We design, train, and deploy machine learning models tailored to your data. From predictive analytics to natural language processing, our team turns raw information into actionable business intelligence.
We started AI Core Value because too many businesses were paying for AI projects that never left the prototype stage. Our founding team spent years inside large consultancies watching proof-of-concept models gather dust. We wanted to fix that.
Every project we take on has a defined production target. If a model cannot run reliably on your infrastructure within a set timeline, we say so before any contract is signed. That honesty saves months and tens of thousands of pounds.
Our team includes specialists in statistical modelling, deep learning architecture, data engineering, and MLOps. We work in two-week sprints, with a live demo at the end of each one so you can see exactly what your investment is producing.
Each service below can be engaged independently or combined into a full AI transformation programme.
We build supervised and unsupervised models for classification, regression, clustering, and anomaly detection. Training data is cleaned, balanced, and version-controlled before a single experiment runs. Typical turnaround from kick-off to a production-ready model: six to ten weeks depending on data complexity.
Forecast demand, churn, equipment failure, or cash flow using time-series models and ensemble methods. We integrate predictions directly into your dashboards or ERP system through REST APIs, so decision-makers see numbers they can act on today.
Sentiment analysis, entity extraction, document classification, chatbot development, and summarisation. We fine-tune large language models on your domain-specific corpus so the system understands your terminology, not just generic English.
Object detection, image segmentation, optical character recognition, and quality inspection systems. We deploy models on edge devices, cloud GPUs, or hybrid setups depending on your latency and bandwidth constraints.
Raw data is worthless without clean pipelines. We design ETL workflows, data lakes, and feature stores that feed your models reliably. Every pipeline includes monitoring, alerting, and automated retraining triggers when data drift exceeds your thresholds.
Deploying a model once is easy. Keeping it accurate over months is the hard part. We set up CI/CD for machine learning: automated testing, canary deployments, A/B model comparison, and rollback procedures. You get a system that improves itself.
Four phases, each with a clear deliverable. No phase starts until the previous one is approved.
We review your existing data sources, interview stakeholders, and define success metrics. This phase typically takes one week and ends with a feasibility report.
A working proof-of-concept model is trained on a representative sample of your data. You see accuracy scores, confusion matrices, and a live demo within three weeks.
The validated model is containerised, tested under load, and deployed to your cloud or on-premise environment with API endpoints your applications can call.
Ongoing performance tracking, retraining schedules, and quarterly reviews ensure the model stays accurate as your business and data evolve.
Straight answers to the questions we hear most often.
Tell us about your project and we will respond within one business day.
45 Bartoletti Gate, New Brown, EL28 1OI, Northern Ireland, United Kingdom
Monday to Friday, 9:00 am to 5:30 pm
