Nothing to beat
Without a baseline — last year’s figure, a moving average, the rule the team already uses — nobody can say whether the model adds anything or just adds cost.
Predictive analytics and machine learning
Predictive Analytics and Machine Learning Your Team Can Act On
Forecasts, propensity and risk scores, and anomaly alerts built from the data you already hold — tested against a simple baseline on past periods, explained in terms people understand, and delivered into the tools where the next decision is made.
- Every model tested against a simple baseline
- Reasons shown alongside every score
- A person decides what to do with it
Why predictive projects disappoint
A model is easy to train. A useful one is not.
Most failed prediction projects had a model that scored well in a notebook. What they lacked was a fair comparison, honest data and a clear owner for what happens after the score appears.
Where prediction earns its keep
The best candidates are repeated decisions, made on data you already collect, where acting a little earlier or a little more precisely is worth money:
- How much stock, staff or capacity to plan for next week
- Which customers are likely to leave, buy again or upgrade
- Which claims, orders or applications deserve a closer look
- Which transactions or readings look unlike the rest
- Where to send limited people and budget first
History that misleads
Definitions changed, a system was migrated, or a field is only filled in after the outcome is known. Train on that and the model looks brilliant until it meets next month.
Scores with no owner
A risk score lands in a spreadsheet nobody opens. If it does not reach the person who can act, at the moment they can act, the prediction changes nothing.
Models that go stale quietly
Prices change, a new product launches, customers behave differently. Accuracy slips month by month, and without monitoring nobody notices until the numbers are clearly wrong.
What we build
Forecasts, scores and alerts, built to be checked.
Every model ships with the baseline it was compared against, a backtest on periods it never saw, the reasons behind its outputs and monitoring that tells you when it starts to drift.
Demand and volume forecasting
Sales, orders, call volumes or cash, forecast by week, site or product with a range rather than a single number — so planners can see how uncertain next month really is.
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Churn and propensity scoring
The likelihood that each customer leaves, renews, buys again or responds to an offer, ranked so your team spends its time on the accounts where it matters most.
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Risk scoring with review
Scores that help prioritise claims, orders, applications or suppliers for checking. The model ranks and explains; a named person makes the decision.
AI for insurance
Anomaly detection
Flags for transactions, sensor readings, expenses or stock movements that look unlike the normal pattern, tuned so alerts are rare enough that people still read them.
Discuss this
Data preparation and features
Pipelines that pull history from your systems, fix the known quirks and build the inputs a model needs — reusing your reporting database where one already exists.
Data and BI services
Monitoring and retraining
Drift checks on inputs and outputs, accuracy tracked against what actually happened, and scheduled retraining with the old and new models compared before any switch.
DevOps and operations
How we work
How a predictive model gets into daily use.
The seven stages we use on every build, with the checks that matter for prediction: a decision to support, a baseline to beat, a backtest on unseen periods and monitoring once it is live.
- 01
Discovery
We start with the decision, not the data: who acts on the prediction, how far ahead they need it, and what they would do differently. Then we look at what history exists to support it.
A decision, a horizon and an owner
- 02
Strategy
We audit the data for gaps, changed definitions and fields that leak the answer, set a simple baseline, and agree the measure a model must beat to be worth running.
Data audit, baseline and success measure
- 03
UX & architecture
We design how the output reaches people — a score in the CRM, a weekly forecast, an alert — with the top reasons beside it and a clear route to override it.
Where and how predictions are shown
- 04
Development
Features and candidate models built in two-week sprints, from simple statistical methods up to gradient boosting, each one scored against the same baseline.
A shortlisted model with its evidence
- 05
Testing
A time-based backtest on periods the model never saw, checks on calibration and on performance for different customer groups, and a review of the cases it gets wrong.
A backtest report you can share
- 06
Launch
The model runs in shadow first, alongside the current way of working, so its predictions can be compared with real outcomes before anyone relies on them.
A monitored, shadow-tested release
- 07
Optimisation & support
Input and accuracy drift watched every week, retraining on a schedule or when drift is detected, and the model retired if a simpler method starts doing as well.
A model that stays honest over time
Technology
The tools we model with.
Mostly Python and well-established libraries. We choose the simplest method that beats the baseline, because it is cheaper to run and easier to explain.
Data and features
Python and SQLpandas and Polarsdbt modelsFeature pipelines
Modelling
scikit-learnXGBoost and LightGBMTime-series methodsPyTorch where it is needed
Explainability
SHAP valuesPer-prediction reason codesCalibration checksSegment-level error analysis
Serving
Scheduled batch scoringFastAPI endpointsScores written back to your CRMAlerts by email or Slack
Data platforms
PostgreSQLBigQuerySnowflakeAWS, Azure or Google Cloud
Operations
MLflow experiment trackingModel versioningDrift monitoringScheduled retraining
Not every question needs machine learning. If a moving average or a well-chosen rule does the job, we will show you the comparison and recommend the simpler option.
Use cases
Predictions UK teams act on every week.
Each one supports a decision someone already makes — earlier, with better evidence, and with a person still making the call.
Stock and demand planning
Forecast demand by product and location, with ranges that account for promotions and seasonality, so buyers order with the uncertainty in view.
Early churn warnings
Rank accounts by risk of cancelling, with the usage and support signals behind each score, so customer success calls the right people first.
Claims prioritisation
Score incoming claims for complexity or signs worth checking, so experienced handlers see the right files first. The model never declines a claim.
Volume and staffing forecasts
Predict parcel, order or call volumes by day and site, so shifts and vehicles are planned against a forecast rather than last year’s rota.
Unusual transaction alerts
Flag payments, expenses or journal entries that do not fit the usual pattern, for a person to review before month-end rather than after it.
Equipment anomaly alerts
Watch sensor and machine data for readings that drift from normal, so maintenance can be planned before a fault stops the line.
Relevant work
Products we have designed and built.
Dental.AI is built around a custom computer vision model we trained on clinical images and retrain as new data arrives — the same discipline of evaluation and monitoring a forecasting model needs. Modernize is a revenue analytics platform — not an ML project — built on the kind of multi-source data pipeline that forecasting depends on.
All case studiesWhy Techsleight
What working with us is actually like.
No inflated numbers — just how we run projects, and what you can hold us to.
Product engineering, not ticket-taking
We ask what the software is for before we estimate it — and we will tell you when something should not be built, or should be bought instead.
AI where it earns its place
LLM features, retrieval and automation built with evaluation, guardrails and cost controls, and plain software where that is the better answer.
Full-stack under one roof
Design, frontend, backend, mobile, cloud and QA in one team, so nothing falls between suppliers.
UK-focused delivery
UK business hours, estimates in pounds, and a contract with a UK company. Our engineers are based in the UK and India.
Flexible engagement
A fixed-scope project, dedicated developers or a monthly retainer — and you can move between them as the work changes.
You own everything
Code, IP, cloud accounts and documentation are yours from day one. We sign an NDA before discovery if you need one.
Support after launch
We stay on for fixes, upgrades and new features, or hand over cleanly to your in-house team with the documentation to match.
FAQs
Questions we get asked.
Straight answers on scope, cost, timelines and how we work. If yours is not here, ask us directly.
How much data do we need for a predictive model?
Enough history to include the patterns you want predicted, several times over. For a weekly forecast with seasonality, that usually means a couple of years; for a churn model, enough past cancellations to learn from. Quality matters as much as volume, so the data audit comes before any estimate of what a model can do.
How do you know a model is better than what we do now?
We compare it with a baseline — your current rule, last year’s figure or a simple average — on past periods the model never saw during training. If it does not beat the baseline by enough to justify the running cost, we say so and recommend the simpler option.
How accurate will the predictions be?
Nobody can say honestly before looking at your data. What we can do is measure it: the backtest shows how the model would have performed on real past periods, and forecasts come with ranges rather than a single number. We never present a prediction as more certain than it is.
Can you explain why a customer got a particular score?
Yes. Each score comes with its main contributing factors — for example a drop in usage or a run of support tickets — so the person acting on it can judge whether it makes sense. Where a decision has to be explainable to a customer or regulator, we choose models that make that practical.
What is model drift, and how do you deal with it?
Drift is when the world changes and the patterns a model learned stop holding — new products, new prices, different customer behaviour. We monitor the model’s inputs and its accuracy against real outcomes, retrain on a schedule or when drift appears, and compare the new model with the old one before switching.
Should we use machine learning or a simpler statistical method?
Whichever beats the baseline at the lowest running cost. For many forecasts a well-tuned statistical method is hard to beat; machine learning helps when many factors interact, such as churn or claims scoring. We try both and show you the comparison rather than defaulting to the more complex option.
Can predictive models be used in decisions about people?
Only as input to a person’s decision. Our models rank and explain; a named person decides on credit, claims, pricing or anything affecting an individual. Where personal data is involved, UK GDPR rules on automated decision-making may apply, and we give your DPO the technical detail they need to assess it.
How is this different from business intelligence and dashboards?
Business intelligence tells you what has happened, from agreed numbers. Predictive analytics estimates what is likely to happen next, with a measured level of uncertainty. They share the same data foundations, and our business intelligence service covers the reporting side in detail.
What does a predictive analytics project cost?
It starts with a discovery sprint, from £2,000, covering the decision, the data audit and a baseline. The build is then quoted as a fixed-price project. Running costs — cloud compute and any data platform — are usually modest for batch scoring, and we estimate them before you commit.
Who looks after the model once it is live?
We can keep monitoring and retraining it on a support retainer, or hand it to your team with the training code, data pipeline, backtest reports and a runbook explaining how to retrain, compare and roll back a model.
Start a project
Which decision would you like to make earlier?
Tell us the decision and the data behind it. We will give you an honest view on whether a model can beat what you do now, and how we would test that before you build anything.
What happens next
- 1A senior engineer reads your brief within one working day, and replies with questions or a first view.
- 2A 30-minute call to understand the goal, constraints and what good looks like — no sales script.
- 3A written proposal with scope, milestones, team and a GBP estimate you can take to your board.
Techsleight Labs is a trading name of Krapton IT Consultancy.
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