What we build
We focus on six core service areas. Each one can be delivered as a standalone project or combined into a larger programme. Scroll through to see what is included and how we typically scope the work.
Predictive analytics
We build models that forecast demand, predict customer churn, estimate claim costs or score leads. The process starts with an audit of your historical data: what fields are available, how far back the records go and how clean they are.
During the discovery sprint we test several algorithm families (gradient-boosted trees, neural networks, linear baselines) to find the best trade-off between accuracy and interpretability for your use case. The winning model is deployed as a REST API behind authentication, with monitoring dashboards that alert your team if prediction quality degrades.
- Data audit and feature engineering
- Model training, validation and bias testing
- API deployment on your cloud account
- Monitoring dashboard and drift alerts
Document intelligence
If your staff spend hours copying data from PDFs, scanned letters or emailed attachments into a database, this service is for you. We combine optical character recognition with layout-aware language models to extract structured fields from messy documents.
The pipeline handles multiple document types in a single workflow. New formats can be added by providing 50 to 100 annotated examples; we retrain the model and redeploy within a week. Output is delivered as JSON via webhook or directly into your ERP, CRM or data warehouse.
- OCR and layout analysis
- Fine-tuned extraction model per document type
- Confidence scoring and human-review queue for low-confidence fields
- Integration with SAP, Salesforce, or custom APIs
Conversational AI
We build chatbots and internal knowledge assistants that answer questions using your own documentation as a source of truth. The system retrieves relevant passages from your knowledge base, feeds them to a large language model and generates an answer grounded in your content.
Guardrails prevent the model from inventing facts or straying into topics outside its scope. Every answer includes a citation link so users can verify the source. We monitor answer quality weekly during the first three months and retune retrieval settings as your content evolves.
- Retrieval-augmented generation architecture
- Guardrail configuration and safety testing
- Widget for your website, Slack or Teams
- Weekly quality reports for the first 90 days
Computer vision
From quality inspection on production lines to counting vehicles in car parks, our computer vision models process images and video feeds in real time. We work with standard IP cameras and edge devices, so you rarely need new hardware.
Training requires labelled images. If you do not have them, we run a two-day annotation workshop with your domain experts using open-source labelling tools. Typical projects need between 500 and 2,000 annotated images to reach production-grade accuracy.
- Object detection, classification and segmentation
- Edge deployment on NVIDIA Jetson or similar devices
- Real-time alerting via email, SMS or webhook
- Annotation workshop and labelling guidelines
MLOps and model management
Already have models running in production? We help you manage them properly. Our MLOps service sets up automated retraining pipelines, version control for datasets and models, and alerting for data drift and performance degradation.
We use open-source tooling wherever possible (MLflow, DVC, Airflow) so you are not locked into a proprietary platform. If your team is new to MLOps, we run a two-day hands-on training session as part of the engagement.
- CI/CD pipelines for model training and deployment
- Data and model versioning
- Drift detection and automated retraining triggers
- Team training on MLOps tooling
AI strategy and feasibility assessment
Not sure where to start? This service is a structured two-week engagement where we assess your data assets, interview stakeholders and identify the two or three use cases most likely to deliver measurable ROI within six months.
The output is a written report with a prioritised roadmap, estimated costs and a candid assessment of risks. If we think a use case is not viable with your current data, we say so and suggest what you would need to collect first.
- Stakeholder interviews across departments
- Data quality and availability audit
- Prioritised use-case roadmap with cost estimates
- Executive summary presentation
How we price projects
We use two pricing models depending on the phase of work. Discovery sprints are always fixed-fee so you know the cost upfront. Build phases are time-and-materials with a cap you agree to before we start.
Discovery sprint
Fixed fee, two weeks
- Data audit and quality assessment
- Proof-of-concept model on your data
- Written feasibility report
- Go/no-go recommendation
- No obligation to proceed
Build and deploy
Capped time-and-materials
- Production model development
- API or pipeline deployment
- Integration with your systems
- Documentation and team training
- 90-day post-launch support
Ongoing support
Monthly retainer
- Model monitoring and drift alerts
- Quarterly retraining cycles
- Priority bug fixes
- Up to 8 hours of ad-hoc work per month
- Cancel with 30 days notice
Our process, step by step
Every project follows the same broad sequence, though the details vary depending on scope and complexity.
Initial call
A 30-minute video call where we learn about your business, your data and the problem you want to solve. Free, no commitment.
Discovery sprint
Two weeks of hands-on work with your data. We build a proof-of-concept model and deliver a written report with accuracy metrics, risks and a recommended next step.
Build phase
We develop the production system: model training, API deployment, integration with your existing tools. Weekly stand-ups keep you informed without eating your calendar.
Launch and support
We deploy to your cloud environment, run final acceptance tests with your team and begin the 90-day support window. Documentation and training are delivered before we step back.
Let us look at your data
The fastest way to find out whether AI can help is a conversation. Tell us what you are trying to achieve and we will give you an honest assessment.
Start a conversation