AI & MACHINE LEARNING
Practical AI and Machine Learning
We design, train and operate AI and ML systems that actually run in production — forecasting, optimisation, document intelligence, NLP, recommendation engines and LLM-powered assistants.
Our focus is on models that improve decisions, automate manual work, and integrate cleanly into your existing tools, data and processes.
Example AI platform architecture
Data layer
Warehouse, lake, event streams
Feature & model layer
Feature stores · training pipelines · experiment tracking
Serving & integration
Real-time APIs · batch scoring · triggers into existing platforms
Where we apply machine learning and AI
We start from the outcome you need — more accurate plans, reduced manual work, faster decisions — then design ML and AI solutions that fit your data reality, regulatory constraints and operational environment.
Forecasting & optimisation
Volume and demand forecasting, capacity and staffing planning, inventory optimisation, pricing support and risk scoring tuned to your business rules.
- • Demand and volume forecasts with confidence bands
- • Capacity planning and what-if scenarios
- • Portfolio, risk and revenue projections
Document & language intelligence
NLP and LLM-powered tooling for reading, classifying and summarising documents, tickets, emails and forms at scale — with human review where needed.
- • Automated routing and triage of tickets and cases
- • Document extraction, validation and summarisation
- • Search and assistants grounded in your own data
Recommendation & decision support
Systems that surface next-best-actions, product recommendations and prioritised work queues, without taking humans out of the loop where they matter.
- • Next-best-offer and content recommendations
- • Priority scoring for operations teams
- • Risk and eligibility decision support
Computer vision & real-world signals
Practical image and video analysis for quality, safety and asset monitoring, combined with sensor and system data where appropriate.
- • Defect and anomaly detection on images
- • Asset and site inspection workflows
- • OCR pipelines for scanned documents
Outcome-first AI flow
MLOps that supports business operations
Our MLOps approach makes AI and ML feel like a natural part of your existing business. Instead of running as isolated experiments, models plug into the tools your teams already use, including your databases, workflows, dashboards and day to day processes. Predictions and automations flow reliably into operations, supported by monitoring and simple deployment steps, so your team can use AI confidently without needing to be experts.
Discovery & feasibility
We work with your teams to understand the business problem, what decisions AI should support, and whether your existing data and processes can realistically achieve the outcome.
- ✔Clarifying objectives and success criteria
- ✔Data and workflow assessment
- ✔Risks, constraints and governance
- ✔ Identifying quick wins and realistic delivery paths
Data & feature engineering
We prepare the data pipelines and features the model relies on, making sure they are stable, repeatable, and simple for your team to maintain long term.
- ✔Production-ready pipelines
- ✔Data quality and lineage tracking
- ✔Secure and governed access patterns
- ✔ Structured documentation aligned with your processes and compliance requirements
Model development & evaluation
We design, test and compare models, selecting the simplest reliable approach that delivers real business value and performs consistently in everyday use.
- ✔Structured experiments and comparisons
- ✔Evaluation focused on business impact
- ✔Transparency and explainability
- ✔Safe update process that won’t disrupt operations
Deployment, monitoring & handover
We deploy models into your environment with the alerts, dashboards and support processes needed so your team can operate AI confidently without specialist knowledge.
- ✔APIs, batch jobs and event triggers
- ✔Live monitoring and drift detection
- ✔Runbooks and team handover
- ✔Alerting when predictions fall outside expected ranges
Examples of how clients use AI with Isodev
Retail & e-commerce
Multi-location demand forecasting and replenishment suggestions that feed directly into existing planning tools.
- • Forecasts updated daily from live sales data
- • Stock and margin impact surfaced in dashboards
- • Fewer stock-outs and less write-off
Services & operations
Case and ticket routing using NLP, so the right team sees the right work first, with summaries and suggested responses.
- • Auto-categorisation and priority scoring
- • Suggested actions surfaced to human agents
- • Improved response times and consistency
Education & training providers
Early-warning models that predict student drop-out risk, supporting timely intervention and improved learner outcomes.
- • At-risk learners highlighted with clear contributing factors
- • Recommended actions surfaced to tutors and support teams
- • Improved retention and learner engagement