01
Feasibility assessment
Can this question be answered with this data? Days rather than months, and sometimes the answer is no, which is far cheaper to learn at the start than in month four.
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- Data Science Consultancy UK
United Kingdom
Data science & machine learning consultancy
Plenty of data science ends as a notebook nobody runs twice. We start with whether the data can actually support the question, and finish with a model deployed into the system that uses it, monitored for the day its accuracy quietly starts to decay.
Thanks — that’s with us.
A senior engineer reads every enquiry and will get back to you shortly.
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Book a time that suits you, or send a message and get an answer sooner.
- Feasibility assessed before anyone commits to a model
- Deployed into production, not delivered as a notebook
- UK-registered company, Companies House 16718403
2020
Building since
1,000+
Clients served
UK
Registered company
4.6/5
Rated on Clutch
Quick answer
What a data science consultancy should tell you first
Whether the question is answerable with the data you have. That assessment is cheap, it takes days rather than months, and it saves the much larger cost of discovering halfway through a project that the signal was never there.
The commonest blocker is not volume, it is labelling. Predicting churn needs a clear historical definition of what churn was, applied consistently. A surprising number of businesses cannot produce that, and no amount of modelling compensates for a target variable nobody agrees on.
The second is leakage: a field that quietly encodes the answer. A model that appears unreasonably accurate in testing is usually reading something that would not exist at prediction time, and it will fall apart in production. Finding that is routine work and it is why offline accuracy claims should be treated carefully.
Then deployment, which is where most value is lost. A model is only worth something when its output reaches the system where a decision is made, on a schedule, with monitoring for drift. We build that part, because it is the part that turns an analysis into something the business actually uses.
What we do
What the consultancy covers
Eight areas. Nearly every engagement starts with feasibility.
02
Forecasting
Demand, stock, cash flow, capacity and energy. Usually where classic statistical methods still beat anything fashionable, and we will say so rather than over-engineer it.
03
Classification and scoring
Churn, risk, lead quality, fraud and triage. The modelling is routine; the work is in the labels and in deciding what the business does at each threshold.
04
Data preparation
Pipelines, cleaning and feature work. Habitually underestimated, usually the majority of the effort, and the part that decides whether anything downstream is trustworthy.
05
Deployment
Getting the model into the system that uses it, on a schedule, with sensible behaviour when it cannot produce an answer. This is what separates a result from a capability.
06
Monitoring and drift
Models decay as the world moves. We monitor inputs and outputs so decay is detected by a system rather than by someone noticing the numbers look odd.
07
Reporting and analytics
Where the honest answer is that you need a clear report rather than a model. Frequently it is, and saying so has cost us work we did not want.
08
Models inside AI products
Classic models and language models in the same system, each doing what it is good at. Not everything needs an LLM, and classification rarely does.
Why projects fail
The notebook that nobody ran twice
Most failed data science we are shown was technically competent. It failed on labelling, leakage or deployment, not on algorithms.
What goes wrong
- A target variable nobody can define consistently
- Accuracy that collapses outside the test set
- A model that exists only in an analyst's notebook
- No monitoring, so decay is noticed months late
- Results nobody can act on because they arrive too late
What we do differently
- Labelling agreed before modelling starts
- Leakage actively hunted and documented
- Deployment into the system where the decision happens
- Drift monitoring from day one, not added later
- Output on a schedule that matches the decision
Not sure whether your data supports the question?
That is the first thing we check, and the conversation is free. It is better to find out in a week than in a quarter.
In practice
The questions we are asked
Six recurring problems. These describe the kind of work rather than named clients.
Who is about to leave
Useful only if the warning arrives with enough time to act and goes to someone who can act. The modelling is the easy half; defining churn consistently across five years of history is the hard half.
Warnings early enough to matter
How much to hold
Forecasting that accounts for seasonality, promotions and lead times, reaching the purchasing system rather than a monthly slide. Often beaten by simple methods, which we will tell you.
Stock decisions on a forecast, not a feeling
What the market will bear
Elasticity and segment-level pricing, built with guardrails so the output is a recommendation a human approves rather than an automatic change to a live price list.
Pricing evidence rather than instinct
Catching defects earlier
Classification over sensor, image or process data to flag problems before they reach a customer. Value depends heavily on what it costs to be wrong in each direction.
Problems caught before dispatch
Routing work automatically
Scoring incoming work so the urgent and the valuable reach the right people first. Usually a small model with a large operational effect.
Attention on the right cases
Who your customers actually are
Segments derived from behaviour rather than assumption, and made actionable by connecting to the systems that use them. A segmentation nobody uses is an expensive diagram.
Segments that reach the campaign
How we work
Feasibility, build, deploy, monitor
The first stage is deliberately short and sometimes ends the project, which is the point.
1. Feasibility
We look at the data and the question together, check whether consistent labels exist, and hunt for leakage. Days rather than months, and we will tell you if the answer is no.
2. Build
The simplest approach that could work, first. Often a statistical baseline is competitive and far cheaper to run, and finding that out early is worth more than a sophisticated model.
3. Deploy
Into the system where the decision happens, on a schedule that matches it, with defined behaviour when the model cannot produce an answer.
4. Monitor
Inputs and outputs watched for drift, so decay is caught by a system rather than by someone noticing the numbers look wrong three months later.
Clear scope · Regular demos · Full handover
Honest fit
When data science is worth commissioning
A good consultancy talks you out of some projects. These are the conditions that usually decide it.
A good fit when…
- You have consistent historical data covering the outcome
- Somebody can define the target variable precisely
- A decision would genuinely change based on the output
- There is a system the result can be delivered into
- You want it deployed rather than presented
Not the right fit when…
- The data does not record the outcome you want to predict
- The decision would be made the same way regardless
- You need an explanation of the past, which is a reporting job
- The process changes faster than a model could be retrained
Sectors
Who we work with
Where decisions repeat often enough for a model to pay for itself.
Retail and ecommerceDemand, pricing and churnFinance and fintechRisk, fraud and segmentationLogisticsCapacity, routing and forecastingManufacturingQuality, maintenance and yieldHealthcareTriage and capacity planningPropertyValuation and demand modellingSaaSChurn, expansion and usageEverything elseIf a decision repeats, a model may help
FAQ
Data science consultancy — FAQs
What UK businesses ask before commissioning data work.
Still have a question?
Ask us directly — a senior engineer will get back to you.
Ask about your projectHow much data do we need?
Less than most people expect, and the quantity matters less than the consistency. What usually blocks a project is not volume but labelling: whether your history records the outcome you want to predict, defined the same way throughout. A tidy three years frequently beats a messy ten.
How do we know if it is even possible?
That is what feasibility is for, and it takes days rather than months. We look at whether consistent labels exist, whether the signal is plausibly present, and whether anything leaks the answer. Sometimes the conclusion is that it is not possible, which is much cheaper to learn at the start.
What is data leakage?
A field that quietly encodes the answer, so the model looks excellent in testing and fails in production. A cancellation reason recorded only for cancelled accounts, for example. Hunting for it is routine work, and it is why we treat very high offline accuracy as a warning sign rather than a result.
Will you deliver a model or a report?
Whichever actually helps. A fair number of requests are better served by a clear report than by a model, and we will say so even though it is less work for us. Where a model is right, we deploy it rather than handing over a notebook.
Do you deploy into our systems?
Yes, and we would push to. A model that lives in an analyst's notebook delivers value once. Deployed into the system where the decision is made, on a schedule, with monitoring, it keeps delivering. That is the part that justifies the project.
What happens when the model gets worse?
It will, because the world moves. We monitor the input distribution and the output quality so drift is detected by a system rather than noticed by a person months later, and we agree in advance what triggers a retrain.
Is this different from the AI everyone is talking about?
Mostly yes. Language models are excellent with text and documents and are generally the wrong tool for forecasting a number or classifying a transaction. Classic machine learning remains better, cheaper and far more explainable for those, and we will recommend it where it fits.
Can you work with our in-house analysts?
Often the best arrangement. We tend to take the feasibility, deployment and monitoring, which is where consultancies add most, and leave the domain modelling with the people who understand your business.
What does it cost?
It depends on the size of the site and the scope of the work, so we do not publish a fixed price: the honest range is wide. The first conversation and the audit are free, and the quote follows them, in pounds, once we both know what the work actually is.
Who do we contract with?
SpiderHunts Technologies Ltd, registered at Companies House under number 16718403, with an office at 182-184 High Street N, Office 14607, London E6 2JA. You are invoiced in pounds. The engineering team works from Lahore, which we state openly rather than implying a room full of desks in London.
Start here
Tell us what you want to know
Describe the question and the data you hold. We will tell you honestly whether it is answerable, what it would take, and whether a model is the right answer at all. The first conversation is free and sometimes ends with us advising against the project.
- You tell us what you needTwo minutes on the form, or a message on WhatsApp.
- A senior engineer reviews itAnd comes back with questions, a realistic range and an honest view on fit.
- Free 30-minute scoping callWe talk through scope, options and a realistic estimate — with no obligation.
Thanks — that’s with us.
A senior engineer reads every enquiry and will get back to you shortly.
Want to take it further now?
Book a time that suits you, or send a message and get an answer sooner.