AI & ML

Overview

Retrieval-augmented generation, autonomous agents, computer vision pipelines, and fine-tuned models — fully deployed, monitored, and maintained.

Why it matters

Most AI demos don't survive contact with production data. We focus on the engineering that actually matters: retrieval quality, latency budgets, cost controls, and evaluation frameworks that let you measure whether the model is actually doing its job. We don't ship proof-of-concepts — we ship systems.

Common use cases

• Internal knowledge bases and document Q&A systems (RAG)
• Autonomous workflow agents that act on data and call APIs
• Fine-tuned models for domain-specific language or vision tasks
• Computer vision pipelines for inspection, counting, or classification
• LLM-powered product features (copilots, summarisation, extraction)

What you get

• Deployed inference API with latency SLA
• Evaluation suite with baseline benchmarks
• Cost and token monitoring dashboard
• Model card and system prompt documentation
• MLOps pipeline for retraining and model versioning