
Ask HN: Do AI/ML companies actually have real customers? - jihoon796
I&#x27;ve seen many companies attempt to use AI&#x2F;ML as a business model.<p>Personally speaking, I left my own job earlier this year due in part to deep disagreements about how my previous company was utilizing and marketing itself as AI-based, even changing its name to include &quot;AI&quot;.<p>But when I left fintech to work in the healthcare industry (totally different role), people started coming to me to pitch their products&#x2F;solutions. I started getting pitched AI products (harness the power of AI &amp; predictive analytics!), and the use of buzzwords was pretty outrageous. I started wondering...do these companies have any real customers? Who are buying these products, and are they seeing a sufficient-enough ROI to justify these purchases?
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smt88
I find it hard to wrap my head around this question because "AI" is a relative
(and moving) target.

Alexa would be considered advanced AI in the 90s, wouldn't it? Google Search
or Facebook ad targeting might be, too. They certainly fit some definition of
ML.

It sounds like you're thinking of companies that are doing simple data
analysis and calling it AI, right?

~~~
2pointsomone
Agreed. Google Search encompasses some of the biggest ideas of AI.

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carlesfe
The most difficult thing to do is to pitch a _product_ instead of a
_technology_. This is more accentuated by the fact that we're mostly engineers
and we're not good at pitching benefits vs features.

But I can assure you that customers do appreciate the AI component, especially
when explained to them. I do a LOT of education in commercial meetings and
demos. Some of our customers we invited to our offices for a more in-depth
explanation of our technology.

My experience is that customers' interest is piqued by buzzwords, but if you
are able to explain the tech in plain words, they are astonished and the sale
is much easier.

To summarize, if you are raising money it may be worth it to buzzword the hell
of your deck, but when selling the product the key is to focus on benefits +
education.

Disclaimer: Am an AI startup founder
([https://optimusprice.ai](https://optimusprice.ai))

~~~
paulie_a
You are incredibly correct. If you are pitching the tech vs the product it's
game over.

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duchenne
In the first startup I created, we made ML-based underwriting and credit
scoring models for several banks and insurance companies. The results were
astonishing. For instance, one insurance company improved its profit by
~70M$/year, thanks to our model (according to our client's report from actual
data, not simulation).

But, it was actually tough to sell, because of several factors. 1) The
internal risk team did not really like a technology that they could not
understand. 2) It was hard to give an explanation of why it would work to the
executives.

In the end, we were getting 2-4 big clients per year.

~~~
joss82
And then what happened to the start-up?

~~~
stephengillie
The AI fired all the humans and became an Etherium contract.

~~~
duchenne
I am not sure that a credit scoring system would become the first AI to reach
consciousness :P

It is just an ML model, it just reads some inputs, then outputs some score.

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vonnik
AI is not a business model. It's usually just a component in a larger
solution. You need to get a lot of other things right for AI to produce value,
notably around the data you gather and pre-process. For most startups, that
data is hard to access, and without it, their algorithms won't have much to
train on.

While there are a few horizontal ML/AI/data science vendors, most AI is
invisible, a component in some vertical solution, and consumed by people and
businesses that don't really care how the results are produced, as long as it
works.

Very few companies are good at applying AI across the board, like we see at
Google, but the number of businesses using predictive models at least in some
capacity is growing.

~~~
JBCaptain
Hi There - Could you please clarify what you mean by "horizontal vs. vertical"
solutions/vendors? Maybe you could provide an example of each of those?

~~~
vonnik
Horizontal means platforms, frameworks and tooling that are agnostic to the
industry they're applied to.

Skymind is a machine-learning operations company (one of several): we help
businesses train, deploy, monitor and update AI models. The same software is
used by telecoms, finance, e-commerce and automotive companies -- horizontal.

There are countless vertical specific companies: e.g. Merlon applies ML to
anti-money laundering.

------
lewisjoe
We use DL for our grammar checker in Zoho Writer
([https://zohowriter.com](https://zohowriter.com)). We don't advertise our
product as "The next generation intelligent word processor".

It's just one of many features that's best done with AI. I like it that way.
Pitching utility is so much better than pitching how we built something with
blockchain, AI and IOT combined ;)

~~~
apexkid
I hate to see that I need to login just to take a demo of your product. I find
it a bad approach. First let the user checkout your product without intrusion.

~~~
lewisjoe
We do have a trial editor without sign-in. Use this
[https://writer.zoho.com/writer/publiceditor.im](https://writer.zoho.com/writer/publiceditor.im)

------
anonymous5133
Right now the whole AI/ML is basically just a buzz word everyone uses because
it is popular to use.

In reality, a true AI/ML company is basically just using AI/ML to create
services/solutions for existing issues.

Case in point: google is using ai/ml to improve their speech to text and text
to speech services.

------
pilooch
What do you mean exactly ? I've been running a machine learning /
reinforcement / optimization service company since 2015 and our customers
range from the largest EU corps to small and mid size startups. If you mean
how many customers use AI/DL/RL in production right now, it's a smaller
percentage of our customers, around 25% I'd say, but growing, slowly. Most is
about research roadmaps and testing this testing that, though these can be
yezr long projects.

DL integration into or as a replacement to pieces of software stacks, plus
long term model updates, measurements, testing and avoiding regressions (very
hard, think of ensuring all past samples are similarly predicted as before
after a model update, that's beyond simply improving the accuracy) is
difficult and standard practice is building up slowly.

~~~
ChaitanyaSai
Hi, what services do you use for data annotation? We've been using DL for some
projects of ours and had to set up a data annotation team (remote) to get more
control over the labeling process. We are looking to see if others might be
interested in utilizing a service like ours, which will also help the people
we've acquired gainfully employed. At present we are mainly looking at taking
on small projects. If this is of interest, please do let me know! I posted
about this here, but hasn't surfaced enough to generate conversation
[https://news.ycombinator.com/item?id=17874184](https://news.ycombinator.com/item?id=17874184)

------
leonroy
I know Seldon have real paying customers:
[https://www.seldon.io](https://www.seldon.io)

I can’t speak for them specifically or any other AI company but from cursory
chats with folk in the industry suggests a lot of work is consultancy and
proof of concept type stuff with bigger companies which they use to partially
fund product dev on their own internal projects - some of which see the light
of day.

------
montenegrohugo
We use DL at my company. It's used to help and suggest the people in data
input what some of the inputs should be. It's been getting better over time as
it collects more data points.

Apart from that, I'm also personally responsible for implementing DL for data
anomaly detection.

So yes, ML/DL is very much over-hyped right now, but it has real world
tangible benefits. It is being used in multiple fields and I believe will grow
quite fast.

~~~
ChaitanyaSai
Hi, what services do you use for data annotation? We've been using DL for some
projects of ours and had to set up a data annotation team (remote) to get more
control over the labeling process. We are looking to see if others might be
interested in utilizing a service like ours, which will also help the people
we've acquired gainfully employed. At present we are mainly looking at taking
on small projects. If this is of interest, please do let me know! I posted
about this here, but hasn't surfaced enough to generate conversation
[https://news.ycombinator.com/item?id=17874184](https://news.ycombinator.com/item?id=17874184)

~~~
montenegrohugo
Hi Chaitanya,

That's quite an interesting project you have. Unfortunately probably not
really relevant to my use case since two skills are necessary:

1\. (good) control of the native language (not english) 2\. Understanding and
experience at the subject field

I don't want to give too many details but the workflow basically consists of
taking a poorly labeled entry and expanding/filling out the missing fields. To
do this you both need to speak the language and have some experience in the
field.

As for the data anomaly detection I don't do any data annotation

Anyways, sorry it's not really relevant to my case but good luck, I think it's
a great project that certainly has a lot of potential. Maybe in a future stage
you might be interested with partnering up with a platform like coursera to
form your workers to do slightly more complex data annotation jobs (like
mine).

Regards

~~~
ChaitanyaSai
Thanks for your response! Partnering with Coursera is an intriguing idea; will
look into that. Thank you

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anonu
I run a company that does financial portfolio optimizations and runs
regressions on financial time-series data. These tools are in some sense the
very first widespread use of "machine learning" techniques (at least in
finance). I've noticed the same effects that the OP mentions: a handful of
companies in this sector using the same tools may slap "ML and AI" all over
their product and marketing. They're not incorrect - it just feels like a bit
of a stretch to do so.

~~~
jihoon796
Sounds like your company is doing something similar to what my company was
doing before I left. You're right - we weren't technically wrong, but it did
feel disingenuous. I was never really sure if our customers actually derived
value from our product in the way we wanted them to, or if it was simply a
wildly speculative investment made by middle managers feeling FOMO after
reading the Wall Street Journal.

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yters
I was at an AI company at my last job. They had customers, but it was largely
speculative. I cannot say the product was worth what was being paid for it.
That being said, there is certainly a problem domain that AI could be good
for, if it ever lives up to the hype. Much more effective is human-in-the-loop
AI, which is what Google, Facebook, etc. use and make lots of money from.

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sandeepeecs
We are an AI company working out of Hyderabad / India, We have a very fast
Classification algorithm as a product. and currently been used by 3 paying
customers.

[http://www.alpes.ai/](http://www.alpes.ai/)

We are releasing our API next week. The idea being you can quickly try out the
algorithm and see Results and Training time on your own datasets.

------
laughingman2
I think legal contract analysis is an area where machine learning is actually
needed for core features to work.

Some products like
[https://www.contractstandards.com/](https://www.contractstandards.com/) are
beginning to appear. They need document classification, text sentence level
classification, entailment, clustering are all necessary for making contract
analysis easier for the user.

And these products need good domain experts to identify pain points where ml
can help.

In ml as API side, one potentially valuable product suite I have seen is
[https://aylien.com/text-api/](https://aylien.com/text-api/) . Even they are
building additional products like news analysis.

But I am little sceptical on whether ml as API can scale in terms of
addressing specific task-specific nuances needed for products building upon
them.

------
crunchlibrarian
I have seen some specialized image classifiers find a market in analyzing
scientific instrument data in academia, outside of that literally nothing
else.

But there sure are a lot of stories about adversarial bayesian multi-model
deep net retention markov cycle consistent custom asic demystified robust
perturbations.

The speech recognition and predictive text on my phone is still awful.

------
tzhenghao
My general rule of thumb is that any AI/ML is a result of it being a solution
to a pain point within existing product(s). Not having a product with a
necessary scale or require the technical sophistication of AI/ML but pitching
it as if it has it is imo a red flag. This I call premature optimization.

Another point is marketing. A ride sharing service like Uber or Lyft could
easily market themselves as "using AI/ML to solve last mile transportation".
There's reason to believe they have some ML teams that do amazing work to
support the ride sharing product. Both above have paying customers.

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malmsteen
A lot of compagnies do basically an externalisation of ml: they are providing
the team the compagny would build if they had to do the job themselves.

Usually their website is buzzwordy but they are not necearily too much vocal
because they find their customers through direct contact. And they do have
customers... theres a real market

In healthcare there are not ao much killer application yet in ai/ml and its
true that a lot are running on investor funds, but in my opinion this will
change soon.

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matt_the_bass
At my company we try to sell a product that solves a customers need. Some of
the things we’re doing as part of the solution we chose for the customers
product could be called AI. But honestly, our customers don’t really care
_how_ we solve their problem. They would be happy buying magic wands if that
would solve their problem.

So sibling comments mentioned that some companies struggle with selling
products not technologies. I agree.

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cm2012
Facebook and Google use ML as a core part of their products? and they're some
of the biggest companies in the world, so...

~~~
duskwuff
Right, but the ML they use is largely developed in-house. It's not part of a
prepackaged ML "product" provided by an outside group.

~~~
sgt101
My experience is that applying the ML to a domain is 99/100ths of the job;
once you've got some traction with well known methods it's time to develop
techniques that address their failings. But, any company that's done the hard
yards of getting something working is definitely adding value. A good example
is NLP, there's no rocket science in building embeddings, but any company that
can build a specialist embedding for an important domain (medicine,
engineering, finance) has an interesting and useful artefact that might be
worth paying for (probably not as much as they would like though).

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amplyfi
Customers (mostly businesses) are pretty much queueing to use our product,
Datavoyant by Amplyfi
([https://www.amplyfigroup.com/](https://www.amplyfigroup.com/)). We have real
paying customers. AI and ML techniques are great for both our product, as well
for our marketing.

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paulie_a
Yes they do, it's generally smoke and mirrors. But by being buzzword
complient, party like it is 1999. Ai/ml is a cute way to fleece clients for
99.999 percent of the time. Ml/ai from the consumer side is a joke.

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SystemOut
Uptake has real (paying) customers specifically for their ML based solutions.

~~~
chibg10
Caterpillar (who initially was their biggest client and went as far as to buy
a stake in the company) dumped them within the last year or two. Which doesn't
exactly reassure me that they're creating real value.

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ekianjo
Aktana has real big entreprise customers. They market themselves as AI while
their algorithms are certainly not matching up to the buzz.

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ackbar03
What kind of products were you're prior company offering before then? What
kind of clients did they have?

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jules
Some do, such as companies that produce software to help radiologists detect
cancer.

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w_t_payne
Some of us not only have real customers, but are profitable also...

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orasis
Most people that balk at the term A.I. seem to presume that intelligence is
some capacity unique to humans.

Intelligence is merely pattern matching and goal oriented planning, exactly
what machine learning is doing today.

Stop worrying and learn to love the bomb.

~~~
posedge
The term "Intelligence" does not have a definition everyone agrees on but it
surely is not "pattern matching". Machine learning today does little more than
pattern matching and it is far from intelligence at a human level.

~~~
andyjohnson0
> Machine learning today does little more than pattern matching and it is far
> from intelligence at a human level.

I'm rather less confident about this than you may be. When I'm doing something
that I could plausibly claim requires "intelligence", then it certainly
doesn't really feel like I'm just doing pattern matching. But I'm increasingly
of the view that our perception of our own mental processes is a poor guide to
their reality. The likelihood that my intelligence really _is_ just large
scale pattern matching, even though it doesn't subjectively feel like that,
seems quite high to me.

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dustingetz
I think the buzzword problem is rooted in sales mentality. Selling to VCs and
selling to customers. Humans have to make decisions with incomplete
information so we "pattern match" and "gut instinct" and "FOMO". So blame it
on capitalism for systemically ensuring that entrepreneurs behave like this,
or whatever, but frankly they are the only rational actors in this system.

