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AI where it already fits

Summarise, classify, extract, draft. Wire a model into the workflow your team already runs (a support macro, a triage queue, a report that writes its own first draft) with a prompt you can read and a test that tells you when it drifts.

Ask your documents

Retrieval over your own corpus: contracts, policies, tickets, wiki, PDFs. Chunking and embeddings tuned to your documents, hybrid search with a reranker, and every answer citing the passage it came from so a reader can disagree with it.

RAG that survives contact with real data

The version that holds up past the demo: incremental ingestion, freshness and permissions carried through the index, query rewriting, retrieval evals with real recall numbers, and a fallback that says "I do not know" instead of inventing a clause.

Agents with hands

Agents that act on your systems through typed tools and MCP servers (book, refund, update, escalate) with scoped permissions, idempotent actions, a full audit trail, and a kill switch. Human approval on anything expensive or irreversible.

Evals, guardrails and the release gate

A graded dataset built from your real traffic, an eval suite in CI that blocks a prompt or model change from shipping when quality drops, plus injection and PII checks, refusal behaviour, and per-request cost and latency budgets you can watch.

Your own inference stack

When the API bill or the data boundary stops making sense: open-weight models served on your own GPUs, quantisation and batching sized to your traffic, LoRA fine-tunes on your data, and a routing layer that sends the easy 80% to the small model. The fine-tuning and quantisation half of this is thesis work I can show; no specific serving stack is named here because that choice depends on your hardware.

Multimodal and voice

Vision, audio and documents in the same pipeline: read the invoice, watch the video, hear the call. Streaming speech in and out for a phone or in-app agent, with latency budgets that keep a conversation feeling like a conversation.

AI inside a mobile app

The part most teams underestimate: streaming into a React Native or Flutter UI, on-device and hybrid inference, offline behaviour, token cost per session, and a review-safe story for what the model can and cannot do on a user’s phone.