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We build AI-powered products, add AI capabilities to existing software and develop custom machine learning models using your data. Our work covers the full delivery cycle, from data assessment and prototyping to production deployment, monitoring and ongoing improvement.
We build machine learning pipelines, predictive models, NLP solutions and generative AI features designed to achieve measurable business outcomes.
We build models that turn your business data into decisions: demand forecasting, churn and risk prediction, anomaly detection and optimisation algorithms. Depending on the problem, we apply classical machine learning, deep learning or domain-specific modelling, and handle feature engineering, training, tuning and evaluation.
We add AI capabilities to existing software and MVPs, including chatbots, recommendation systems, semantic search, automated tagging and clustering. We can integrate GPT, Claude or open-source models based on your performance, privacy and cost requirements.
We build LLM-based tools for your own staff: question answering over internal documents, data extraction from contracts, invoices and forms, and agents that carry out multi-step tasks across your internal systems.
We prepare AI systems for reliable production use with automated deployment, real-time and batch prediction pipelines, model versioning, performance monitoring and retraining workflows. Solutions can run on AWS, Azure or Google Cloud and scale with changing demand.
We build the data foundation required for effective AI, including ETL and ELT pipelines, data warehouses, cleaning, labelling, transformation and enrichment. We also connect data from CRMs, ERPs, IoT systems and analytics platforms.
We add experienced machine learning engineers, Python developers, data scientists and MLOps specialists to your team. Engagements can be short or long term, with working hours arranged to overlap with your internal team.
Most engagements begin with one of these six situations.
You have a working digital product and want to introduce capabilities such as an AI assistant, recommendation engine or semantic search.
The work
You need a team to take a new AI application or SaaS product from initial concept to a production-ready release.
The work
You want to use historical business data to identify patterns, predict outcomes or support operational decisions.
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You do not have enough machine learning or MLOps capacity internally and need experienced specialists who can join quickly.
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You have a prototype or trained model but need the infrastructure, controls and engineering required to deploy it reliably.
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You are considering OpenAI, Anthropic or open-source models for a customer-facing feature or an internal workflow.
The work
Our AI project delivery lifecycle.
We define what the system needs to predict or produce, examine the available data and determine whether it is sufficient for the intended use case. This stage ends with a documented recommendation to proceed, adjust the scope or stop.
Deliverables: data profile, feasibility assessment and measurable success criteriaWe test a first model or LLM-based approach on a representative sample of your data. The people who will use the output review the prototype before the full build begins.
Typical tools: Jupyter and StreamlitBased on the prototype results, we train and tune a custom model or build the retrieval, prompting and integrations required for an LLM-based feature.
Typical tools: PyTorch, TensorFlow and Hugging FaceWe evaluate performance on data the model has not seen before. Together with your domain experts, we review incorrect or uncertain outputs and decide whether the system meets the release criteria.
Typical tools: MLflow and custom evaluation frameworksWe deploy the system as an API, application component or batch process within your cloud environment and connect it to your existing authentication, infrastructure and product workflows.
Typical tools: Docker, SageMaker, Vertex AI and Azure Machine LearningAfter launch, we monitor model quality, data drift, usage and operating costs. We retrain or adjust the system when performance declines or the underlying data changes.
Typical tools: monitoring dashboards, alerts and Kubeflow PipelinesA technically workable AI concept is not always a viable product. Before committing to full development, we examine the four factors that determine whether the system can produce reliable results and operate within your business constraints.
We define the decision, task or user experience the AI system should improve. Together, we establish success criteria that can be tested against the current process, rather than relying on a broad goal such as “adding AI.”
We review the volume, quality, structure and accessibility of your data. If important records are missing, inconsistent or restricted, we identify what must change before model development can begin.
We agree on representative test cases, acceptable error rates and the situations that require human review. This creates a practical basis for comparing models and deciding whether the system is ready for release.
We assess integration requirements, security controls, expected usage, response-time targets and infrastructure constraints. These findings shape the architecture, deployment model and expected operating costs.
Coding agents handle data-loading scripts, training code, API wrappers and tests, allowing us to evaluate more approaches within the same timeframe. A senior engineer reviews the results and decides which model ships based on its performance against held-out data.
AI-native product developmentAgents draft the feature code, prompts and training configurations for several approaches, so the prototype compares options instead of polishing the first one that looked promising.
Coding agents work against schemas and masked samples; production data, credentials and model registries are handled by our engineers under the access you grant.
Training, serving and evaluation code is read by a senior ML engineer before it merges, whether an agent or a person wrote it, because a quiet bug in scoring code can make a weak model look strong.
Each project uses a tailored technology stack based on its data, architecture and deployment requirements.
For forecasting, classification, anomaly detection and other predictive use cases, we start with the simplest model that can reliably meet the target.
We combine commercial and open-source models with retrieval, orchestration and specialised NLP tools. The choice depends on accuracy, latency, privacy and cost.
We build the pipelines that prepare, validate and move data from business systems into training and production workflows.
We deploy models in your existing cloud environment, with versioning, monitoring and automated release workflows built in.
We turn model outputs into tools your teams can use, from internal applications and operational dashboards to APIs embedded in existing products.
Personal data masked before training or prompting, and models, logs and training sets stored in your own cloud and region.
From sales intelligence to healthcare diagnostics, our teams build AI systems that solve real operational problems. These projects show two ways we work: delivering a complete product end to end or contributing specialised AI expertise within a client’s engineering team.
AI systemAI Sales Intelligence Platform
Our team designed and built this AI product end to end. It analyzes each sales conversation objectively using real call data, automates previously manual review and scales to handle changing data volumes.
Read the AI Sales Intelligence Platform case studyThe cost depends largely on the state of your data. Clean, labelled records from a single source make development faster, while fragmented or unlabelled data means preparation will account for a larger share of the budget. We provide a detailed estimate after the data audit and before any model training begins.
We set the timeline for a working prototype after the data audit, together with clear evaluation and release milestones. In practice, accessing and preparing the data often takes longer than training the model itself.
An existing LLM is often the fastest option for text processing, search and summarization. A custom model becomes worth the investment when performance must be validated against your own data or when that data cannot leave your infrastructure. During prototyping, we compare both approaches using real examples from your business.
We use Python with PyTorch, TensorFlow, scikit-learn and Keras for model development; OpenAI, Hugging Face, LangChain, Haystack, spaCy and NLTK for LLM and NLP applications; SageMaker, Vertex AI, Azure Machine Learning, Docker, MLflow and Kubeflow for deployment and MLOps; and Streamlit, Power BI, Looker or Dash for dashboards. We select the technology stack based on your data, infrastructure and cloud environment.
You retain full ownership. The training code, model files, prompts and evaluation results are stored in your repositories and cloud environment, with IP assignment included in the contract. We also sign an NDA before discussing your data in detail.
Its accuracy is watched, because it slips as customers, prices and behaviour drift away from the training data. Support covers drift checks, retraining when scores slip, and moving to a newer model or provider when one does the job better or for less.
Yes. Our engineers can work inside your team under your direction, or our team can deliver the project and hand it over to your engineers with its pipelines and documentation. Some clients switch between the two as the work grows.
During the initial consultation, we will review the business objective, intended use case and available data. Based on this information, our team will recommend a suitable technical approach, outline the first project milestone and prepare an initial estimate.
The proposed next steps will also specify the data, system access and input required from your team.
We sign an NDA before discussing your data in detail. We reply within 24 hours.
