Practical AI solutions for real business problems
We build, train, and deploy machine learning models that fit your data, your team, and your budget. No hype, just results you can measure in weeks.
Get a free consultation See our services
We build, train, and deploy machine learning models that fit your data, your team, and your budget. No hype, just results you can measure in weeks.
Get a free consultation See our services
Dynamic AI Insights started in 2021 with a small group of machine learning engineers who were tired of watching businesses pay for AI projects that never made it past the prototype stage. We decided to build a consultancy focused entirely on production-ready models.
Our team now includes twelve specialists: five ML engineers, three data engineers, two NLP researchers, and two computer vision experts. Every project gets a dedicated lead who stays with you from the first data audit through post-deployment monitoring.
We work primarily with mid-size companies in retail, logistics, healthcare, and financial services across the UK and Europe. Most engagements run between eight and sixteen weeks. Some clients keep us on retainer for model retraining and drift detection after launch.
Our office sits in Scotland, but roughly half our team works remotely. We visit client sites when the project calls for it, and we hold weekly video stand-ups regardless of location.
Six core service areas, each backed by engineers who have shipped models into production environments.
We fine-tune large language models for customer support automation, document classification, and sentiment analysis. A typical support-ticket classifier reaches 92% accuracy within three weeks of training on your historical data.
From quality inspection on factory lines to shelf-stock monitoring in retail, our vision models detect defects, count objects, and read labels. We deploy on edge devices when latency matters, keeping inference under 40 milliseconds per frame.
Demand forecasting, churn prediction, pricing optimisation. We connect to your existing data warehouse, build time-series or gradient-boosted models, and deliver forecasts through a dashboard your operations team can actually use.
Getting a model into production is only half the job. We set up CI/CD pipelines for retraining, automated drift alerts, A/B testing infrastructure, and rollback procedures. Our standard stack uses Kubernetes, MLflow, and Prometheus.
Messy data kills AI projects faster than anything else. We build extraction pipelines, clean and label datasets, design feature stores, and set up data quality checks. If your data is scattered across five SaaS tools and a spreadsheet, we can fix that.
Not sure where AI fits in your business? We run a two-day assessment: audit your data assets, interview key stakeholders, and produce a prioritised roadmap with ROI estimates for each use case. No obligation to hire us for the build.
Four stages, clear milestones, no surprises on the invoice.
We spend the first week understanding your business problem and assessing the data you have. This includes schema reviews, volume checks, and a quick feasibility analysis. At the end you receive a written report with our recommendation on approach, timeline, and cost.
Over weeks two through four we build a working prototype on a sample of your data. You see real metrics: precision, recall, latency, throughput. If the numbers are not where they need to be, we adjust the architecture or feature set before moving on.
The model gets containerised, tested under load, and integrated with your existing systems via API or event stream. We write monitoring dashboards and set alert thresholds together with your ops team. This phase typically takes four to eight weeks.
We document everything: model cards, runbooks, retraining schedules. Your engineers get a hands-on walkthrough. Optional retainer packages cover ongoing retraining, feature additions, and performance reviews on a monthly or quarterly basis.
Most engagements fall between £15,000 and £80,000 depending on scope. A focused NLP classifier on clean data sits at the lower end. A multi-model pipeline with edge deployment and ongoing MLOps lands higher. We always quote a fixed price after the discovery phase.
No. About 40% of our clients have no in-house ML capability. We handle the full build and can train your developers to maintain the system after handover. If you do have data scientists, we collaborate with them directly.
Retail, logistics, healthcare, and financial services make up most of our portfolio. We have also completed projects in energy, agriculture, and legal tech. The common thread is structured or semi-structured data at a volume that justifies a model.
We sign NDAs and data processing agreements before any data transfer. For healthcare and finance projects we work inside your cloud environment so data never leaves your infrastructure. All team members hold current security clearances where required.
Yes. We regularly take over models built by other teams or internal staff. We start with a code and performance audit, identify the bottlenecks, and propose targeted improvements. Sometimes a better feature engineering step lifts accuracy more than a new architecture would.
Tell us about your project and we will reply within one working day.