Custom AI models, intelligent agents, and automation pipelines — engineered from first principles to solve your specific problems, not retrofitted from a generic product.
Every AI Solutions engagement is scoped around your data, your team, and your goals.
Demand forecasting, churn prediction, pricing optimisation — trained on your data, deployed into your workflows.
GPT, Claude, and open-source LLMs wired into your product — from customer-facing chatbots to internal knowledge assistants.
Retrieval-augmented generation over your documents, policies, and data — so your AI answers accurately from your own sources.
Image classification, object detection, document parsing, sentiment analysis — end-to-end pipelines built to production scale.
Replace repetitive manual processes with intelligent automation that learns, adapts, and escalates only when human judgement is needed.
CI/CD for AI — automated retraining, drift detection, and performance monitoring so your models stay accurate over time.
A retail chain reduced overstock by 43% and improved inventory turnover 2.1x using a custom ML forecasting model trained on 4 years of sales data.
An asset management firm automated 90% of their document intake workflow using NLP and OCR pipelines, saving 1,200+ hours per month.
A telehealth platform integrated an LLM-powered triage assistant that pre-qualifies patient symptoms, reducing physician intake time by 35%.
A DTC brand deployed a real-time recommendation engine that increased average order value by 28% and repeat purchase rate by 19%.
We assess your data sources, quality, and volume to identify what's feasible and where the highest-value signals live.
Architecture selection, feature engineering, and training strategy — every decision documented before a model is trained.
Iterative training with rigorous evaluation — precision, recall, and business-metric benchmarks agreed upfront.
Production deployment with APIs, dashboards, or direct integrations into your existing stack — zero disruption.
Drift detection, automated retraining, and continuous performance monitoring — AI that compounds in value over time.
Not always. The right approach depends on the problem. For structured prediction tasks, a few thousand labelled records can be enough to build a valuable first model. For LLM-based solutions, you often need very little proprietary data at all — we use your documents or knowledge base as context. We assess your data situation in discovery and tell you honestly what's feasible before scoping the project.
Yes. We design models with integration in mind from the start. We can expose predictions via a REST API, embed them directly into your application, connect them to your CRM or ERP, or pipe results into your data warehouse. We've integrated AI into AWS, Azure, GCP, Salesforce, HubSpot, SAP, and custom-built platforms.
We define accuracy metrics in business terms before training begins — not just technical metrics like F1 score. Every model goes through rigorous offline evaluation, then a controlled production pilot before full rollout. Post-launch, we monitor for model drift and retrain on a schedule or when performance drops below agreed thresholds.
Both — we recommend what's right for your situation. For sensitive data or strict latency requirements, open-source models (Llama, Mistral, etc.) running on your own infrastructure are often the best choice. For rapid development or lower data-sensitivity use cases, GPT-4 or Claude API can deliver faster results at lower cost. We'll give you a clear tradeoff analysis before you commit.
Book a free AI audit — we'll identify your top three automation opportunities within 48 hours.
No commitment. No cost. Just clarity.