Your business deserves smarter AI
We build machine learning systems, language models, and computer vision pipelines that solve real problems. No buzzwords, no bloated timelines. You get working software that fits your data and your team.
We build machine learning systems, language models, and computer vision pipelines that solve real problems. No buzzwords, no bloated timelines. You get working software that fits your data and your team.
AI Leapfrogs started in 2019 when two machine learning researchers grew tired of watching promising AI projects stall inside large consultancies. The original idea was simple: pair every client with engineers who actually build models, not slide decks.
Today we are twelve people, mostly PhDs and senior engineers, spread across Scotland and working with clients in retail, healthcare, logistics, and financial services. We have shipped 47 production systems since launch. Fourteen of those handle more than a million predictions per day.
We work best with companies that already have data but lack the internal expertise to turn it into something useful. If you have messy CSVs, unstructured text dumps, or image archives you suspect contain valuable patterns, that is exactly where we start.
Each engagement starts with a two-week discovery sprint. We audit your data, define success metrics, and deliver a technical roadmap before writing any model code.
We train supervised and unsupervised models for classification, regression, clustering, and anomaly detection. Most projects go from raw data to a validated prototype in four to six weeks. We use Python, PyTorch, and scikit-learn, and we deploy on your existing cloud infrastructure so your ops team can maintain the system after handoff.
Chatbots, document classifiers, sentiment analysis, entity extraction, and summarisation. We fine-tune large language models on your domain-specific corpora so the output actually reflects your terminology and your customers' vocabulary. Response accuracy typically sits above 92% after the first tuning cycle.
Object detection, image segmentation, OCR, and quality inspection systems. We have built defect-detection pipelines for food packaging lines that reduced false rejects by 38%, and a retail shelf-monitoring tool that scans 12,000 SKU facings per hour using standard CCTV cameras.
Demand forecasting, churn prediction, lifetime value estimation, and risk scoring. We connect to your data warehouse, build feature stores, and set up automated retraining schedules. One logistics client cut excess inventory costs by 22% in the first quarter after deployment.
Bias testing, explainability reports, model cards, and compliance documentation for regulated industries. If you operate under FCA, GDPR, or NHS data standards, we produce the audit trail your compliance team needs. We run fairness checks across protected characteristics and document every training decision.
CI/CD pipelines for model training, containerised inference endpoints, monitoring dashboards, and drift detection. We set up everything in Kubernetes, AWS SageMaker, or Azure ML, depending on what you already run. Average deployment time from final model to live endpoint: three days.
Four phases, clear milestones, no surprises on the invoice.
We spend two weeks inside your data. Interviews with stakeholders, exploratory analysis, and a feasibility report with estimated accuracy ranges.
A working model on a representative sample. You see real predictions within four weeks and decide whether to proceed to production.
Full-scale training, integration with your systems, load testing, and deployment. We write the API contracts and the monitoring hooks.
Monthly retraining runs, performance reviews, and on-call support. We track data drift and retrain before accuracy degrades.
It depends on the task. For tabular classification, a few thousand labelled rows is often enough to build a useful prototype. Image models typically need 500 to 2,000 annotated images per class. During discovery we assess what you have and tell you honestly whether it is sufficient or whether we need to augment or collect more.
Yes. About half our clients are elsewhere in the UK, and we have two ongoing projects with firms in the Netherlands and Germany. All collaboration happens over video calls and shared repositories. We visit on-site for the discovery phase when the project warrants it.
Discovery sprints start at £8,000. A full prototype-to-production cycle for a single model usually falls between £25,000 and £70,000 depending on data complexity and integration requirements. We quote fixed prices after discovery so there are no open-ended billing surprises.
Not unless you want it to. We can work entirely within your VPC, your on-premise servers, or a dedicated cloud tenant. For healthcare and financial clients we routinely sign BAAs and operate under strict data-handling agreements. Your data stays yours.
You get a working prototype with measurable accuracy metrics within six weeks of project kick-off. Production deployment typically follows four to eight weeks later. The timeline stretches if data quality issues surface during discovery, but we flag those early.
Tell us about your data and your goals. We reply within one business day.
Monday to Friday: 9:00 am to 5:30 pm
Saturday and Sunday: closed