Predictive analytics and forecasting
Your historical data already contains patterns that can reduce costs and improve planning. We extract those patterns with supervised learning models, typically gradient-boosted trees or neural networks depending on the data volume and feature complexity.
A typical engagement looks like this: we spend the first week auditing your data warehouse, identifying which tables matter, and flagging quality issues. Week two is feature engineering and baseline model training. By week four you have a dashboard showing live predictions alongside actuals, so you can gauge accuracy before committing to full integration.
- Demand and inventory forecasting for retail and logistics
- Customer churn scoring for subscription businesses
- Fraud detection models for payment processors
- Lead scoring pipelines that feed directly into your CRM
After deployment we monitor model drift weekly and retrain on a schedule that suits your data cadence, whether that is daily transaction feeds or monthly batch uploads.
Conversational AI and language models
We build chatbots and internal assistants that answer questions using your own knowledge base. Unlike a generic chatbot, ours are grounded in your documents: product manuals, policy PDFs, internal wikis, Confluence pages, or SharePoint sites.
The architecture uses retrieval-augmented generation (RAG). When a user asks a question, the system searches a vector index of your documents, retrieves the most relevant passages, and feeds them into a large language model to compose an answer. This means the bot cites sources and stays on topic rather than hallucinating.
We also set up guardrails: topic boundaries so the bot does not answer off-topic questions, profanity filters, and PII redaction if your documents contain sensitive data. The bot can run inside Microsoft Teams, Slack, a web widget, or a custom front end.
- Customer support bots that deflect 40-60% of tier-one tickets
- Internal knowledge assistants for HR policy and IT helpdesk
- Sales enablement tools that summarise RFP documents
Computer vision systems
If the problem involves images or video, we can probably automate it. We have built defect-detection systems for food packaging lines, vehicle damage classifiers for insurance claims, and document OCR pipelines that extract structured data from scanned invoices.
Our preferred stack is PyTorch with YOLO or Detectron2 for object detection, and EfficientNet or ConvNeXt for classification. For OCR we combine open-source layout parsers with fine-tuned text recognition models, which outperform generic cloud OCR on domain-specific documents like medical forms or shipping labels.
Deployment can be cloud-based (AWS, Azure, or GCP) or on-premises on NVIDIA Jetson devices for real-time edge inference. We provide a monitoring dashboard that tracks inference latency, confidence distributions, and flagged low-confidence predictions for human review.
- Quality inspection on manufacturing lines (sub-200ms per frame)
- Automated document data extraction and validation
- Retail shelf analytics: planogram compliance and stock counting
- Safety monitoring: PPE detection on construction sites
MLOps and model management
Already have models but struggling to keep them running? We offer standalone MLOps support. This covers CI/CD pipelines for model retraining, experiment tracking with MLflow or Weights & Biases, feature stores, model registries, and automated alerting when performance degrades.
One common engagement: a client had three data scientists who each trained models locally and deployed them via ad-hoc scripts. We set up a shared training pipeline on Kubernetes, a model registry with approval gates, and canary deployments so new model versions are tested on 5% of traffic before full rollout. Deployment time dropped from two days of manual work to a 20-minute automated pipeline.
- Automated retraining and deployment pipelines
- Model monitoring dashboards with drift detection
- Cost optimisation for GPU compute on cloud platforms
- Migration from notebooks to production-grade code
How every project runs
Regardless of which service you pick, the engagement follows these five stages.
Discovery
We interview stakeholders, audit data sources, and define success metrics. This takes one to two weeks.
Proof of concept
A working prototype on a subset of your data, delivered within three to four weeks.
Iteration
We refine the model with your feedback, improve accuracy, and handle edge cases.
Production deployment
Integration with your existing systems, load testing, and go-live.
Ongoing support
Monthly monitoring reports, retraining runs, and priority Slack or Teams access to our engineers.
Ready to start?
Describe your problem in a few sentences and we will tell you which service fits, how long it will take, and what it will cost.
Contact us