Forecasting
- Sales forecasting
- Demand forecasting
- Revenue forecasting
- Inventory planning
- Workforce planning
Intelligent Automation
See the month before it happens.
Forecasting and scoring models built on your operational history, so planning, staffing, stock, and sales attention are based on what is likely to happen rather than what happened last time.
- Fixed-scope discovery before any build commitment
- Integrates with the systems you already run
- Human approval on the actions that matter
- Measured after launch, not just at handover
The business problem
Why organizations ask for this.
Most reporting explains the past. Decisions about stock, staffing, budget, and risk are made weeks ahead, and are usually based on averages, instinct, and last month's spreadsheet.
Capabilities
What is included.
Customer intelligence
- Customer churn prediction
- Lead scoring
- Customer segmentation
- Customer lifetime value
Risk and operations
- Fraud detection
- Anomaly detection
- Predictive maintenance
- Risk scoring
- Operational intelligence
Use cases
Where it earns its place.
Representative examples of how this service is applied. Your version starts from your own process, systems, and constraints.
- A retailer plans stock by location and season with less tied-up capital
- A subscription business identifies accounts likely to lapse while there is time to act
- A sales team prioritises the enquiries most likely to close
- A manufacturer schedules maintenance before failure rather than after
- A finance team flags unusual transactions for review
- A service business forecasts staffing against predicted demand
Integrations
Connects to what you already run.
Elevariq selects models, platforms, and infrastructure according to security, scalability, cost, integration, and ownership requirements.
- Salesforce, HubSpot, Zoho, Microsoft Dynamics
- SAP, Oracle, Odoo and custom ERP
- Microsoft 365 and Google Workspace
- Slack and Microsoft Teams
- ServiceNow and helpdesk tools
- Accounting and finance platforms
- HR and recruitment platforms
- Shopify and WooCommerce
- Data warehouses and BI tools
- Custom databases and internal APIs
Delivery approach
Six stages, defined deliverables.
- Stage 01
Discover
Document the current process end to end, including the exceptions people work around.
- Stage 02
Design
Model the target workflow, decision rules, approvals, and measurement plan.
- Stage 03
Build
Develop the automation, extraction logic, or model, and test against historical cases.
- Stage 04
Integrate
Connect source and destination systems with the right permissions and error handling.
- Stage 05
Launch
Run in parallel with the manual process until accuracy and coverage are proven.
- Stage 06
Optimize
Review exceptions, expand coverage, and retrain or re-tune on a schedule.
Controls & quality
Built to be trusted in production.
Security, permissions, and human oversight are designed in from the first release rather than added after something goes wrong.
Permission mapping
Automations inherit the access rules of the systems they touch.
Exception queues
Anything the workflow cannot confidently handle is routed to a person.
Approval checkpoints
Financial, legal, and customer-facing steps can require sign-off.
Full audit trail
Each run records inputs, decisions, outputs, and who approved what.
Confidence thresholds
Low-confidence extractions and predictions are flagged, not applied silently.
Rollback and re-run
Failed steps can be retried or reversed without corrupting records.
Questions
Predictive Analytics, answered.
How much data do we need?
Enough history to show a pattern, often 12 to 24 months for seasonal forecasting. We assess data readiness before proposing a model.
Who owns the model?
You do, along with the code, the training pipeline, and the documentation.
How do you prove it works?
By backtesting against periods the model has never seen and comparing it to your current planning method, with the results shared openly.
How does an engagement start?
With a strategy conversation about the problem, the systems already in place, and the outcome you need. If the scope is unclear, we run a short assessment first and come back with options.
Can we start small?
Yes. Most clients begin with one focused use case, prove it works, and expand from there rather than committing to a full programme up front.
NEXT STEP
Ready to talk about predictive analytics?
Tell us what is happening today, what it costs you, and what a better version would look like. We will come back with a practical next step.
Your first conversation focuses on the problem, feasibility, priorities, and potential business value.