Computer vision, forecasting, language models and knowledge systems: clear definitions and the decisions each approach can support.
Computer vision interprets images; forecasting estimates future quantities; language processing extracts meaning from text. Knowledge systems organise concepts and evidence so that information can be retrieved and related. These capabilities can work together, but they answer different questions.
Choosing between them starts with the input, the decision and the cost of being wrong. A forecast needs a time horizon and a baseline; a classifier needs meaningful labels; a document assistant needs accessible sources and a way to refuse unsupported answers. More capable models do not remove these requirements.
| Input | Capability | Evaluation question |
|---|---|---|
| Images and video | Computer vision | Which defects or objects are missed? |
| Time-ordered sales and stock | Demand forecasting | Is error persistently too high or too low? |
| Reviews and support messages | Sentiment analysis | Does performance hold across languages and topics? |
| Documents and linked concepts | Knowledge systems | Does the cited evidence support the answer? |
Vendor-neutral advisory covering feasibility, architecture, model selection, governance and EU AI Act readiness. Output is a defensible roadmap, not a slide deck.
/artificial-intelligence-consultancyEnd-to-end build of production AI systems: model R&D, evaluation harness, MLOps, monitoring, integrations, runbooks and operator training.
/ai-software-solutions-servicesVision pipelines for defect detection, classification, segmentation, OCR and video analytics — deployed at the edge or in the cloud.
/image-processing-servicesForecasting and probabilistic modelling — hierarchical reconciliation, causal inference and scenario planning to drive operational decisions.
/predictive-analysisSKU × store × week forecasts reconciled across hierarchies, fused with promo, calendar and weather features for S&OP.
/demand-forecasting-planningPipeline modelling for revenue, capacity and territory planning — explainable and reconciled to bottom-up actuals.
/sales-forecasting-planningMultilingual sentiment with topic and intent tagging — across reviews, support tickets, social and survey free-text.
/sentiment-analysisStreaming and batch pipelines, lakehouse, feature stores, data quality and lineage — the substrate every production ML system rests on.
/big-data-processingDistributed training, evaluation harness, drift monitoring and retraining cadence — ML you can ship and keep shipping.
/machine-learning-on-big-dataOntologies, knowledge graphs and retrieval-augmented generation — domain copilots with audit trails and grounded answers.
/knowledge-systems-design-artificial-intelligence-systems-designSector-specific delivery patterns across manufacturing, retail, healthcare, financial services, logistics and the public sector.
/industriesThree ways to work with us: Feasibility Sprint (from £18k, 2–3 weeks), Production Engagement (from £85k, 8–16 weeks), Embedded AI Team (from £24k/month).
/engagementBig data processing is the substrate. It feeds machine learning on big data, which is the engine behind computer vision, predictive analysis (including demand and sales forecasting) and sentiment analysis.
Knowledge systems sit above NLP — ontologies and knowledge graphs combined with retrieval-augmented generation turn raw documents into grounded copilots. AI software development productionises any of the above; AI consultancy wraps feasibility, architecture and governance around it.
Each vertical playbook in Industries composes these capabilities differently: manufacturing leans on vision plus predictive maintenance; retail combines forecasting with sentiment; healthcare and the public sector lead with knowledge systems and audit. Proof points live in Case Studies; pricing and timelines in Engagement.
Tell us the problem, the data and the deadline. We'll point you at the shortest path to value.