Frame the operational question before the model.
Workflow analysis, data constraints, error modes, and commercial impact define what should be built.
Research depth for AI systems that have to work.
The technical layer behind Zeffron's consulting work: data science, ML modelling, retrieval, fine-tuning, agent design, and evaluation harnesses.
Choose the right technical path for the task.
Retrieval, fine-tuning, classification, forecasting, and agent workflows are selected against measurable criteria.
Measure before the system reaches users.
Harnesses, golden sets, regression checks, and review loops keep quality visible after launch.
01
Data science and ML modelling
Dataset preparation, feature analysis, model comparison, error analysis, and decision support.
02
Retrieval and knowledge systems
Document pipelines, vector search, freshness, citations, access control, and fallback design.
03
Fine-tuning and adaptation
Domain adaptation, response shaping, labelled datasets, experiment tracking, and release criteria.
04
Agents and workflow orchestration
Tool use, permission boundaries, handoff logic, monitoring, and human review for high-impact actions.
Turn research into a deployable system.
Book a focused consultation on your data, evaluation needs, and production constraints.