The problem with developing a machine learning model is that you don’t know how
well it’ll work until you try — and trying is very expensive. Obviously, this
risk is unappealing, but the existing solution in the market, one-size-fits-all
cloud services, are even worse. We’re launching Explosion AI to give you a
better option.
Update (March 2018)
In December we were pleased to release our first commercial product, our
annotation tool Prodigy. In just a few months, we’ve been
able to welcome hundreds of developers into our new community. Unfortunately,
this means we’re not currently available for new consulting projects.
However, we still believe that the best way to do AI is in-house –
one-size-fits-all solutions still don’t work, and we don’t see that changing.
For the last two years, we’ve been developing spaCy, one of
the leading NLP libraries. Matt
left academia to write spaCy in 2014. Ines has been working on it since
early 2015. Now dozens of companies are using spaCy in production, in
combination with the rest of Python’s
awesomeopen-sourceecosystem.
Most companies that use spaCy in production use it for machine learning. Machine
learning is now considered a “solved problem”. This is true, but only in a
fairly academic sense. If you get good at reading research papers, it’s not too
hard to find a suitable design. With enough experience, you’ll also be able to
implement the system fairly quickly. If you’re doing research, you’re done now.
If you’re not, you can now start working on your problem.
If you want your product to make good use of AI technologies, you’re probably
going to need your own training and evaluation data. You’ll also need to pick
the right system from the recent literature and adjust an implementation to suit
your requirements. The best AI products won’t be built from boxed solutions.
Instead, they’ll be built from custom software, and in particular, custom data.
However, building these assets in-house is expensive and extremely risky. Here’s
what we’re offering instead:
We want to build you custom statistical models. You come to us with a business
problem, and we’ll suggest a way to translate it into a machine learning
problem. We’ll also figure out whether you need new data assets, and quote you a
price. What we can’t do is quote you an accuracy figure, because performance is
too difficult to predict. Instead, we’ll give you a trial.
To make this a bit more concrete, here are three examples:
Let’s say you’re GitHub. You tell us about a planned feature that will flag
duplicate issues. We suggest a model that will take two pieces of text and
return a similarity score. You’ll be able to train it on all the public
issues users have flagged as duplicate, and then “fine-tune” it for
enterprise users. You give us some trial data to work with, along with a
deposit. Two months later, we ship you some binaries for onsite testing. If
the tests go well, we ship you the source and documentation.
Let’s say you’re a startup working on a chatbot that helps companies provide
better customer service. You tell us that your bot needs to detect whether
the customer is reporting a positive or a negative experience so it can reply
accordingly. We annotate some of your data and build you a model that returns
a sentiment score between 0 and 1. You test it with live users and find that
the behaviour does not work in practice — sarcasm proves particularly
problematic. You decide not to buy the solution.
Let’s say you’re MailChimp. You tell us that your users often forget to
update links in their campaigns and accidentally send their subscribers to
the wrong sites. We suggest a model that asks: is that page a likely target
for that link? You don’t have annotated examples, but you can’t give us your
users’ emails to annotate for you. We implement the classifier and an
annotation tool to efficiently train the system in-house. It works great, so
we ship you both the front-end and back-end source.
Commissioning software is usually very different from ordering other types of
goods and services. If you ask someone to tailor you a suit, they’re responsible
for delivering it according to the agreed specifications, and you’re responsible
for paying the agreed price. You’re also responsible for changes to your
requirements. If you need a different size or style from what you ordered, you
may be able to arrange an alteration — at your expense.
This sort of structure is a poor fit for most software projects, where taking
the measurements is harder than making the suit. But for AI projects, the
functional requirements are often very easy to describe, at least in informal
terms. We don’t need a water-tight specification. Instead, you’ll know project
success when you see it.
Commissioning a project from us works a little like commissioning a tattoo.
Imagine you want to immortalise your undying love for sloths, so you talk to an
artist and come up with a concept. The artist then draws you a design. If it’s
not something you can use “in production”, you don’t have to pay. But if you’ve
found the sloth of your dreams, it’s yours — forever.
We don’t know whether we’ll be able to take on your project — but we do know how
to find out. You don’t have to send us an email or book a phone call. Whether
you have an ambitious idea, or a tricky project planned, it’ll only take you a
few minutes to fill out our compatibility questionnaire.
Update (March 2018)
In December we were pleased to release our first commercial product, our
annotation tool Prodigy. In just a few months, we’ve been
able to welcome hundreds of developers into our new community. Unfortunately,
this means we’re not currently available for new consulting projects.
However, we still believe that the best way to do AI is in-house –
one-size-fits-all solutions still don’t work, and we don’t see that changing.
