Capabilities across the AI stack.

Predict demand, sales trends or asset turnover with models validated through rigorous backtesting. I deliver forecasts you can trust, with clear uncertainty bounds and drift monitoring so you spot degradation before it affects your planning.

Identify churn before it happens, flag risky applications or prioritise cases that need attention now. Models are built for your specific domain — risk, healthcare, fraud — with explainability built in so stakeholders understand the why, not just the what.

Estimate prices, valuations or operational KPIs with models that go beyond simple benchmarks. Regularised regression, tree ensembles and stacking are combined with production-grade retraining so your estimates stay accurate as markets shift.

Group customers, transactions or operational events into meaningful segments that drive action. I use unsupervised methods with dimensionality reduction to ensure the segments are interpretable, stable and directly usable for targeting or resource allocation.

Your models are only as good as the data feeding them. I build clean, reliable data foundations — SQL models, ETL pipelines and BI dashboards — that turn fragmented sources into a single source of truth your team can actually use.

I build autonomous agents that handle real work — reading documents, querying APIs, updating databases and drafting responses. When confidence drops, they escalate to a human. Whether it is loan review, contract analysis or customer-support triage, you get a system that works while you focus on higher-value decisions.

Turn scattered documents and knowledge bases into intelligent systems that answer accurately, cite sources and enforce structured outputs. From RAG pipelines to multi-step reasoning agents, every system is built with retrieval, tools and evaluation so quality is measurable, not assumed.

Before any system goes live, it is tested. I run structured LLM evaluation with RAGAS, DeepEval and PromptFoo, measuring quality, cost and latency against real use cases so you choose the right model and configuration with confidence.

When off-the-shelf models are not specific enough, I fine-tune open models on your data using LoRA and QLoRA. A recent customer-support adaptation lifted response quality by 87 percent over the base model — real weight updates, not clever prompting.

A model in a notebook is not a product. I ship end-to-end pipelines with experiment tracking, drift detection, automated retraining and REST APIs — so your system runs reliably in production, not just in a demo.