We build, train, and deploy machine-learning systems that save your team hours every week. No hype, no jargon: just measurable results you can see in your next quarterly report.
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Founded in Scotland, Integrity AI Hub grew out of a simple observation: most businesses know they should use AI, but few know where to start. Our founding team spent years inside enterprise data science departments before deciding to bring that expertise directly to growing companies.
We specialise in natural language processing, computer vision, and predictive analytics. Each project begins with a thorough audit of your existing data, because the best algorithm in the world is useless without clean, well-structured inputs.
Our clients range from e-commerce retailers who need demand forecasting to healthcare providers automating patient triage notes. The common thread is this: every engagement produces a working prototype within four weeks, not four months.
Transparency matters to us. You own your models, your data stays on your infrastructure, and we document every decision so your internal team can maintain the system long after our contract ends.
Six core service areas, each backed by repeatable frameworks we have refined across dozens of deployments.
Chatbots that actually understand context, sentiment analysis dashboards for customer feedback, and automated document summarisation. We fine-tune large language models on your proprietary corpus so responses match your brand voice.
Quality-control cameras on production lines, shelf-stock detection in retail, and medical image classification. Our models run on edge devices when latency matters, or in the cloud when you need to process thousands of images per minute.
Demand forecasting, churn prediction, and dynamic pricing engines. We connect directly to your ERP or CRM, train gradient-boosted models on historical transactions, and deliver probability scores your sales team can act on the same day.
Before any model can learn, your data pipelines need to be reliable. We design ETL workflows, set up data lakes on AWS or Azure, and build monitoring alerts that flag anomalies before they corrupt downstream models.
Bias audits, model explainability reports, and GDPR-aligned data handling procedures. If your sector is regulated, we produce the documentation auditors expect, including model cards and fairness metrics broken down by protected characteristics.
Deploying a model is only half the job. We set up CI/CD pipelines for retraining, A/B testing infrastructure, and drift-detection dashboards so your predictions stay accurate as your business data evolves over months and years.
A four-phase engagement model designed to reduce risk and deliver value early.
We spend one to two weeks reviewing your data sources, interviewing stakeholders, and mapping the business KPIs that AI should move. The deliverable is a prioritised roadmap with effort estimates for each opportunity.
Within four weeks we build a minimum viable model using a representative sample of your data. You see real outputs, test edge cases, and give feedback before we invest in production-grade engineering.
We containerise the model, integrate it with your existing systems via REST API or event streams, and run load tests. Monitoring dashboards go live on day one so your ops team has full visibility.
Monthly performance reviews, quarterly retraining cycles, and a dedicated Slack channel for ad-hoc questions. If accuracy drops below the agreed threshold, we investigate and retrain within 48 hours.
Honest answers to the questions we hear most often.
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United Kingdom, Scotland, Castle Roberts, YJ37 4JD, 37 Wiza Court
Monday to Friday, 9:00 am – 5:30 pm GMT