Extend the appropriate amount of credit in project financing deals.

Anticipate & reduce write-offs.

Augments traditional credit reporting tools/companies.

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:

Many large, disparate data sets:

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

Graph database of network effects / relationships

User reviews & ratings.

Extensive decision tree computes hundreds of clean metrics.

NLP sentiment analysis on customer reviews.

Outputs & Benefits:

Risk reports sold to third parties as a data product:

Potential six-figure additional revenue.

Internal credit rating aids in internal credit decisions:

Reduces default rates by 30-40%.

Construction industry statistics for marketing, white papers, etc:

Bolsters firm reputation.

Case Study:
Hedge Fund, Meme Stock Rating Score

Business Goals:

Identify the set of "Meme" stocks most frequently
mentioned in a positive light on social media.

Score companies by ticker frequency & sentiment.

Identify which metrics predict stock volume & price.

Data Sets & Models:

Automatically scrape historical data from online sources
including Reddit & YouTube (audio transcribed to text).

Custom, finance-specific sentiment modeling:

BERT model trained on financial corpus.

Custom phrases per site & analysis of emojis.

Intelligent NLP applied to get clean ticker / company extraction.

Several models, including neutral networks, trained
against volume & price.

Outputs & Benefits:

Overall stock rating = (Ticker Mention Frequency Score + Sentiment Score)

Actual portfolios formed & traded upon the ratings, and sold to retail investors.

Production environment runs all scraping & calculations multiple times per day.

Case Study:
Real Estate Valuation

Outputs & Benefits:

Composite investment indicator.

Visualization by zip code on an interactive map.

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

Business Goals:

Grade physical locations on investment attractiveness.

Identify trends in demographics & other time series.

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

Data Sets & Models:

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

Zillow home price estimates.

Yelp reviews.

School reviews.

Case Study:
Predictive Maintenance

Business Goals:

Anticipate equipment failure.

Increase predictive maintenance.

Reduce reactive service calls by 30%.

Outputs & Benefits:

Variables & patterns most related to future failure;

Provides engineering insight into failure points.

Probability of failure within various time periods.

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

Anticipation & reduction of service calls:

Calls reduced by 30+%.

Costs reduced by 20+%.

Many on-demand calls transformed into anticipatory ones.

Data Sets & Models:

Log data from equipment.

Sensor readings.

Error, warning, and status codes.

Failure flag.

Machine ID#'s and diagram of manufacturing process.

Both random forests & neural network models trained on data.

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

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