Machine learning consulting for problems an LLM won’t solve.
Fraud scoring, demand forecasting, churn prediction, ranking, anomaly detection. These are not language problems, and reaching for a language model makes them slower and more expensive to solve.
- A model that scored well offline and disappoints in production.
- Predictions nobody in the business trusts enough to act on.
- Accuracy that decays month over month with no alarm attached.
- A rules engine that has grown past the point anyone can reason about.
What’s included
Problem framing
Turning a business question into a prediction target with a measurable definition of success.
Feature and label design
What the model gets to see, and how ground truth is defined — where most model quality is won or lost.
Model development
The simplest approach that clears the bar, benchmarked against a baseline worth beating.
Offline and online evaluation
Validation that reflects how the model will actually be used, not a favourable random split.
Serving and integration
Predictions delivered where the decision is made, at the latency the decision requires.
Drift monitoring
Alerting on input and performance drift, so decay is caught before it costs money.
How we work
- Map~1 week · fixed fee
AI Opportunity Sprint
Find where AI creates real value — and what it’s worth.
- Workshop with your team
- Prioritized use-case backlog
- ROI model for the top opportunities
- Prove3–4 weeks
Production PoC
Build one use case for real — and measure it.
- One use case built production-grade
- Evaluated against a real KPI
- Evidence-based go / no-go
- Ship6–12 weeks
Build & Integrate
Put the system into production — integrated and compliant.
- Full build, integrated with your stack
- Security & compliance built in
- Live, monitored, documented
- ScaleOngoing
Scale & Enable
Expand across workflows — and level up your team.
- Rollout across teams and workflows
- Evals & monitoring in place
- Team enablement + fractional AI leadership
When classical ML beats a language model
If the input is structured — transactions, events, telemetry, customer records — and the output is a number or a class, a gradient-boosted tree or a linear model will usually beat a language model on accuracy, cost, and latency at once. It will also be far easier to explain to a regulator.
Language models earn their place when the input is unstructured text, when the task needs world knowledge the training data does not contain, or when the output is prose. Fraud scoring on transaction history is none of those things. Reading a claim narrative and extracting the disputed amount is all three.
The expensive mistake is deciding by fashion rather than by problem shape. We have seen teams spend a quarter prompting their way toward a scoring task that a well-specified classifier solved in a fortnight, with a confusion matrix everyone could read.
Features and labels decide the outcome
Model choice matters far less than what the model gets to see and what it is taught to predict. Two failures cause most disappointing production results, and neither is visible in an accuracy score.
The first is leakage: a feature that quietly encodes the answer, usually because it is only populated after the event you are predicting. Offline accuracy looks excellent and production accuracy collapses. The second is label drift between training and serving — the definition of a flagged transaction shifting under you as an operations team changes how it works.
Both are caught by building the evaluation the way the model will actually be used: split by time rather than at random, reconstruct features as they stood at decision time, and check that every input would genuinely have been available at that moment.
Drift is the failure mode, not a rare event
A deployed model is a claim that tomorrow resembles yesterday. That claim weakens continuously — customer behaviour shifts, an upstream system changes a field, a competitor runs a promotion. Accuracy decays whether or not anyone is watching.
So monitoring belongs in the first release, not a later phase. Track the distribution of inputs, the distribution of predictions, and, where feedback arrives, live performance against ground truth. A model without drift alerting is a model whose failures your customers will discover first.
- Do we have enough data for machine learning?
- Often yes, and less than people expect for well-framed problems. The volume that matters is labelled examples of the thing you want to predict, not total data. Assessing that is part of the Opportunity Sprint.
- Can you work with our existing models?
- Yes. Reviewing and repairing a model that underperforms in production is common work — usually a leakage, labelling, or serving problem rather than a modelling one.
- How do you make predictions explainable?
- By preferring models that are inherently interpretable when the accuracy cost is small, and by adding feature attribution when it is not. In regulated settings the explanation is part of the deliverable.
- How long until something is running?
- A Production PoC is three to four weeks and ends with one use case built properly and measured against a real KPI, so the go or no-go decision rests on evidence.
- What if a simpler solution would do?
- We will say so. A well-tuned rules engine or a query sometimes beats a model on total cost of ownership, and that recommendation is a legitimate outcome of the Sprint.