Enterprise AI that survives scrutiny.
Enterprise buyers don't buy model quality — they buy evidence, auditability and predictable cost. This is the practice I use to get AI features from demo to dependable.
The reference pipeline I build against
Documents, events and system data normalised into one schema.
Chunking, embeddings and hybrid search with citation spans.
LLM with structured outputs, tool calls and confidence scoring.
Human-in-the-loop queue routed by confidence thresholds.
Overrides become labels; evals gate every prompt or model change.
AI Product Strategy
Choosing where AI compounds: high-volume, judgement-heavy workflows with tolerant failure modes. Building the business case around hours saved and deals unblocked, not model novelty.
LLM Integration
Structured outputs, function calling, model tiering and fallbacks. Designing the contract between the model and the product so failures degrade gracefully.
RAG & Retrieval
Chunking strategy, embeddings, hybrid retrieval and citation spans. Retrieval quality is product quality — most 'hallucination' problems are retrieval problems.
Prompt Engineering
Versioned prompts treated as product code: reviewed, evaluated, rolled out behind flags, and never changed without a regression run.
AI Evaluation
Golden datasets, task-level rubrics, LLM-as-judge with human calibration, and regression gates in CI. Evaluation is the roadmap item that makes every other one safe.
Agentic AI
Scoped, tool-using agents with explicit stop conditions and human checkpoints. Autonomy earned incrementally as reliability data accumulates.
Human in the Loop
AI proposes, expert approves. Confidence thresholds route work; every override becomes training signal and every edit becomes audit trail.
Responsible AI & Governance
Data lineage, PII handling, model cards, bias review and an approval path enterprise risk teams can actually sign.
Cost Optimisation
Caching, tiering, token budgets per transaction and unit-economics dashboards. AI features that don't survive contact with a CFO don't ship.
Enterprise AI Adoption
Shadow mode, design partners, change management and evidence-first UX. Enterprise AI is sold on trust and bought on audit.
My rule for shipping AI
If I can't describe the failure mode, the fallback, the reviewer and the cost per transaction, the feature isn't ready. AI is a probabilistic component inside a deterministic product promise — the product design is what makes the probability acceptable.