CiterLabsPRACTICAL USE CASES
Where it can help.
Explore consistent classification, extraction, response formats, or domain-specific style when prompting alone falls short.
AI model fine-tuning
Specialize a model only when the evidence supports it.
Assess whether fine-tuning fits the task, then build a measured experiment with an evaluation baseline.
Scope your project
DEFINED DELIVERABLES
What to scope.
A feasibility assessment, data preparation plan, baseline evaluation, and—if viable—a training experiment with documented results.
BEFORE WE BUILD
Check the foundations.
Data rights, privacy, dataset quality, model support, and compute costs are checked first. Compare against prompting or retrieval; improvements are not guaranteed.
A CLEAR PATH FROM IDEA TO USE
A considered process.
A concrete outcome.
Work is scoped around your priorities, with checkpoints to review the result before moving on.
Discover
Map the problem, users, existing tools, and data constraints.
Define
Agree deliverables, acceptance criteria, responsibilities, and an estimate.
Build & review
Develop in stages and review working outputs with your project owner.
Handover
Check the agreed scope and provide documentation, access, and next steps.
START A CONVERSATION
Turn a business problem
into a clear project.
Bring a task, a bottleneck, or a product idea. The first step is a clear scope.