AI frameworks and agent build
The proofs of value that start most roadmaps
Spend and purchase-order anomalies, demand and cash forecasts, KPI automation, field-failure patterns: the use cases where the data is already in hand and the return is measurable in weeks. We build them as the early wins that earn the rest of the programme its budget.
Data in hand, value in weeks
Most ambitious forecasting ideas, demand planning across the business, inventory optimisation, commodity prediction, are gated by data that is not yet pooled. But a handful of related use cases are not: spend and purchase-order anomalies sit in the ERP already; KPIs that are compiled by hand each month come from data that already exists; field-failure records and factory test yields can be linked today. These are the data-in-hand proofs of value we recommend in the first quarter of every roadmap, because they show the business its own programme working while the foundations for the larger forecasting prizes are built.
Four patterns
Spend and transaction anomaly detection
Purchase orders, invoices and transactions screened for duplicates, off-contract spend, unusual amounts and timing, with each flag explained and routed to the owner.
KPI automation
OEE, cost per unit, throughput, margin, pipeline: compiled automatically from the systems of record instead of by hand each month, with the lineage visible.
Quality and failure patterns
Field failures linked to factory test data and production lots to surface the patterns root-cause analysis needs, where the records exist.
Forecasting where the data supports it
Demand, spend and cash forecasts built on pooled history, with promotion and seasonality effects, once the data audit shows the inputs are there; and an honest statement of what is not yet forecastable.
Four to six weeks, one question
Pick the question
One measurable question with data in hand: how much off-contract spend is there, which failures correlate with which test results, what does the KPI pack look like compiled automatically.
Audit and assemble the data
Confirm the sources, pull them, and document what is missing; the audit is itself a deliverable.
Model and validate
Classical statistics and machine learning where they fit, language models where documents are involved, validated against cases the business already knows the answer to.
Report and decide
The result, its confidence, the value found, and what it would take to run it continuously. The business decides whether to productionise.
Weeks, with a decision at the end
A proof of value runs four to six weeks and ends with a decision rather than a demo. Productionising adds integration, monitoring and a run model, scoped separately. Both are scoped and fixed before they start.
About forecasting and anomaly detection
Is this generative AI?
Mostly not. Forecasting and anomaly detection are classical machine learning and statistics, which return faster than language models for numerical problems. Language models come in where documents, descriptions or explanations are involved.
Our data is messy; can you still start?
That is the point of starting with data-in-hand questions. The audit tells you what the mess costs and what to fix first, and a proof of value on the clean part shows the return while you fix it.
What if the forecast is not good enough?
Then the proof of value says so, with the reason, which is usually a data gap, and the data-foundation work that would close it. A clear no is cheaper than a quiet failure in production.
Pick the question your data can already answer
There is almost always one. It is where the programme should start.