Service details
Predictive analytics and forecasting
We work with tabular data: sales records, sensor logs, CRM exports, financial transactions. Our process begins with a data audit where we check volume, completeness and labelling quality. If your data has gaps, we flag them before any modelling begins so you can decide whether to fill them or adjust the project scope.
Model types we commonly use include gradient-boosted trees (XGBoost, LightGBM), time-series models (Prophet, ARIMA variants) and, where the dataset justifies it, neural architectures like temporal fusion transformers. We always benchmark against a simple baseline so you can see the marginal value of the more complex approach.
- Demand and inventory forecasting for retail and distribution
- Customer churn prediction with actionable risk scores
- Pricing optimisation based on elasticity modelling
- Predictive maintenance for manufacturing equipment
Typical timeline from kickoff to a production-ready API: 6 to 10 weeks, depending on data readiness.
Natural language processing
Language is messy. Customers misspell things, use slang, switch between English and Irish mid-sentence. Off-the-shelf NLP tools often fail on domain-specific text because they were trained on Wikipedia and news articles, not on your internal documents.
We fine-tune transformer models (typically RoBERTa or DeBERTa-sized, not billion-parameter models that cost a fortune to host) on your labelled examples. If you don't have labelled data yet, we help you set up a labelling workflow and build an initial model from as few as 300 annotated samples, then improve it iteratively.
- Support ticket classification and routing
- Contract and legal document clause extraction
- Sentiment analysis on product reviews and survey responses
- Automated summarisation of long-form reports
We host models on your infrastructure or on a managed cloud endpoint. Inference latency for classification tasks is typically under 120 ms per request.
Computer vision
We deploy image and video analysis models for quality control, safety monitoring and asset management. Our preferred framework is PyTorch with Ultralytics YOLO for detection tasks and Segment Anything for complex segmentation needs.
Hardware matters here. If your use case requires real-time inference on a factory floor, we benchmark on NVIDIA Jetson or Intel NUC devices and optimise models with TensorRT. For batch processing (e.g., analysing photos uploaded overnight), a modest cloud GPU instance is usually cheaper and simpler.
- Defect detection on production lines (sub-millimetre accuracy with proper camera setup)
- Shelf and stock monitoring for retail chains
- Vehicle and property damage assessment for insurers
- Safety compliance monitoring on construction sites
We provide the camera specification guidance, not just the model. The best algorithm in the world won't help if the lens is wrong or the lighting creates reflections.
MLOps and model lifecycle management
A model that works in a notebook but crashes in production is worthless. We set up the infrastructure to train, version, deploy, monitor and retrain your models automatically.
Our standard stack uses MLflow for experiment tracking, Docker containers for deployment, and GitHub Actions or GitLab CI for continuous integration. Monitoring covers data drift (using Evidently AI), prediction latency, and accuracy degradation alerts sent to Slack or email.
If your team already uses Kubernetes, we integrate with your existing cluster. If not, we can deploy to a simpler setup with systemd services behind an Nginx reverse proxy. Overengineering the infrastructure is a common trap; we match the complexity to the actual scale of your workload.
AI strategy and feasibility consulting
Not sure whether AI is the right approach? We offer a standalone feasibility assessment. Over two to four days, we review your data assets, interview the people who would use the system, and produce a written report with one of three verdicts: go ahead, fix these data issues first, or don't bother with AI for this problem.
The report includes estimated development cost, expected accuracy range, infrastructure requirements and ongoing maintenance effort. We've talked several prospective clients out of AI projects that would have cost more than they saved, and pointed them toward simpler solutions instead. That honesty is why most of our new work comes from referrals.
How a typical project runs
We follow the same five-phase structure on every project. Timelines vary, but the sequence doesn't. Each phase has a clear deliverable, and you can stop at any phase boundary without penalty.
Data audit
We review your available datasets for volume, quality and relevance. You get a written data readiness score and a list of gaps to address. This takes 3 to 5 working days.
Prototype
We build a working model on a sample of your data and test it against a held-out set. You see real numbers, not projections. Usually complete within two weeks of receiving clean data.
Refinement
Based on prototype results, we tune hyperparameters, engineer features and expand the training set. This phase runs 2 to 4 weeks and often involves daily check-ins with your domain experts.
Deployment
We package the model as an API or batch job, integrate it with your systems and run parallel testing. Deployment typically takes 1 to 2 weeks including load testing.
Monitoring
Post-launch, we track accuracy and data drift weekly for the first three months. You receive a monthly performance report with recommendations for retraining if needed.