Custom AI Development

AI Built Your Way,
for Your Data

Generic AI products hit a ceiling. We build custom models trained on your data, calibrated to your domain, and integrated into your systems — production-grade from day one.

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Capabilities

What We Build

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LLM Fine-Tuning

Take foundation models (GPT, Llama, Mistral, Gemma) and specialise them on your proprietary data — achieving domain expertise that general models cannot match.

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Computer Vision

Object detection, classification, segmentation, and OCR systems for manufacturing QC, document processing, security, and retail analytics.

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NLP & Text Intelligence

Sentiment analysis, entity extraction, document classification, contract parsing, and knowledge extraction from unstructured text.

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RAG Systems

Retrieval-Augmented Generation: give your LLM access to your internal knowledge base, with source citations and auditability built in.

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MLOps & Model Lifecycle

CI/CD pipelines for models, automated retraining, drift detection, A/B testing frameworks, and model registry management.

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Cloud-Native Deployment

Deploy on AWS, Azure, or GCP with auto-scaling inference endpoints. On-premises and air-gapped deployments available for regulated industries.

Generative AI Applications

From fine-tuned model to working product: conversational chat interfaces, automated content generation pipelines, AI code assistants, and document automation built on your custom LLMs.

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ML Forecasting & Recommendations

Custom ML models for demand forecasting, personalised recommendations, and anomaly detection built on your tabular or time-series data — not generic off-the-shelf solutions.

Tech Stack

Technologies We Use

Python .NET C# TypeScript JavaScript PyTorch TensorFlow Hugging Face Transformers LangChain LlamaIndex Semantic Kernel Microsoft Agent Framework OpenAI Agents SDK Google Agent Development Kit Google Agent CLI OpenCV FastAPI Node.js React Next.js Angular MLflow Azure ML AWS SageMaker Vertex AI Anthropic OpenRouter Ollama Pinecone Weaviate pgvector Qdrant Neo4j Redis n8n Kubernetes Docker Azure OpenAI Azure AI Search Vercel AI SDK LangGraph
FAQ

Common Questions

It depends on the task. For domain adaptation, a few thousand high-quality examples can be sufficient. For highly specialised tasks, we may need more. We always start with a data audit to give you a realistic picture.

Data quality matters, but messy data doesn't mean no AI. We include data cleaning and labelling as part of our engagements. We'll tell you upfront what data work is needed and how it affects timeline.

Yes. For regulated industries or data-sensitive clients, we routinely deploy models on-premises or within a client's private cloud environment. No data leaves your perimeter.

We set up monitoring for model drift, performance degradation, and anomalous outputs. We offer ongoing retaining and optimisation as a managed service, or we can hand over the MLOps to your team.

Ready to Build Your Custom AI?

Tell us what you're trying to achieve. We'll come back with a realistic assessment, timeline, and fixed-scope proposal.

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