AI and Machine Learning Solutions

The gap between an AI demo and an AI system is enormous, and almost all of it is engineering. A prototype that impresses in a meeting has no evaluation harness, no cost ceiling, no handling for the request that returns nonsense, no audit trail for the decision it just influenced, and no plan for what happens when the underlying model is deprecated. Most stalled AI initiatives did not fail at the model — they failed at everything around it.

We start by pressure-testing the use case. Is there a measurable decision or task being improved? Is there a baseline to beat? What is the cost of a wrong answer, and who absorbs it? Several engagements have ended with us recommending a rules engine or a well-indexed search instead of a model, which is a cheaper and more honest outcome than shipping something that cannot be evaluated. When the case holds, we build: retrieval-augmented generation with chunking and reranking tuned against your corpus, agentic workflows with explicit tool boundaries and step limits, classical ML where tabular data and interpretability matter, or fine-tuning where a smaller specialised model beats a large general one on cost and latency.

Evaluation is non-negotiable. Every system ships with a golden dataset, automated regression evaluation in CI, and quality metrics tracked over time — because model behaviour drifts, prompts get edited, and providers change things underneath you. We add guardrails against prompt injection and data exfiltration, PII redaction before anything leaves your boundary, token cost budgets with alerting, graceful degradation when a provider is down, and human-in-the-loop review wherever the stakes justify it.

On the platform side we cover feature stores, model registries, versioned datasets, reproducible training pipelines and monitored inference endpoints — so a model in production is a governed artefact with lineage, not a pickle file someone copied to a server.