AI & Machine Learning Solutions

What we build

We design and build end-to-end AI systems that deliver measurable business value — not demos that never ship. Our work spans semantic search engines, intelligent document processing pipelines, autonomous AI agents, and custom large-language-model integrations.

With direct experience building AI platforms consumed by enterprise clients and AI agents alike, we understand the full stack: prompt engineering, vector databases, retrieval-augmented generation (RAG), cost management, and production monitoring.

Core capabilities

• OpenAI API integration — completions, embeddings, batch processing, function calling, and Assistants
• Google Gemini and Vertex AI — including cost-effective PDF OCR and GCP prediction batches
• Semantic search using word embeddings and vector similarity — OpenSearch, pgvector, Pinecone
• Intelligent document processing — extract structured data from PDFs, company accounts, and reports using JSON schema-based prompts
• Clustering and classification pipelines for large unstructured datasets
• MCP (Model Context Protocol) server design — expose your data and tools to AI agents
• AWS SageMaker deployments with open-source models (e.g. DeepSeek)
• Pydantic AI agents for automated research and insight gathering
• OpenAI Batch API — reduce per-request costs by up to 50%

Cost-effective AI engineering

AI projects fail when costs spiral out of control. We design pipelines that use the right model for each task — expensive frontier models only where precision demands it, cheaper models and batch processing everywhere else. In practice, this has halved OpenAI request costs without sacrificing output quality.

Who this is for

This service suits businesses looking to automate data extraction, build intelligent search, add AI summarisation to existing products, or explore AI agent architectures. Whether you have a clear brief or just a problem to solve, we can scope and deliver it.