Not sure your data can actually support a model?
We'll tell you honestly before we build anything.
Most machine learning projects never make it to production, not because the math is hard, but because the data or the problem wasn't ready. We check that first, in plain English, then build for production if it makes sense.
Self-check
Signs a model might actually help
Most businesses that think they need machine learning don't, yet. If none of these sound familiar, start with a simpler tool, and we'll say so.
You have a hunch, not a plan
You suspect a model could predict, flag, or automate something in your business, but you don't know if the data supports it or where to start.
You're making the same judgment call by hand, repeatedly
Someone on your team eyeballs the same pattern in the data every week to make a decision, and it's slow, inconsistent, or doesn't scale.
You've been burned by "AI" before
A previous attempt, in-house or outsourced, never made it past a demo, and you're not sure why.
You need a straight answer before you spend real money
You'd rather find out now if this is realistic than discover it six months and one invoice later.
Once you know which one you need
What each path actually costs you
Every competitor page skips straight to "we build custom ML solutions." We start one step earlier: here's the real cost and tradeoff of each option, not just the one we sell.
Off-the-shelf AI tool
$20-$300/month · Days
Fast and cheap for common, well-solved problems, but it won't fit a use case specific to your data or business.
Data & AI Readiness Audit
$1,000-$2,000 · 1-2 weeks
Answers the question that actually matters first: do you have enough of the right data, and is this problem worth solving with a model at all.
Custom model build
Scoped after audit · 4-8+ weeks
Higher upfront cost, but built for your actual data and problem, with a plan for who owns and maintains it once it's live.
What happens next
People fear wasted spend more than the honest answer. Here is exactly what working with us looks like.
Scoping Call
Free
30-minute call
Tell us the decision or pattern you want a model to handle. We'll tell you honestly whether it's realistic, and roughly what it would take.
Data & AI Readiness Audit
$1,000 - $2,000
1-2 weeks
We assess whether your data and problem can actually support a model, and give you a written answer, and a fixed-price build quote if it makes sense.
Build & Ship to Production
Fixed price, per the quote
We build, validate, and deploy the model into your actual systems, not a notebook, and document who owns it and what happens if it drifts.
Wrong page?
This page covers building and shipping a new model. If you already have a model in production that's drifting, unreliable, or that no one currently owns and maintains, that's a Machine Learning Operations engagement, not a build. Mention it on the scoping call and we'll point you the right way.
Frequently asked questions
What data do we need to bring to a first call?
Nothing formal. A description of the decision or pattern you want the model to handle is enough. If you already have the data collected somewhere, even messily, that helps us gauge feasibility faster, but it's not required to start the conversation.
What if the model doesn't work?
That's a real possible outcome, and we'd rather find out during the readiness audit than after a full build. Most machine learning projects fail quietly, not because the math was wrong, but because the data or the problem wasn't ready. We check that first.
Do you handle deployment, or just modeling?
Both. A model that only works in a notebook isn't useful to your business. We build for production from the start, and hand over something your team can actually run, not a proof of concept that dies on someone's laptop.
What's the difference between this and Machine Learning Operations?
This page covers building and shipping a new model. If you already have a model in production that's drifting, unreliable, or that nobody currently owns, that's an operations problem, not a build problem, and a different engagement.
Why do most machine learning projects never reach production?
Industry research puts the figure as high as 87-90% of data science projects never shipping. The usual causes are a problem that was never well-scoped, data that wasn't actually sufficient, or no plan for who owns the model after the initial build. We address all three before writing any code.
How is pricing structured?
Almost every engagement starts with the Data & AI Readiness Audit ($1,000-$2,000), which tells you honestly whether a model is realistic for your data and problem. If it is, the build is scoped and quoted as a fixed price at $110/hr before any work starts.
87-90%
of data science projects reportedly never reach production. We check readiness first specifically to avoid becoming another one.
$110/hr
All work is billed at a single rate, scoped upfront into a fixed price. No surprises, no scope creep billed silently.
PhD
physicist-trained technical leadership on the team, not a generic "senior engineers" claim. Ask us who's doing the math.
A small, senior team, not a service catalog.
Most machine learning shops lead with scale: hundreds of engineers, dozens of industries, award badges. We lead with the opposite: a small senior team that tells you the truth about whether a model will actually work, then builds it for production, not a demo.
No risk to getting started
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The scoping call is free, and "your data isn't ready yet" is a real possible outcome
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The readiness audit tells you the truth before you commit to a full build
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Fixed pricing and a written scope before any build work begins
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We build for production and document who owns the model after handover
How we work
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Honesty. If your data or your problem isn't ready for a model yet, we tell you that on the call. We'd rather scope an audit than sell you a build that won't work.
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Clear process. You know what we are doing, why, and what it costs before we start.
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No overruns. Everything is scoped upfront. $110/hr, derived to a fixed price.
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We stay. We are not a project vendor. We are a partner who is still here after the model ships.