Introduction to BizTexter:
We are changing the way consumers interact with businesses by providing an intelligent text messaging tool for digital customer service and follow-up marketing. The power of big data and artificial intelligence has been simplified and made available to businesses with no long term agreements or months of setup time.
Our mission is to cause a paradigm shift in the market from B2C over to the more empowering C2B. “Consumer to Business” is far more powerful and revenue generating than annoying customers with advertisements. Allow the consumer to engage with your business, on their terms, and the loyalty will bring you far more sales than you probably could imagine.
Consumers want to order their favorite products and services through mobile messaging and IoT devices such as Direct TV, Uverse, smart TVs, Appliances and the coming Smart WiFi routers such as Amazon Echo and Google Now’s router. Of course, smartphones and tablets lead the way.
In the future, we want to bridge the gap between retail & services and this demand, by providing a free mobile messaging interface to all businesses in exchange for a % of sales that come through our system. Right now, we charge for usage the same as any telecom reseller. We can open truly C2B messaging to the world!
Other Notable Achievements:
Vision For Future:
Basically, consumers demand to text message with businesses. Telecoms are being forced to recognize this. OTT apps and cloud carriers do NOT have an on premise mobile device to provide businesses and therefore, are at a decisive disadvantage against a BizTexter/AT&T solution. The business wants ALL the features a mobile phone or tablet provides to their staff and OTT messaging does NOT work when data is down or there is a problem with WiFi.
We are building an artificial intelligence NLP platform ecosystem for connecting the “on-demand” economy and “API” economy to the “IoT” economy, through the “Mobile Messaging” channel.
Another way to put it would be, a mobile messaging marketplace connecting businesses with consumers for sales, upsells, cross-sells, reactivations and transactional.
Latest Demos of Technology
You can play with the latest iteration of the deep learning NLP Neural Net technology through our Twilio-powered Uber demo. (This was supposed to be powered by AT&T!)
Text message this phrase to Uber demo:
“I need a ride”
to this number:
Follow the instructions on how to use it. It requires a full FROM street address as well as a TO street address in the same text message to work.
Don’t worry, a real Uber won’t be ordered. This is an inactive demo to show the latest technology.
You can also test our Hotel demo by texting:
to this number:
When you are done, play around with it and ask it questions that a consumer would ask a business like “who are you”, “why are you texting me”, “Can I speak with someone on the phone”, etc., etc.
You can even get angry and see if it handles the escalation correctly, i.e. “I am very upset. The waiter served me cold food!”
We were IoT before it was a thing. We were turning cell phones and tablets into IoT natural language (NLP) processing routers with onboard business logic. In the words of Motorola’s 1st responder division “You have basically created a deployable mobile server with NLP and command logic.” In fact, we have almost 6,000 business users using these devices as an on-premise messaging hub for consumer interactions. We have extended this capability to the cloud for enterprise businesses.
IoT- Internet of Things
Cell phones are now and will likely continue being the central processing hubs for most IoT interactions. The primary use case that we are going after is consumer-to-machine messaging that requires NLP automated responses and logic based transactional processing.
The consumer uses their Voice-to-Text program on their phone to speak a message that is converted to a text message and it gets sent to one of our businesses. Maybe something like “Do you have any specials right now?” Our system running on a mobile device for the business, on-location or in the cloud, receives that message, runs through the NLP business logic and sends an auto response: “Yes, in fact, we do. We have a medium 2 topping pizza for $5.99 each. Would you like us to charge your card on file?”
A consumer tells their TV to turn on and shows a list of comedy shows to watch. The audio processing will take place in the consumer’s phone, the tv remote or in the TV itself just like it was converted to a text message in the scenario above. However, after it converts that message to text and an onboard linguistic processing will take place to convert the text to actual control commands as if they pressed the buttons on the remote directly. This requires onboard business logic to work with the NLP just like our systems work now.
This scenario will apply to all human to machine interactions in the future.
NLP AI – Machine Learning
Both on the device and in the cloud there are algorithms for understanding NLP as well as figuring out which commands to execute on behalf of the user. The data is segmented by Industry, so our data covers interactions in general but picks up on the nuances between different industries.
What a consumer says to their doctors office may be drastically different than what is said to the local pizza parlor, even though some words will cross over.
Big Data & Analytics
Every interaction with consumers by our business’s mobile devices is anonymized and recorded. The data is processed in the cloud and available for analytics. Businesses can see their own stats, and additionally, they benefit from the interactions of every other user.
Periodically, the cloud AI algorithms process all the data and redeploy cached versions of the NLP to the IoT mobile devices on a per-industry basis. This ensures each deployed system has the most relevant NLP available for its unique circumstances and, therefore, can operate in very low power situations such as in an IoT CPU chip.
Enterprise IoT Problem
All enterprises attempting to enter the IoT market will face several big obstacles. The 1st is the sheer amount of data. The second is the cost of the bandwidth for millions of devices talking to their cloud system. To make matters worse, what happens when the IoT devices attempts to communicate with the cloud are blocked by lack of WiFi or other interruptions in the communication streams. Low power devices and small memory chips can’t store every NLP library on the planet.
The logical fix is to have as much of the processing happen “on-location” or “in-chip” as possible, and only report back to the cloud periodically when there is a relevant need to do so. Then have the cloud do the heavy data crunching and afterward, send fresh NLP data sets back down to the device, so it only has to know what it needs to know given its unique circumstances.
This solution requires both NLP and business logic command processing to happen within the chipset of the IoT device itself and communicate with the end user as well as the cloud via a radio chip. It’s a reciprocal cycle of big data collection, machine learning processing and storing local business logic per device locally. This is the exact scenario covered under our IP and patent filing US 20140087697 A1.
No major player in the IoT market can claim a 2012 patent filing date prior to ours for these unique circumstances let alone an IP claim going back to 2009. It is inevitable that our technology and IP will massively benefit one or more of the major players wanting to play in IoT.