The problem this solves
AI research is powerful and easy to overspend: token-heavy models on every row, or prompts whose quality drifts.
Common situations
- Claygent output quality is inconsistent.
- AI columns are the biggest cost line.
- You need structured, verifiable research at scale.
Typical scope
- Model selection (fixed-price vs token-priced)
- Prompt design with output structure
- Verification columns and sampling
- Cost controls per run
Systems involved
Claygent / Use AI, your own model API keys optionally.
Deliverables
- Prompts with structured output formats
- Quality-control sampling method
- Cost per researched record
What we need from you
What the research must produce, volume, tolerance for AI error rates.
Technical considerations
AI output should be verified where it drives decisions — plan verification columns, not blind trust.
How the request process works
- Submit the project form with your requirements (about 5 minutes).
- The CFE team reviews it and responds with scope and next steps.
- If there is a fit, you get a scoped proposal; if not, an honest referral to a better-fit resource.
Request this service
Describe your project — the form carries this service selection automatically.