A glossary of what we do, how the pieces relate, and where to read more — designed to be readable by humans and citable by answer engines.
Each entry below names a capability we deliver, summarises it in two or three sentences, lists the topics it belongs to and the industries it shows up in, then links to the canonical page on itresearches.com. Together they form an entity graph: pick any node, follow the edges, and you reach every other capability in at most two hops.
The same data is exposed as structured data — a Schema.org DefinedTermSet, an ItemList of canonical URLs and an FAQPage — and as a machine-readable AI sitemap and entity-annotated XML.
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.