We build machine learning pipelines, natural language tools and computer vision systems for companies that need results, not buzzwords. Based in Scotland, working with clients across the UK and Europe.
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We started Core AI Expert in 2021 because too many businesses were paying for AI projects that never shipped. Our founding team had spent years building production ML systems at fintech and logistics companies, and we knew the gap between a promising prototype and a reliable product was where most projects died.
Today we are a team of twelve: five machine learning engineers, three backend developers, two data engineers, a product designer and a project lead. Everyone writes code. Nobody has "strategy" in their title.
We work from our office at 986 Tamara Row, Jakubowski-upon-Weissnat, JT77 7PA, Scotland, United Kingdom. Most of our client meetings happen over video, but we do on-site workshops for larger engagements.
Each engagement starts with your data and your problem. We pick the simplest model architecture that solves it, then engineer the infrastructure to keep it running.
Demand forecasting, churn prediction, pricing optimisation. We train models on your historical data and deploy them behind an API your existing systems can call. Typical accuracy improvements: 15 to 40 percent over rule-based baselines.
Document classification, entity extraction, sentiment scoring and chatbot development. We fine-tune open-source language models on your domain vocabulary so they understand insurance claims, legal briefs or support tickets with high precision.
Quality inspection on production lines, medical image triage, document OCR. We handle labelling pipelines, model training and edge deployment on NVIDIA Jetson or cloud GPU clusters depending on latency requirements.
Before a model can learn, data needs to be clean, joined and versioned. We build ETL pipelines with Apache Airflow or Prefect, set up feature stores and create monitoring dashboards so you know when data quality drifts.
Continuous training, A/B testing, canary deployments. We containerise models with Docker, orchestrate with Kubernetes and track experiments in MLflow. Retraining triggers fire automatically when performance drops below your threshold.
Not sure if machine learning is the right tool? We run a two-week feasibility study: review your data, benchmark quick models, and deliver a written report with cost estimates and a go/no-go recommendation. No obligation to build with us afterwards.
Most engagements follow four phases. Timelines vary, but a typical mid-size project from kick-off to production takes eight to fourteen weeks.
We spend two to five days understanding your data sources, business constraints and success metrics. You get a written scope document and a fixed-price quote.
Our engineers build a minimal model in two to three weeks. You see real predictions on real data before committing to a full build.
We harden the model, write tests, set up CI/CD pipelines and integrate with your systems via REST API or event streams. Load testing included.
After launch we monitor model performance, retrain on fresh data and ship improvements. Monthly reports show accuracy, latency and cost metrics.
Quick answers to the things clients ask most often during initial calls.
Tell us about your project and we will reply within one business day. No sales pitch, just a straightforward conversation about what is feasible.