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ML & PREDICTIVE ANALYTICS

Machine Learning & Predictive Analytics

Classic ML still powers the highest-ROI AI use cases. We build predictive models and put their insights straight into the dashboards your teams already use.

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ML & Predictive Analytics

Predict, detect and optimise

Building on our Power BI and Zoho Analytics practice, we add forecasting, scoring and anomaly detection models, then surface them as dashboards, alerts and agent tools. Models are versioned, monitored and retrained so they stay accurate.

What We Deliver

Capabilities included in ML & Predictive Analytics

Forecasting

Demand, sales, cash-flow, staffing and inventory forecasts with confidence ranges.

Anomaly & fraud detection

Spot unusual transactions, devices and behaviour in near real time.

Churn & propensity

Scores that tell sales and success teams who to call and what to offer.

Recommendation engines

Next-best-product and content recommendations for commerce and media.

Computer vision

Quality inspection, counting, safety monitoring, OCR and visual search.

AI-augmented BI

Natural-language Q&A, auto-insights and predictions inside Power BI and Zoho Analytics.

Technology & Platforms

Tools and models we work with

Pythonscikit-learnXGBoostPyTorchTensorFlowProphetOpenCVYOLOMLflowPower BIZoho AnalyticsDatabricksAzure MLSageMakerAirflow

How We Deliver

A proven path from idea to production

Frame the problem

Business question, target metric and baseline.

Prepare data

Collect, clean and engineer features.

Model

Train, validate and explain candidate models.

Deploy

APIs, batch scoring and BI integration.

Monitor

Drift detection, retraining and value tracking.

Use Cases

Where it creates business value

Retail demand planning

Reduce stock-outs and overstock with SKU-level forecasts.

Credit & risk scoring

Explainable scoring models for lending and collections.

Predictive maintenance

Predict failures before downtime from sensor and log data.

Visual quality control

Detect defects on production lines with cameras.

Revenue forecasting

Pipeline and cash-flow forecasts in management dashboards.

Customer analytics

Segmentation, lifetime value and churn drivers.

Commercials & Engagement Models

Flexible ways to start and scale

Pick the model that fits your stage and risk appetite. Every quote is tailored to your scope - and every cost driver is explained upfront.

FIXED SCOPE

AI Discovery Workshop

1-2 weeks. Use-case discovery, data and architecture review, ROI model and a costed roadmap.

FIXED PRICE

Proof of Concept / Pilot

3-6 weeks. A working solution on your real data with success metrics and a clear go / no-go.

MILESTONE

Fixed-Scope Project

Scoped build with milestone payments, defined deliverables and acceptance criteria.

MONTHLY

Dedicated AI Pod

A cross-functional team - AI engineer, data engineer, solution architect, QA - on a monthly retainer.

SLA

Managed AI Operations

LLMOps monitoring, evaluation, tuning, cost control and incident response under a monthly SLA.

PASS-THROUGH

Usage-Based Costs

LLM tokens, vector databases and GPU compute billed at cost, or run in your own cloud account.

PERFORMANCE

Outcome-Linked Options

For well-measured processes, part of the fee tied to agreed KPIs such as hours saved or cases resolved.

LICENCE

Product & Platform Licences

Subscription for our reusable orchestrator, RAG and MCP accelerators to cut build time.

What drives the price

Scope & number of use cases

Data readiness & quality

Number of integrations (ERP, CRM, APIs)

Model choice & token volume

Cloud, private cloud or on-prem GPUs

Security, privacy & compliance needs

Languages & channels (WhatsApp, voice, web)

Accuracy targets & human review level

Support SLA & hours of coverage

Training & change management

Request a Tailored AI Quote All Engagement Models

FAQ

Frequently asked questions

Do we need big data for ML?

No. Many valuable models work with modest, well-prepared data.

Can predictions appear in Power BI or Zoho?

Yes, as scored datasets, visuals and alerts inside your existing dashboards.

How do you keep models accurate?

Monitoring, drift alerts and scheduled retraining under MLOps.

Can you run computer vision at the edge?

Yes, on edge devices or servers depending on latency needs.