Predictive analytics and forecasting
We build regression, time-series, and classification models that turn your historical data into forward-looking decisions. The output is always a deployed API or dashboard your team can query in real time, not a Jupyter notebook handed over on a USB stick.
What you get
- A data audit that maps your existing records, identifies gaps, and estimates model accuracy before training begins.
- Feature engineering tailored to your domain. For a logistics client, that meant encoding seasonal demand curves, fuel price indices, and driver availability patterns into a single feature set.
- Model selection across gradient-boosted trees, neural networks, and ensemble methods. We benchmark at least three approaches on your data and pick the one that balances accuracy with inference speed.
- A monitoring dashboard that tracks prediction drift and triggers retraining alerts when accuracy drops below a threshold you set.
Typical timeline
Four to six weeks from data handover to a production-ready endpoint. Retraining cadence depends on how fast your data changes; most clients retrain monthly or quarterly.
Natural language processing
From customer support chatbots to contract analysis pipelines, our NLP work covers the full stack: tokenisation, named entity recognition, sentiment scoring, intent classification, and response generation. We fine-tune open-weight language models on your proprietary corpus so the system understands your terminology, abbreviations, and tone.
What you get
- A domain-specific language model fine-tuned on your documents, emails, or transcripts. One legal client's model learned to distinguish between 47 clause types across commercial lease agreements with 94% accuracy.
- An entity extraction layer that pulls structured fields (dates, monetary values, party names, product codes) from unstructured text and writes them directly into your database or CRM.
- Conversational agents that handle tier-one support queries. We design the dialogue flows, train the intent classifier, and build fallback logic that routes complex questions to a human agent with full context attached.
- Evaluation reports with precision, recall, and F1 scores broken down by entity type and intent category, so you know exactly where the model is strong and where it needs more training data.
Typical timeline
Six to eight weeks for a fine-tuned model with a basic API. Conversational agents with multi-turn dialogue take eight to twelve weeks, including user testing.
Computer vision
We design image and video analysis systems that run on edge devices, on-premise servers, or cloud infrastructure. Applications range from quality inspection on manufacturing lines to document digitisation and aerial survey analysis.
What you get
- Object detection and classification models trained on your labelled images. If you don't have labels yet, our annotation team will create them using your quality standards as the reference.
- Image segmentation for pixel-level analysis. A food processing client uses our segmentation model to measure the colour distribution of baked goods in real time, flagging batches that fall outside specification before packaging.
- OCR and document parsing that converts scanned invoices, delivery notes, or engineering drawings into structured JSON. Accuracy on printed English text exceeds 99.2% in our benchmarks; handwritten text sits around 91%, depending on legibility.
- Edge deployment packages optimised for NVIDIA Jetson, Intel NUC, or Raspberry Pi hardware, so inference happens on the factory floor without sending images to the cloud.
Typical timeline
Five to seven weeks for a detection or classification model. Segmentation projects with custom annotation add two to three weeks for the labelling phase.
Data engineering and pipeline design
A model is only as good as the data feeding it. We build the plumbing that gets raw records from your source systems into a clean, versioned, queryable format that machine learning pipelines can consume reliably.
What you get
- ETL pipelines that extract data from APIs, databases, flat files, and streaming sources, then transform and load it into a data warehouse or lakehouse architecture.
- Data quality checks at every stage: schema validation, null-rate monitoring, distribution drift alerts, and deduplication logic. When something breaks upstream, you find out within minutes, not when a model starts producing nonsense.
- Version-controlled datasets using DVC or a similar tool, so every model training run is reproducible and auditable.
- Documentation that your own engineers can follow. We write runbooks, architecture diagrams, and troubleshooting guides as part of every engagement.
Typical timeline
Three to five weeks for a standard pipeline connecting two or three source systems. Larger data estates with legacy databases may take six to eight weeks.
AI strategy and readiness assessment
Not every problem needs a neural network. Sometimes a well-structured SQL query or a simple rules engine will do the job faster and cheaper. Our strategy service helps you figure out which problems are worth solving with machine learning and which are not.
What you get
- A two-day on-site workshop with your leadership and technical teams. We map your business processes, identify data sources, and score each potential use case on feasibility, expected impact, and estimated cost.
- A prioritised roadmap that ranks use cases by return on investment and implementation difficulty, with realistic timelines and resource estimates for each.
- A data maturity scorecard that grades your organisation on collection practices, storage infrastructure, governance policies, and team skills. The scorecard includes specific recommendations for closing gaps before starting model development.
- An executive summary written for non-technical stakeholders that explains what AI can and cannot do for your specific business, with concrete examples drawn from your own data.
Typical timeline
Two weeks from workshop to final deliverable.
How every engagement works
Regardless of which service you choose, every project follows the same four-phase structure. This keeps scope clear and gives you decision points before each phase begins.
Discovery
We meet your team, review your data sources, and define the problem in measurable terms. This phase takes three to five days and ends with a written scope document and fixed-fee quote.
Build
Our engineers write the pipeline, train the model, and iterate on accuracy. You get weekly progress updates with metrics, not vague status reports. If something is not working, we tell you early.
Deploy
We push the system into your production environment, whether that is AWS, Azure, GCP, or on-premise hardware. Load testing, security review, and integration testing happen here.
Monitor
For 30 days after deployment we watch prediction quality, latency, and error rates. If accuracy drifts, we retrain. After handoff, your team has full access to the monitoring tools and runbooks.
Have a project in mind?
Describe what you are trying to achieve and we will tell you which of these services fits, how long it will take, and what it will cost. No commitment required.
Talk to our team