AI consulting firm

If your looking for the best AI consulting company in the US, you have landed in the right place. With over 100 use cases completed in AI, we are the #1 AI consulting company in US.

We will create bespoke AI solution precisely suited to your needs; we will analyze your data and help you design the exact use-case that applies to your situation; we will provide end-to-end AI consulting & implementation for the duration of your project.  If you want to get past all the hype and learn how AI can work for you, WildFire is your new home.

The expression “AI” has been bandied about C-suites for the last decade, full of promise—but for the average business, it has been a little short on delivery.  What is so essential about AI,what has changed, and why should modern industry now be vitally interested in it?

A Solution that Fits

AI has never been a “one size fits all” technology.  Each instance must be carefully custom crafted to suit the individual company because each company is unique.

Even companies that have nearly identicallines of interocitors, framastats, and doohickeys, have different customers; they have various collections of Big Data; they are probably in different regions.  To further complicate matters, each customer will have unique needs, and possibly wildly different uses for your particular product.

You shouldn’t be surprised to learn that although you sell your line of interocitors to NASA to control the fuel flow of rocket engines, your competitor, ABC Inc., markets a nearly identical framastat in India to control hydro-electric dam projects, while DEF Inc. sells their doohickey-version to Cruise Ships for onboard fire control.  It is just the way that life is—and that means you need an AI system that will reach your customers in the way best suited to them, and their needs.

Our Strengths

Unique Solutions

That is where we excel compared to others in the AI field.  You can seldom use somebody else’s solution effectively to solve your company’s unique challenges.  We’ve crafted more than 100 different use-cases for all of our customers since our inception in 2013.  Typically we complete an end-to-end use case tailored to your business in just 3 to 6 months, and that includes the POC (Proof of Concept), so you know it will do the job for you.

Domain Expertise

This is only possible because of our expertise in more than 20 vertical domains.  These include Automotive, Banking, Consumer, Education, Engineering, Energy, Oil and Gas, Fast-Moving Consumer Goods (FMCG), Finance, Food & Beverage, Government, Healthcare, Insurance, Legal, Manufacturing, Media, Online, Real estate, Retail, Technology, Telecommunications, Transportation, Electronics, Not-for-Profit, and provides the consequent ability to apply all our knowledge to understand so much more.

ROI Focus

There are multiple aspects to ROI (Return on Investment).  Sometimes it takes a little nudge for people to focus on the value of customer satisfaction and word-of-mouth advertising, beyond simple improvements to the bottom line.  C-suites need something tangible to show stockholders, vested interests, and stakeholders.  Black ink is a vital part of it, but we’ll teach you how to display them even more!

Early Birds

In recent memory, the first players on the field were the ones that had the most to gain—with names like Google, Azure, and Amazon Web Services (AWS) which are very familiar. Let’s consider one you may not have heard of recently (or ever):

Google’s almost-forgotten antecedent, based in Palo Alto California, was ALTAVISTA.  Paul Flaherty, with Louis Monier and Michael Burrows, virtually invented the modern day search engine.They developed “spiders,” programs specifically designed to “crawl the web,” and together theydeveloped the first powerful search engine that was believed to have the capability to index all existing Internet pages (in 1995).

When the Internet was “invented” in the late 1960s, it consisted of four computers.  Different colleges and the government wanted to share data but were using what was primarily a manualprocess.  As the body of data grew, that quickly became impossible without indices to help locate files.

By 1995, the spiders of Flaherty, Monier, and Burrows could search thousands of pagesautomatically, checking back weekly or monthly to update the page information about changes that had taken place.

Sorting out that information was a monumental task that required algorithms whichput related phrases together.  If you were searching for “bake,” you most likely didn’t want a link to Bakersfield Airport in California.  That was when it was still simple.

Modern, sophisticated searches have gone far beyond the bounds that are manageable by mere humans.  Indexing became a challenge forputting the results in a format that most closely recognized searchers’ needs.  It is a much tougher job than you might imagine.

Google eventually ended up dominating the market while BING and YAHOO (who purchased AltaVista and then closed it) tried to carve out their niches.

Once it came of age and became sophisticated enough, AI was the eventual answer to managing all this data.  Algorithms could sort and present data quickly, but real-time AI could create individually-specific algorithms for each unique searcher.  It was the only way to make it work, so Google did it through necessity.

How much information is there?

Estimates place the amount of information on “the Internet” at 500 million terabytes.   Google has indexed just 200 terabytes, for a current total of more than 35 trillion web pages indexed.  Put in perspective, however, that represents only 0.004% of the entire “Internet” content.  AI is no longer just an “option”; it is essential because of the explosive information growth in the last 25 years

How is AI good for Me?

On average, the entirety of human knowledge doubles now every 12 months.  Some fields advance more slowly while others zip ahead, but the average seems to be “annual” now.

Information is accumulating at such a prodigious rate that there can no longer be a“Renaissance Man” (or woman) who understands most fields of scientific endeavor.  The Ben Franklins and Nicolas Teslas of old would undoubtedly flounder trying to understand it all.

At WildFire we are intensely aware that if you throw an AI into the mix the whole game changes!

Prescience—seeing the future

You can know more about your customers than has ever been possible before.  Publically available information could tell you that one of your clients has opened up a new office or manufacturing plant in another jurisdiction.

That order for 250,000 interocitors might parley into twice that if you get to your customerbefore ABC Inc. and DEF Inc. find out. You could be primed and all ready to go while your competitors wonder how you even knew.

On the flip-side of that coin, an AI could see where a company had overextended itself and was in danger of financial ruin.  Now you have a deeper insight, and you can proceed cautiously in business dealings with them.

A human being might stumble upon such information.  A dedicated AI, scanning all news, business announcements, social media, and business media could probably isolate indications of an incipient expansion (or business failure)long before any news was reported on the subject.  That information can be yours with a well-programmed AI at the helm.

Big Data

There are additional ways to capitalize on your Big Data other than knowing your customers intimately.  A “fashion house” might optimistically put out the message that “white is the new black,” going on to create a whole new Summer Selection of clothing, fervently hopingthat people will believe it based on their industry influence.

Conversely, an AI looking at social trends around the world might recognize that pastels of green and blue are rising in popularity and favor among the fashion conscious.  As you can see, any industry can benefit if you have the right AI system in place to comb through publically or privately held Big Data.

Machine Learning Frameworks

The key to a long and useful business life for your AI is to use Open Source programming to create it.  We use these public domain tools, such as Tensorflow (Google Brain), Caffe (Berkley), MLlib (Apache), Scikit-learn, and others because they are designed to interface with multiple systems, are platform independent, and they are all under active development.

Don’t invest too early

It is still too soon in AI development to commit to a proprietary solution for your AI needs because the field is evolving so rapidly.  Open source provisioning means that the platforms are not tied to a particular operating system or interface.  That makes them easy to maintain, support, and update.

More importantly, what could be a significant financial investment is deferred until there is more consistency in the market.  Any person or company developing an AI system is hoping that their system will solve the most problems for all consumers, and be adopted by everyone.  Harken back to the days of Betamax and VHS videotape recorders though, and you’ll see that the “second best” system won the battle.

Winners can become losers

Instead of rallying behind a good company that might very well end up eliminated from the race, leaving you with no support, no updates, and desperately scrambling to find a replacement, now is the time to stick with Open Source.  Maybe Google Brain will win because they are big and have tremendous resources, but any engineer, even at the tiniest company that you have never heard of, can have an epiphany that can change everything.

Think of Bette Nesmith Graham (mother of Mike Nesmith of the Monkees) who invented Liquid Paper to erase typing mistakes; think of Helen Barnett Diserens who made her fortune by looking at a ball-point pen, imagining it much larger, and went on to invent roll-on deodorant.  The entire game can change in a moment when someone is struck by an insight.

The Cloud

Cloud is another buzzword that is overused, but it is vital to AI development.  If you have a custom AI system on your network, everyone who needs to access it must make a connection to it.  If you have a large international company, that amount of throughput could make access more difficult.

Your worker in Nepal or Bangladesh being required to connect with a server in your New York City head office could be at a severe disadvantage.  Your clients, looking to work with you from Japan or Australia, could experience significant delays when forced to go through the New York bottleneck.

AIaaS, or Artificial Intelligence as a Service, is a cloud-based solution.  You can hire space on a server as a home for your brand new AI, where they can provide support staff and access to their Data Scientists.  You upload your Big Data into a secure private space and get to work.

The difference is that such systems are distributed; this means they are in multiple locations and are continually being coordinated, so they all have the same data.  This consistently happens in the background so that there is never an appreciable difference between one system and another.

The primary advantage is that the worker or client in Australia (or Egypt, or China, or anywhere) has instant access from a local server, which is then updated all around the world in the background.

The Cloud saves money

Maintaining your own AI system requires a fleet of data scientists (at an average salary of $100,000 per year), but it doesn’t end there.  Infrastructure requirements include new servers, technicians to maintain them, rental space for additional duplicate off-site back-up, as well as Engineering Services for cooling and building maintenance.

The difficulty is further compounded by the fact that there are a limited number of data scientists graduating from the education system.  The supply doesn’t keep up with demand, and you may be hard-pressed to find someone to fill that role.

Creating your system in a virtual space “in-the-cloud” avoids the massive initial investment; your material is automatically backed-up, and is offsite, in multiple locations; and you don’t need to first locate, and then hire new staff because they are already there, in place, and ready to go.


Customers love ChatBots

Study after study has shown that people being put on hold when they make a phone call engenders anger in just 17 seconds; that wait times of over 1 minute are considered entirely unacceptable; that most people will hang up after only 2 minutes and, of those that don’t, they tend to be abusive towards employees.

There is a much better way.  Consider an electric utility during a power outage, receiving thousands of phone calls simultaneously.  There is no practical way for them to answer all of those phone calls. The inquiries are simple, but time-consuming since most people want an estimate, or a timeline until power is restored.

Provided with an alternative, most customers will choose to interact with a ChatBot.  One single ChatBot can handle thousands of simultaneous conversations with clients.  Its responses are instantaneous, and customers don’t feel shy about consulting them over and over again until information becomes available—and it only takes one employee to update the ChatBot as new information becomes available.  All the other staffers can be handling much more complicated matters.

Better Service

We are not far from the date where speaking AIs will be able to respond directly to human voice inquiries via phone lines or internettelephony.  They might be indistinguishable from humans.

Despite their current utility as ChatBots for texting, soon they will interact with our customers verbally, providing them with that most valuable asset—someone to listen to them—the value of which is almost incalculable!

Contextual analysis, voice-stress, word-usage, and countless other factors will make them compassionate, able to “understand” clients and respond appropriately.  Eliminate the exhausted, overworked employee and replace them with an AI that is always ready to listen. We’ll discuss that further, below.

Predictive Analytics

This is the science of clustering and segmenting information, of finding trends, relationships, and anomalies.  WildFire will help you use this data in a way that will lead you to success ahead of your competitors.

Predictive Analytics provides an almost preternatural understanding of how prices fluctuate, the best way to distribute asset investment to maximize ROI, and how consumers will respond to product classifications, intended uses, or labeling designs.  It’s the closest you will get to owning a crystal ball to predict the future.

It also can understand activities that are out of place, such as fraud detection, money laundering, or illegal asset movement.  When one new bank account (for instance) suddenly receives balance transfers from half a dozen pensioners, it can immediately be frozen for investigation.  The odds are that the customers might never know the event occurred as their funds are returned to their accounts, and the bank’s reputation remains untarnished.

Big Data

These words come up all the time.  There are many different approaches to Big Data, but you don’t have to worry about that.  WildFire’s expertise covers Hadoop, Spark, Kafka and many other Big Data management technologies.  We can handle it all.

Machine Learning & Deep Learning

AIs evolve (or learn) by looking at thousands of cases covering a situation, analyzing the strategies employed to resolve it, and then recording the positive results, the negative results, and the instances which produced a neutral effect.  In future encounters with that situation, it can pick the best solution for the circumstance.

Deep Learning takes it one step further.  It assumes that experience it has gained and learns to apply it to significantly different experiences.

Because of the computer’s tremendous speed, it makes very little difference if it reviews a million different possibilities.  A human can intuitively eliminate many options, but a computer is so fast that it can look at them all—even if they appear irrelevant—and still arrive at a logical result more swiftly than a human being.

The Future

It’s said that a full-scale, functional Artificial Intelligence is that last thing humans will ever need to invent. It’s still quite a ways off in the future, unfortunately, but we’re getting there.

Once we succeed we can dedicate ourselvesto research, learning more and more, so we can add it to humanity’s collective database.  Let the AI sort through it all and correlate it.

We will ask it questions; it will consult the database of “All-knowledge”; out will come an answer.  There will be no need to wonder if teleporters or “matter transmitters” are possible.  It will either say “no, according to current knowledge” or print out the plans for building it…


We touched on this area earlier when speaking about ChatBots understanding verbal language.  NLP or Natural Language Processing isvital for AI models to learn and interpret writteninformation as well, and then garner insights from it.

The ability to understand the written word teaches AIs about context and meaning. It is also vital for progress in learning verbal skills. NLP has been getting better and better because of the resources we have made available to it.

It was only halfway through 2018 when an AI finally outperformed a human on a reading comprehension test, and now C-suites are abuzz with the notion that Verbal AI is ready for its business debut.  What was considered a “Technology” has now graduated to the status of “Product.”

Google’s project to optically scan all 130,000,000 existing unique books in the worldinto digital format uses Optical Character Recognition (OCR) and automated page turners.  This will contribute to the available database of all human knowledge and, not incidentally, help to train our AIs to truly understand the ambiguity of English (or any of the 130+ languages that Google can currently translate to-and-from English).

The Takeaway

We are experts in Artificial Intelligence and will work diligently with your in-house team to deliver projects on time and on budget, relying on our in-house resources.  We can also fulfill the DevOps role to design, develop, and maintain your AI solution.

Our AI systems are modular, using a plug-and-play design, so that new capabilities and be added as needed.  We can assemble a complete system in record time, ready to deploy.  We’re realistic and honest, telling you what is possible, and differentiating between what remains to be developed.  No empty promises, ever!

We are the best AI Service Company in the United States, and we deliver value by understanding your specific use-case, developinga clear roadmap, and then implementing it so that your custom-designed AI system meets allof your challenges.  We’re not going to “sell you a program” and then vanish—we’re going to see it through right to the end.  We want to provide you with the best possible AI System because your success is our success.

Contact us here to get started

Contact us
Rest assured, you will not be spammed!
Ex: best AI consulting company