Service catalogue
Predictive modelling and forecasting
We train supervised-learning models on your historical data to predict future outcomes: weekly sales volumes, equipment failure dates, customer churn probability, or raw-material price movements. The work follows a fixed process: data audit, feature engineering, model selection (we benchmark at least three algorithms), validation on held-out data, and deployment to a production API or scheduled batch job.
Typical project length: six to ten weeks. You receive the trained model, source code, a Jupyter notebook documenting every experiment, and a monitoring script that alerts you when prediction accuracy drops below the agreed threshold.
- Demand and sales forecasting for retail and distribution
- Predictive maintenance for manufacturing equipment
- Churn scoring for subscription businesses
- Credit-risk classification for lending platforms
Natural language processing
Text is the most common unstructured data type in business, and also the hardest to work with at scale. We build NLP pipelines that classify documents, extract entities (names, dates, amounts, clauses), summarise long reports, and route customer messages to the right team.
Our standard stack uses fine-tuned transformer models running on your own infrastructure or a private cloud instance, so sensitive text never leaves your control. For smaller datasets we use few-shot learning techniques that need as few as 200 labelled examples to reach usable accuracy.
- Invoice and receipt data extraction (OCR + NLP)
- Contract clause detection and risk flagging
- Customer-support ticket classification and priority scoring
- Sentiment analysis on product reviews and survey responses
Computer vision
We train image-classification and object-detection models for quality inspection, inventory counting, and safety monitoring. A recent project for a packaging company involved a camera mounted above a conveyor belt: the model flags misaligned labels with 96.2% accuracy, replacing a manual check that previously required two full-time inspectors per shift.
We handle the full pipeline from image collection and annotation through model training (typically YOLO or EfficientNet architectures) to edge deployment on NVIDIA Jetson or similar hardware.
- Defect detection on production lines
- Shelf and warehouse inventory counting from camera feeds
- PPE compliance monitoring on construction sites
Process automation
Not every automation project needs a neural network. Sometimes a rules engine, an API integration, and a well-designed queue are enough. We assess each workflow and choose the simplest tool that meets the accuracy requirement: regex for structured text, a small classifier for semi-structured documents, or a large language model for open-ended queries.
We integrate with the tools you already use. Common connectors include Xero, QuickBooks, SharePoint, Slack, Zendesk, HubSpot and custom REST APIs. Average setup time is three to five weeks, including user-acceptance testing.
- Automated invoice matching and approval routing
- Email triage and auto-response for common queries
- Data migration and deduplication between CRM systems
AI strategy and workshops
If you are not sure where to start, this is the right entry point. We spend three days with your leadership and operations teams, map your data landscape, and identify the two or three use cases with the highest ratio of business impact to implementation effort.
The output is a written roadmap with cost estimates, data-readiness scores and a recommended sequence. We also run a half-day training session so your team understands the basics of how models are built, what data quality means in practice, and how to evaluate vendor claims.
- Three-day on-site or remote workshop
- Data-asset inventory and quality assessment
- Prioritised use-case backlog with estimated ROI
- Half-day AI literacy training for non-technical staff
How a typical project runs
Every engagement follows the same five-phase structure, though the length of each phase varies with complexity.
Discovery
We review your data, interview stakeholders, and define success metrics. This takes one to two weeks and ends with a signed scope document.
Data engineering
Cleaning, joining and transforming raw data into training-ready datasets. We document every transformation so results are reproducible.
Model development
We train and compare multiple algorithms, tune hyperparameters, and run bias checks. You receive a weekly progress report with accuracy metrics.
Deployment
The chosen model goes live as an API, a batch job, or an edge application. We handle infrastructure setup and load testing.
Monitoring
Six months of included monitoring. We track prediction drift, retrain on new data when needed, and provide a monthly performance summary.
Ready to discuss your project?
Describe what you are trying to achieve and we will reply within one working day with an honest assessment and a rough cost range.
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