Since 2010
Building Software
Models That Survive Production.
Machine Learning Development Services
We build machine learning systems that make predictions your business acts on: demand forecasting, recommendation, risk scoring, and optimisation, with MLOps and monitoring so models stay accurate after launch. Fixed pricing, senior engineers.
- 15+
- Years Building Software
- 9,000+
- Projects Delivered
- 120+
- AI Engineers
- 25+
- Countries Served
Trusted by the brands we build for
9,000+
Projects Delivered
3,900+
Apps Published
500+
Engineers
revenue model
Where machine learning earns its keep
A model is only worth building when a decision repeats often enough and the data supports it. Forecasting demand, scoring risk, recommending products, detecting anomalies: these pay because they run constantly and the alternative is human guessing at scale. We start every engagement by checking the decision and the data, and we will tell you when rules or a spreadsheet beat a model.
The hard part is not the model; it is everything around it: data pipelines, retraining, monitoring, and integration into the systems where decisions happen. That production discipline is what our AI development services practice is built on.
What our machine learning development services cover
From data assessment to a monitored model in production.
Prediction and forecasting
Demand, churn, revenue, and capacity forecasts with uncertainty bands your planners can actually use.
- ✓Time-series pipelines
- ✓Uncertainty quantification
- ✓Backtesting against history
- ✓Planner-friendly outputs
Recommendation systems
Personalised ranking for products and content, evaluated on business metrics rather than offline scores alone.
- ✓Collaborative and content signals
- ✓Cold-start handling
- ✓A/B testing frameworks
- ✓Business-metric evaluation
Risk and fraud scoring
Classification systems with explainability and threshold tuning, because a score without reasons cannot be defended.
- ✓Gradient-boosted models
- ✓Feature importance and SHAP
- ✓Threshold tuning
- ✓Regulator-friendly reports
Anomaly detection
Monitoring systems that surface unusual patterns in transactions, operations, or infrastructure with alerting that respects on-call sanity.
- ✓Baseline modelling
- ✓Severity scoring
- ✓Alert routing
- ✓False-positive tuning
MLOps and model operations
Training pipelines, model registries, and deployment automation so models are retrained and shipped safely without heroics.
- ✓Automated training pipelines
- ✓Model versioning
- ✓Staged rollouts
- ✓Rollback procedures
Data engineering for ML
Feature stores, data quality checks, and label pipelines, because models inherit the quality of the data underneath.
- ✓Feature pipelines
- ✓Data quality gates
- ✓Label management
- ✓Lineage tracking
What every engagement includes
Every machine learning project ships with the following, agreed in writing before the first sprint begins.
Discovery workshop
A structured workshop maps your goals, users, and constraints into a scoped machine learning roadmap with a fixed written quote before any sprint starts.
Named senior team
You get named, senior engineers with CVs you can review, not a rotating bench. The people in the kickoff are the people who ship.
Sprint demos every two weeks
Working builds are demoed live each cycle with CI running on every merge, so progress is visible in software, not slideware.
100% source-code ownership
All machine learning source, configurations, designs, and documentation transfer to you on delivery with no licensing strings or vendor lock-in.
60 days of post-launch support
Engineering time is on standby for two months after go-live to triage defects, tune performance, and stabilise the release at no extra cost.
12 months of free updates
Compatibility updates, security patches, and platform upgrades ship free for a full year after handover.
What you can build with machine learning
Systems our clients run in production, each tied to a repeated decision.
Demand forecasting
SKU or service-level forecasts that drive purchasing, staffing, and inventory decisions with confidence intervals.
Churn prediction
Early-warning scores on customers at risk, routed to retention teams with reason codes attached.
Product recommendations
Personalised ranking across your catalogue, A/B tested against revenue and engagement rather than offline metrics.
Dynamic pricing support
Price and discount recommendations with constraint handling, reviewed by your commercial team before automation.
Predictive maintenance
Equipment failure early-warning from sensor and service history, prioritised by cost of failure.
How a machine learning Project Runs
Six stages, each with a sign-off gate before the next begins.
- 01
Discovery
We map your ML goals, users, and constraints in a structured workshop, ending in a fixed written quote and an architecture risk list.
✓ step 01 complete - 02
Solution design
The machine learning architecture, integrations, and delivery plan are designed and reviewed before build work starts, so scaling later is not a rewrite.
✓ step 02 complete - 03
Foundation sprint
Repositories, CI, environments, and a walking skeleton are set up first. Something real runs on a staging URL before feature sprints begin.
✓ step 03 complete - 04
Sprint delivery
Two-week sprints with live demos every cycle. machine learning work is reviewed, tested, and merged continuously, so scope surprises surface early.
✓ step 04 complete - 05
Hardening and launch
Performance, security, and accessibility checks run against agreed budgets, followed by a staged ML launch with monitoring from hour one.
✓ step 05 complete - 06
Support and handover
60 days of included support, 12 months of free updates, and documentation that lets your internal team own the system with confidence.
✓ step 06 complete
The stack behind our ML builds
Default choices, stated with reasons.
Modelling
Gradient-boosted trees as the strong default on tabular data, deep learning where signals demand it, and LLMs where text reasoning dominates.
Data platform
Pipelines with quality gates and lineage, orchestrated on tools your team can operate, with feature stores where reuse justifies them.
Serving
Real-time and batch inference with staged rollouts and shadow scoring, so new models prove themselves before taking traffic.
Monitoring
Prediction drift, data drift, and business metric tracking with retraining triggers, because every model decays without maintenance.
How intelligence flows through the system
What the model reads, and what it is allowed to change.
Feature pipeline
Prepares clean, current features from your data sources.
Reads
Transactional, behavioural, and reference data
Changes
Feature store records
Model server
Scores requests in real time or batch with versioned models.
Reads
Feature vectors and model registry
Changes
Prediction records and logs
Drift monitor
Watches prediction and data drift, triggering retraining.
Reads
Prediction logs, ground-truth outcomes
Changes
Retraining jobs and alerts
What's included, standard
The baseline on every machine learning engagement. Not an upsell tier, just the floor.
Fixed written quote
The price agreed at discovery is the price you pay, with change handled through explicit scope changes rather than silent drift.
Solution architecture document
System design, data flows, and technology decisions written down so your team can audit and own the result.
Dedicated project manager
One accountable point of contact with real authority over the delivery, not a message relay between time zones.
QA on every build
Automated and manual testing on each machine learning release candidate, with defects triaged before anything reaches production.
Security review
A checklist-based security pass covering authentication, data handling, and dependency vulnerabilities before launch.
CI/CD and environments
Pipelines, staging, and preview environments configured from week one, so releases are routine rather than dramatic.
Analytics and monitoring
Instrumentation wired before launch so you can see real user behaviour and system health from day one.
Documentation and handover
Code comments, runbooks, and architecture notes written for your next developer rather than for our convenience.
Feasibility and ROI baseline
A data-driven feasibility check with projected business impact before build is committed.
Case studies that speak volume
Etihad Airways
A high-traffic consumer platform our team ships and operates at national-carrier scale, the same bar every build is held to.
1M+
Monthly Users
40%
Engagement Lift
PragPlay
A consumer platform from the same engineering system, proving the infrastructure core holds under real-time load with strict uptime demands.
500K+
Players
99.9%
Uptime
Marvel Studios
A high-volume consumer experience our team ships at global-release scale, the same discipline applied to every launch.
10M+
Streams
65%
Completion
/11
Why teams pick us for machine learning
The differences live in engineering habits, not pitch decks.
Decision first, model second
We start from the decision the model serves and its measurable alternative, and we recommend against ML when simpler tools win.
Production or it did not happen
MLOps, monitoring, and retraining automation ship with the model, because an unmonitored model is a decaying asset.
Explainability baked in
Scores come with reasons and documented limitations, so risk, compliance, and operations teams can defend the outputs.
120+ AI engineers
Data engineering, modelling, and platform skills in one accountable team rather than stitched across vendors.
Track record since 2010
9,000+ projects and 3,900+ apps across 25+ countries, with production ML systems among them.
Machine learning development FAQs
$ moxace launch --nowReady to Put a Model in Production?
Talk to our ML team about a fixed-scope build with MLOps, monitoring, and full source-code ownership included.
- 01
Share your idea
a quick call, no deck required
- 02
See the live demo
every screen, admin to user
- 03
Launch with source
full code, your brand, your servers
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