Machine Learning Consulting.
Find out if you actually need ML before you spend a fortune building it.
Most companies that want machine learning do not need machine learning. They need a clean spreadsheet, a few rules, and a Monday meeting. We tell you which one you are, honestly, before anyone writes a line of training code.
The work starts with a hard conversation. Do you actually have enough data to train a model? Almost no SMB does. Is the prediction you want to make really a prediction, or is it a calculation? Most of the 'we need AI to predict churn' projects we get asked about are actually 'we need to look at the last 50 customers who left and write down what they had in common'. That is not a model. That is a spreadsheet and a Tuesday afternoon. We will tell you that, and we will save you the build cost.
The lie the consulting industry sells is 'every business needs a custom ML model'. Almost none do. The ones that genuinely do have one of three things: enough labelled data to train on (tens of thousands of examples, minimum), a prediction problem where the input is too complex for rules (image classification, document extraction, fraud detection at scale), and an output where being right 80 percent of the time is more valuable than being right 100 percent of the time by hand. If you do not have all three, the answer is rules, heuristics, or an off-the-shelf API call.
If you actually do need ML, the work runs in a strict order. We scope the problem, find or build the training data, pick the smallest model that could work (often a logistic regression or a gradient-boosted tree, rarely a deep neural network), and ship a working version into production behind a feature flag. We measure whether it actually moves the metric, and only then do we invest in scaling, monitoring, and the rest. Half the ML projects that get built never make it to production because nobody scoped them honestly at the start. We scope honestly.
- 20 yrsDoing this professionally, 40,000 hours of it
- 1,000+Entrepreneurs helped
- 1.6M+Views on 150+ how-to videos. Check the work before you buy
Industry benchmarks, not our client data
The gap ai & data has to close
- 4 hours A repeated weekly task nobody has looked at, still done by hand
- Under 1 hour The same task once it is automated, on the workflows where this works at all
Adoption among smaller businesses has roughly doubled in two years and still sits under 40 percent, against about 72 percent of large enterprises. The gap is not access to the tools. Everyone has the same models. It is that nobody has sat down and worked out which repeated task is worth automating first.
The honest scope: this works on well-defined, repetitive, text-and-data work, and it does not work on judgement calls or anything where being wrong once is expensive. You will see a 35 percent operating-cost reduction quoted around AI adoption. It is self-reported by the people who bought it, so treat it as a reason to run one pilot on one real workflow and measure it, not as a number for a business case.
Source: 2026 SMB AI adoption and automation reports (time savings and cost reductions are self-reported)
This is for you if
- Someone pitched you an ML project and you cannot tell if it is necessary or theater
- You have been told you 'need a model' without anyone naming what it predicts
- You have data and do not know if it is enough to train anything useful
- You want an honest 'you do not need this yet' answer before you spend on a build
What it costs
No proposal theatre. These are the real numbers, and the full list for every service is on the pricing page.
- $140£110/hr Standard Consulting Opportunity audit, data assessment, scoping, and the honest 'do you actually need this' conversation. Usually 5 to 15 hours up front.
- $190£150/hr Advanced Technical Prototype build, model training, deployment, and integration work. Most production builds run 40 to 200 hours depending on complexity.
Need a number for your situation?
The prices above cover the standard engagement. If yours is bigger, messier, or does not look like any of that, tell us where you are and what you are trying to fix and we will come back with a straight price and a timeline. No lock-in, no "discovery phases", no surprises.
What this looks like when it works
- You stop paying for ML projects that never ship, because the scoping conversation happens before the build
- The 80 percent of 'we need AI' projects that should have been rules and analytics get solved in weeks, not quarters
- The 20 percent that genuinely need ML get shipped to production with measurable accuracy and real business impact
- You stop paying enterprise vendors for 'AI features' you could have built in a weekend with an API
- Your team understands what ML can and cannot do, so the next pitch from a vendor lands with informed skepticism
How we approach machine learning consulting
The handful of principles that decide what we do and what we skip.
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Honest scoping over enthusiastic building
Most projects we audit get advised against. The ones we do build are the ones that actually pay back.
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Smallest model that could work
Logistic regression beats a transformer when the problem is small. We start at the small end and only escalate when the data demands it.
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Rules and APIs first
If 5 lines of IF statements or one OpenAI API call solves the problem, that is the answer. ML is the last option, not the first.
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Ship behind a flag
The first version runs on a fraction of traffic, with a kill switch. We earn the trust to expand it.
Questions owners ask before they commit
Everyone says I need AI/ML. Do I?
Probably not yet, and we will tell you straight. Most problems people want to throw ML at are solved cheaper with a rule, a spreadsheet, or an off-the-shelf tool. We find that out before you spend a fortune building a model.
Isn't it risky to build ML on my data?
It is risky to build it blind. We start with whether the data and the problem even justify ML, so you do not sink months into a model a simpler answer beats.
What if the answer is you don't need it?
Then you just saved a fortune, and that is a good outcome. We would rather tell you the truth up front than bill you for a build you did not need.
How we compare to the alternatives
The honest sales argument. We will not pretend the other options do not exist.
Doing it yourself
- You and your team learning every platform from scratch
- The work eats nights and weekends
- Hard to tell what is actually working until you have burned the budget
- One channel at a time because you cannot run them all yourself
- Pricey mistakes you eat alone
With Mujgos
- An operator who has actually shipped this work for paying clients
- Plain-English plan, plain-English reporting, no PDFs to interpret
- Fixed scope, real deadline. No 12-month contracts
- Decisions in 48 hours, not 4 weeks
- You keep the keys to every account, tool, and asset
Typical agency
- Junior staff in a process they cannot change
- Dashboards that confuse instead of clarify
- 12-month contracts and cancellation clauses
- Cookie-cutter playbooks they run on every client
- Padded retainers that grow quietly over time
How working with us actually goes
No 12-month contracts, no jargon, no lock-in. You pick what you need, we do the work, and you keep the keys.
- 01
Diagnose
A free 30-minute call. We figure out where you really are and what the next dollar of effort should go to.
- 02
Plan
We write the next 90 days with you. What to do first, what to skip, what to spend. So you stop guessing on Monday.
- 03
Build
We do the work. Fast and on a fixed price, not on hours billed.
- 04
Grow
Ongoing playbooks and a Slack thread or call when you're stuck. You run the business. We're the brain you call when something is off.
More inside AI & Data
- AI for Marketing Use AI for the marketing work that repeats. Skip the rest.
- AI Automation Automate the work that repeats. Keep the humans on the work that doesn't.
- Data & Analytics Five numbers in your inbox on Monday morning. Not a 12-tab dashboard nobody opens.
- Conversational AI Build a chatbot only when it earns its keep. Most don't.
Ready when you are.
Pick the offer that fits. We'll meet you there.