The decision comes first
We start with the decision the prediction should improve, not the algorithm.
Machine learning that tells you what happens next
Backendz builds custom machine learning models on your own data to forecast demand, predict failures and flag risks early, then puts those predictions into the tools your team already uses.
Or see how we engage first — four shapes, no sales call required.
- Predictive maintenance
- Demand forecasting
- Workforce forecasting
- Capacity planning
- Churn prediction
- Risk scoring
- Anomaly detection
- Estimation models
- Time-series forecasting
- Classification
- Feature engineering
- Explainable predictions
- Model deployment
- Model monitoring
- Retraining pipelines
- Data pipelines
No more fixed-calendar maintenance that misses the failures in between
Stop planning shifts from last year's spreadsheet and a gut feeling
Forget finding out a customer was unhappy when they cancel
Done with years of data that only ever feeds a monthly report
Skip spotting unusual costs or readings weeks after they happened
Say bye to models that never left a data scientist's laptop
Why build machine learning & predictive analytics with us?
Beat today's method or don't ship
Every model is tested against how you decide now, on past periods it has never seen.
Explainable, not a black box
Your team sees which factors drive each prediction, so they can trust it or challenge it.
Production, not notebooks
We've deployed machine learning in live products, including estimation models in agriculture, and we build the software that uses it.
We buildMachine Learning & Predictive Analytics
How we engage
Four ways this usually starts
Most enquiries land in one of these. If yours does not, say so on the call — we would rather shape the engagement than sell you the nearest shape we have.
Most teams start here
You have the data and one big question
- One prediction tied to one decision
- Tested on past periods against your current method
- Clear view of the improvement before any build
Best for: Teams sitting on years of operational data
Equipment failures are costing you
- Predictive maintenance from service history and sensor data
- Risk ranking for every asset
- Visits planned around predicted failures
Best for: Facilities, field service and manufacturing
You need to plan ahead
- Forecasts for demand, workload, stock or staffing
- By site, team or product, with confidence ranges
- Delivered into your planning tools
Best for: Operations and workforce planning teams
Predictions inside your product
- Machine learning features built into your software
- Per-customer models or shared models with safeguards
- Monitoring and retraining as usage grows
Best for: SaaS companies adding predictive features
Case studies
Ideas that became systems in production
Every card is a build we shipped and still support. Open one for the decisions behind it.
Ops Copilot
- AI agents
- LLM
- Python
- +3
- +2
Tender Response Drafter
- AI
- LLM
- RAG
- +3
- +2
Policy & Contract Assistant
- AI
- RAG
- LLM
- +3
- +2
Pallet Counting
- AI
- Computer vision
- Python
- +4
- +3
Construction Site PPE & Safety Monitoring
- AI
- Computer vision
- Python
- +4
- +3
Crowd Density Monitoring
- AI
- Computer vision
- Python
- +4
- +3
Digital Security Planner
- React Native
- Node.js
- AWS Amplify
- +2
- +1
Virtual Estimator
- .NET
- Blazor WebAssembly
- Bootstrap
- +2
- +1
Joblogy
- Web app
- iOS
- Android
- +1
Pamacrafts
- WordPress
- WooCommerce
- PHP
- +4
- +3
RollBnB
- WordPress
- Listeo
- PHP
- +4
- +3
Venus Aesthetics
- React
- Tailwind CSS
- shadcn/ui
- +1
ShiftTracker
- Web platform
- iOS app
- Android app
- +4
- +3
Freight Dispatch
- Custom software
- Integrations
- Cloud
- +1
Invoice Reconciliation AI
- AI agents
- Document AI
- Finance
- +1
Chilled Traceability
- ERP
- Data platform
- Compliance
- +1
Steel Quoting Engine
- AI
- Pricing
- ERP
- +1
Factory Floor App
- Mobile
- Offline-first
- IoT
- +1
Fleet Driver App
- Mobile
- Telematics
- Platform
- +1
We serve the best in the industry
Scope
All of it, written down
What falls inside this service. Nothing here is an upsell discovered later.
What we predict
- Equipment failure
- Demand and workload
- Staffing needs
- Customer churn
- Contract and job risk
- Anomalies
- Values that are costly to measure directly
Getting data in
- Databases
- ERP and CRM exports
- Sensors and meters
- Spreadsheets
- Event logs
- Third-party data such as weather and calendars
Keeping it honest
- Held-back test periods
- Comparison with your current method
- Explainability reports
- Drift monitoring
- Scheduled retraining
Stack
- Python
- pandas
- scikit-learn
- XGBoost
- LightGBM
- PyTorch
- MLflow
- PostgreSQL
- SQL Server
- FastAPI
- Docker
- Azure
How it works
The sequence matters more than the label. Skipping the first one is how projects in this area go wrong.
We agree the decision to improve and how success will be measured.
Testimonials
The people who have to live with what we build
Operations directors, CTOs and plant managers — the ones still using the system a year after the invoice cleared.
5.0
Freight operator · 14 depotsFreight operator · 14 depotsWe wanted answers our team could check against the original document. Having the relevant clause beside the response makes the assistant useful when reviewing policies and contracts.
Our rental journey had to work for both vehicle owners and travellers. Backendz brought listings, availability and bookings together into a platform that feels straightforward to use.
A single headcount does not tell us where congestion is developing. The zone-based view gives our team a clearer picture of how people are moving and where attention may be needed.
Tender preparation involves gathering evidence and getting the right people to review each section. Backendz brought those steps into one workspace, with drafts our team can check and approve.
Our estimating process needed to reflect how we actually price work. Backendz took time to understand the inputs and rules, then built a quoting journey our team could review and refine.
Thomas MitchellManaging Director at Ridgewell Roofing & CladdingThe team took time to understand how our staff work between appointments. The resulting system brings scheduling and client information together without making the day more complicated.
We could review working features throughout the build and give specific feedback. That made it easier to keep the product focused on the people using it.
Our field team needed access to job details away from the office. The mobile workflow keeps the relevant information close at hand and gives the office a clearer view of progress.
We wanted a client portal that reflected how we deliver our services. Backendz brought documents, requests and progress updates into a straightforward experience for clients and staff.
Our teams were entering the same information into several tools. Backendz mapped the process with us and built a more connected workflow with clear review points.
We needed scheduling, availability and shift changes in one place. Backendz shaped the system around the way our operations team works and stayed involved after launch to help us refine it.
Ask for references and we will put you on a call with the team that runs the system — not a case study PDF.
References available on request
We wanted a practical way to review PPE and safety-zone events using our existing cameras. The team focused on the situations our site managers need to investigate and made the footage easier to review.
The priority was a counting workflow our warehouse team could use during a normal shift. Backendz connected image-based counts with the records we already use and made discrepancies easier to review.
Our information was spread across several operational systems. Backendz designed a clearer way to ask questions, inspect the supporting evidence and keep approval with our team before an action is taken.
The reconciliation workflow gives our finance team one place to review mismatches and supporting records. We can trace an exception back to its source and decide what needs attention.
We had outgrown the spreadsheets that held our pricing rules. Backendz helped turn that knowledge into a quoting tool we can maintain and explain to new colleagues.
Connecting our sales and inventory systems was the priority. The integration gives our team a clearer view of updates and a practical way to investigate records that need review.
The cloud work started with understanding our existing systems and dependencies. The team documented the setup and gave us a clearer basis for monitoring and ongoing maintenance.
The engineers worked alongside our team and explained their decisions clearly. Code reviews and practical documentation helped us stay involved throughout delivery.
Why Backendz
5 yrs
In production
Ops CopilotTender Response DrafterPolicy & Contract AssistantPallet CountingConstruction Site PPE & Safety MonitoringCrowd Density MonitoringDigital Security PlannerVirtual EstimatorJoblogyPamacraftsRollBnBVenus AestheticsShiftTrackerFreight DispatchInvoice Reconciliation AIChilled TraceabilitySteel Quoting EngineFactory Floor AppFleet Driver App
We quote fixed and we hold it
2
Platforms run in-house
Predictive maintenanceDemand forecastingWorkforce forecastingCapacity planningChurn predictionRisk scoringAnomaly detectionEstimation models
Cloud Strategy & ArchitectureCloud MigrationCustom ERP DevelopmentInfrastructure ModernisationCloud EngineeringERP ImplementationCloudOpsERP IntegrationDevOpsSupport & Managed Cloud ServicesERP Customisation & ModulesCloud Cost OptimisationERP Data MigrationCloud Security & ComplianceAI & Edge InfrastructureERP Support & OptimisationSaaS Stack AuditCustom CRM DevelopmentCustom HR & Workforce ManagementCustom Job & Field Service ManagementCustom Client Portals & BookingSaaS Data Migration & ExitAI Proof of Concept & MVP
Time-series forecastingClassificationFeature engineeringExplainable predictionsModel deploymentModel monitoringRetraining pipelinesData pipelines
AI Agent DevelopmentCustom AI Assistants & ChatbotsGenerative AIAI Workflow AutomationNatural language processingCustom Software DevelopmentComputer Vision DevelopmentWeb App DevelopmentMobile App DevelopmentAI Video Analytics for CCTVProduct DevelopmentMVP DevelopmentUX/UI DesignApplication ModernisationSoftware Testing & QAMachine Learning & Predictive AnalyticsERP & operationsAI Product DevelopmentData & cloudProduct & designAutomation & integrationsSupport & operations
45
Services, one team
Senior engineers, not account managers
24/7 production support and monitoring
Bring us the part of the business that still runs on workarounds
FAQ
The things people ask before they commit.
Still have questions?
Ask it on the call. We would rather answer an awkward question early than discover it in month three.
Book a callWhat is predictive analytics?
Predictive analytics uses machine learning models trained on your past data to forecast future outcomes, such as demand, equipment failures or customer churn. Reports show what already happened; a predictive model shows what is likely to happen, and how confident it is, so you can act early.
What is the difference between AI and machine learning?
Machine learning is a type of AI in which software learns patterns from data instead of following hand-written rules. Predictive models, computer vision and the language models behind chatbots all use machine learning. In business, it usually means custom models trained on your own data.
How much data do I need for machine learning?
It depends on the problem. Many forecasting and risk models work well with two or more years of consistent records. Rare events, such as specific equipment failures, need more history or extra data sources. Backendz assesses your data before recommending a build.
What is predictive maintenance?
Predictive maintenance uses machine learning to estimate when equipment is likely to fail, based on service history, usage and sensor data. Maintenance is then scheduled before a breakdown, instead of on a fixed calendar or after failure, reducing downtime and unnecessary visits.
How accurate are machine learning predictions?
Accuracy varies with the problem and data quality. The useful question is whether the model beats your current method. Backendz tests every model on past periods it hasn't seen and reports its accuracy against your existing approach before deployment.
Do machine learning models need ongoing maintenance?
Yes. Models lose accuracy as your business, customers and conditions change. Backendz monitors performance after launch and retrains when accuracy drops below an agreed level.