Predictive and decision systems
See what’s coming. Decide with evidence.
We build forecasting, anomaly detection and decision-support systems from your operational data - designed to inform the people making decisions, not replace them.
Decision challenges
Most businesses have the data. Few have the signal.
- 01
Reactive planning
Stock, staffing and budgets are adjusted after problems appear rather than before.
- 02
Noise
Dashboards show many metrics but rarely highlight the change that actually matters.
- 03
Trust
A forecast without context or uncertainty is hard to act on - or to challenge.
- 04
Data quality
Gaps, inconsistencies and changing definitions undermine models unless they are handled deliberately.
Capabilities
From historical data to forward-looking decisions
Methods are chosen to fit the problem and the data - from statistical models to machine learning.
Forecasting
Project demand, volumes or workload with uncertainty ranges, at the granularity you plan at.
Anomaly detection
Flag unusual patterns in transactions, operations or performance as they emerge.
Operational insights
Summaries that explain what changed, where and the likely drivers.
Decision support
Recommendations with their rationale, so planners can accept, adjust or reject them.
Illustrative decision loop
From signal to decision
How a forecasting and anomaly system might support a weekly inventory decision.
Conceptual loop. Data sources and decision owners depend on your operation.
Flow
Illustrative01Collect and prepare data
Sales history, inventory levels and calendar events are cleaned and aligned.
warehouse · sales data
02Forecast demand
A model projects demand for the coming weeks with an uncertainty range.
03Detect anomalies
Recent sales well outside the expected range are flagged with likely drivers.
When: Planner approves or adjusts
04Recommend an action
The system proposes a reorder adjustment; a planner reviews and decides.
Data requirements
What a useful model needs
We assess these early. Where data falls short, we say so and suggest how to close the gap.
Relevant history
Enough historical records to capture seasonality and typical variation for the decision in question.
Consistent definitions
Stable meanings for key fields - what counts as a sale, a return or an active customer.
Context signals
Events such as promotions, holidays or price changes that explain movements in the data.
Feedback
Outcomes recorded after decisions are made, so forecasts and recommendations can be evaluated.
Implementation approach
How we would approach it
Every engagement is shaped by your systems and constraints. A typical path looks like this.
- 01
Frame the decision
Start from the decision to improve and how its outcome will be measured.
- 02
Assess the data
Profile data quality and coverage, and establish a simple baseline to beat.
- 03
Model and validate
Develop models and test them on held-out historical periods.
- 04
Embed and monitor
Deliver results where decisions are made, and watch for drift over time.
Have a business problem AI could solve?
Let’s understand the challenge and explore what a useful solution could look like.
Or email info@skarp.global