Choose the right model
Compare hosted APIs and open-weight models on a representative evaluation set. Assess task quality, context, latency, licensing, privacy, operating cost, and deployment constraints.
Research AI engineering
Make AI part of a method.
Not just an experiment.
Build useful research tools, evaluate models against the task, and integrate AI into a workflow that people can inspect and repeat.
Build research assistants
Search approved papers, protocols, manuals, and internal knowledge with source references. Design retrieval, access boundaries, and evaluation for your corpus rather than a generic demo.
Engineer scientific data pipelines
Organize inputs, extract structured information, validate outputs, and connect analysis steps. Keep source provenance and exceptions visible, with human review for consequential decisions.
Improve an existing AI system
Investigate weak retrieval, unsupported answers, prompt fragility, unreliable tools, and escalating costs. Add traceability, regression checks, retries, and deployment controls.
Make workflows reproducible
Capture code, model and data versions, configurations, evaluation conditions, and known limitations. Distinguish repeatable computation from claims that need independent scientific validation.
Help the team use it
Hands-on sessions around real work, operating notes, and agreed ownership. Transfer the knowledge needed to use, monitor, and maintain the solution.
From question to acceptance
Define what a good answer looks like first.
A useful pilot begins with a task, suitable data, and a way to evaluate the output. We agree what the tool must do and what remains the research team's judgment.
A typical delivery sequence
- Map the task, data access, review needs, and existing tools.
- Establish a baseline and compare suitable approaches.
- Build a bounded implementation and test against the agreed evaluation set.
- Document results, limitations, operation, and next steps.
No client data is sent to an external model provider until the processing arrangement is agreed. Initial inquiries should contain only a non-sensitive description.
Scope an application or pipeline →