Business Questions First
We start from the decisions that matter — not from the data you have. Every model and dashboard is anchored to a measurable business outcome.
AI Analytics & Insights
Turn your data into decisions — before your competitors do.
NCompas builds AI analytics systems that surface insights automatically, predict outcomes accurately, and recommend actions clearly — across every business function.
3×faster strategic decisions at organisations using AI-augmented analytics (McKinsey Global Survey)
63%higher profitability reported by data-driven organisations vs. less data-mature peers (MIT Sloan)
$280Bprojected AI-powered analytics market by 2030 — the fastest-growing segment of enterprise software
95%of data and analytics leaders say AI is now critical to their analytics strategy (Gartner, 2024)
Why NCompas
Analytics that changes decisions, not just dashboards.
The ROI of analytics doesn't come from the dashboard — it comes from the decision the dashboard changes. We build for the decision, every time.
Governed from Day One
Data quality gates, lineage tracking, semantic layers, and role-based access baked into every analytics product — not retrofitted after audits.
Models Designed to Improve
Production retraining pipelines, model monitoring, drift detection. Your forecasting engine gets more accurate as your business generates more data.
Insight Embedded in Workflow
Analytics that live in Power BI, your ERP, your CRM, your ops tools — not in a separate portal nobody opens after week three.
Platform-Agnostic
Power BI, Fabric, Databricks, Snowflake, Tableau, Looker — we work in your existing stack, and help you right-size it if needed.
Adoption as a Deliverable
We don't ship dashboards — we ship adoption. Training, change management, and executive storytelling are part of every engagement.
The analytics journey
Most companies are stuck at descriptive. We get you to predictive — and beyond.
The gap between "what happened" and "what should we do" is where competitive advantage compounds. NCompas builds the AI layer that closes it.
What we build
Eight analytics capabilities — from raw data to revenue-changing insight.
Predictive Analytics anchors our practice. Everything else connects to give your business a complete picture of what's happening, why, and what to do next.
Predictive Analytics & Forecasting
Demand forecasting, churn prediction, revenue modelling, risk scoring, and supply chain optimisation — built on ML models trained on your historical data and retrained on production feedback. We don't sell generic forecasting platforms; we build models calibrated to your industry's seasonality, your customers' behaviour, and your operational constraints.
Average 22% improvement in forecast accuracy vs. legacy statistical modelsReal-Time Analytics & Streaming
Event-driven insight surfaces: live operational dashboards, anomaly alerts within seconds, streaming pipelines on Azure Event Hubs, Kafka, or Databricks Delta Live Tables.
Millisecond-to-minute latency vs. overnight batch reportsAI-Powered Business Intelligence
Power BI Copilot, natural language queries, auto-generated insight narratives, and AI that surfaces anomalies your analysts didn't know to look for. BI that reads the data so your team reads the decision.
Self-serve analytics adoption increases 3× with AI-assisted queryingCustomer Intelligence & Segmentation
AI-driven customer personas, behavioural cohort analysis, propensity-to-buy modelling, lifetime value prediction, and next-best-action recommendations wired into your CRM.
Average 34% improvement in campaign conversion ratesAnomaly Detection & Alerting
ML-based outlier detection for fraud signals, operational deviations, quality escapes, and financial irregularities — with configurable confidence thresholds and escalation paths.
Detects anomalies 8× faster than rule-based threshold monitoringPrescriptive Analytics & Optimisation
Recommendation engines, constraint-based optimisation, and scenario modelling that moves beyond "what will happen" to "here's the highest-value action to take right now."
Decision cycle from days to hours with AI-recommended actionsAnalytics Engineering
dbt models, semantic layers, data products, and metric stores that give every dashboard and model a single, governed, tested source of truth — so different teams stop arguing about whose numbers are right.
Single version of truth across all business unitsEmbedded & Operational Analytics
Analytics surfaced inside your ERP, CRM, supply chain, or ops platform — not behind a separate login. Insight at the moment of action, not after a Slack notification.
Decision latency cut by 70% when insights live inside the workflowHow we work
From business question to production insight in weeks.
Six steps designed to remove the most common failure mode of analytics projects: technically correct output that nobody trusts, uses, or acts on.
01
Discover
Map your key business decisions, data landscape, KPIs, and current analytics gaps. Output: a prioritised analytics roadmap tied to ROI.
02
Engineer
Build governed data models, semantic layers, and clean data products. No dashboard is more trusted than the data underneath it.
03
Model
Train, validate, and tune predictive and prescriptive ML models against your production data and business KPIs.
04
Visualise
Design insight surfaces — dashboards, alerts, embedded analytics — built for the specific decision-makers and workflows they serve.
05
Embed
Wire insights into existing tools, workflows, and notification systems. Analytics used where decisions happen, not in a separate portal.
06
Evolve
Monitor model accuracy, detect data drift, retrain on new data, and expand analytics scope — with evidence of what to prioritise next.
Every engagement delivers a working analytics product in weeks — not a strategy deck. We ship incrementally so you see value before committing to scale.
Results by industry
Analytics ROI isn't hypothetical — here's the evidence.
Four industries, four distinct analytics challenges, one consistent outcome: measurable business improvement in weeks, not quarters.
The Challenge
Reactive replenishment driving 14% overstock cost and 9% stockout-driven revenue loss across 8,000+ SKUs. Leadership making assortment decisions from 6-week-old data.
What We Built
ML demand forecasting engine trained on 3 years of sales, seasonality, promotions, and external demand signals. Real-time replenishment recommendations wired into the existing planning tool.
23%reduction in overstock holding cost
18%fewer stockout events in peak season
6 wks → 2 daysplanning data freshness improvement
Azure MLDatabricksPower BIdbtPython (Prophet + XGBoost)
Reactive replenishment driving 14% overstock cost and 9% stockout-driven revenue loss across 8,000+ SKUs. Leadership making assortment decisions from 6-week-old data.
23%reduction in overstock holding cost
18%fewer stockout events in peak season
6 wks → 2 daysplanning data freshness improvement
Azure MLDatabricksPower BIdbtPython (Prophet + XGBoost)
7.2% false-positive rate on fraud alerts burning analyst capacity. Rule-based system missing novel fraud patterns with $4.2M in undetected losses in the prior year.
94%fraud detection rate (up from 71%)
61%reduction in false-positive analyst workload
$3.8Min annual fraud loss recovered
Azure Event HubsDatabricks StreamingPythonAzure MLPower BI
23% avoidable 30-day readmission rate. Clinical teams had no early-warning signal — readmission risk was assessed manually, inconsistently, and only at discharge.
31%reduction in avoidable readmissions
140+clinical features scored per patient per day
4.1×ROI in year one (avoided penalty + LOS savings)
Azure Health Data ServicesPython (scikit-learn)FHIR APIPower BIAzure ML
6.8% defect rate on a high-volume production line costing $2.3M annually in scrap and rework. Quality inspections were manual, end-of-line, and caught defects too late.
28%reduction in defect rate over 6 months
40 minearlier defect signal vs. end-of-line inspection
$640Kannual scrap and rework savings
Azure IoT HubDatabricks Delta Live TablesPython (PyTorch)Power BIAzure ML
Platforms & tools we work in
PlatformMicrosoft Fabric
BIPower BI Copilot
PlatformAzure Synapse
PlatformDatabricks
WarehouseSnowflake
Engineeringdbt
MLAzure ML
MLPython / scikit-learn
ProcessingApache Spark
StreamingAzure Event Hubs
StorageDelta Lake
BITableau
The analytics gap is the next competitive moat.
3×
faster strategic decision-making at organisations using AI-augmented analytics vs. those relying on traditional BI alone.
22%
average improvement in forecast accuracy when moving from statistical to ML-based predictive models on production data.
$280B
projected AI analytics market by 2030 — growing faster than any other segment of enterprise software (Precedence Research).
5.5×
higher ROI on analytics investments at companies that embed insights directly into operational workflows vs. standalone dashboards.
Expert Insights for Smarter Digital Innovation
Insights from real-world engineering, cloud, and AI leaders - helping you make better decisions, faster.
Coming Soon
We're putting the finishing touches on this. Check back soon for in-depth insights.
Your data already holds the answers. Let's build the system that surfaces them.
Start with a free analytics readiness conversation. We'll identify your highest-ROI analytics opportunity, map your data landscape, and outline what a first production insight would look like — before you commit.