01 · Existing material
Claims and Sources Map
See what sits behind the claims you already make—and where the support is thin or missing.
See if it fitsField uses Prova’s proprietary AI systems to read across the program files, reports, data, notes, and relevant studies scattered throughout an organization. A Field principal turns that material into clear answers for the board, funder, or program decision that matters now. Field is the first practice from Prova, which develops the method and systems behind the work.
For nonprofit leaders—and foundations giving grantees room to work through questions that matter.
A board meeting, public claim, or program choice can demand a clear answer now. The useful material is usually spread across program files, grant reports, staff notes, data, and years of learning.
Inside the work
Detailed knowledge built by running the program and working with people every day. It is spread across files, notes, meetings, and the people who carry the work.
Ready to use
Clear conclusions tied to sources, with the limits stated plainly enough for a board, funder, partner, or leadership team.
Three focused engagements move from the information you already have, through the assumptions behind the program, into measurement that becomes more useful over time.
01 · Existing material
See what sits behind the claims you already make—and where the support is thin or missing.
See if it fits02 · Program logic
Make the assumptions beneath the program visible, then connect them to relevant research and local learning.
See if it fits03 · What comes next
Choose measures, workflows, and review points that fit the decisions your team makes and how the program actually runs.
See if it fitsThe systems help Field read across program files, reports, notes, and relevant research, then connect important conclusions back to their sources.
Prova combines frontier models, authored logic, source handling, traceability and human review. That makes it possible to extract, compare, and synthesize information across far more material than a traditional review could cover.
AI addresses a specific constraint: the time required to read all of this together. Models can miss context and produce confident mistakes, so Field adds professional review and brings the organization’s context into the interpretation. The reading scales. Consequential judgment stays human.
High-volume reading, structured extraction, comparison, and synthesis.
Field’s structured rules for claims, sources, limitations, and measurement.
A Field principal brings context, interpretation, and judgment.
Important conclusions stay connected to the files, reports, and research behind them.
Result Clear conclusions you can inspect, use, and stand behind.
A Claims and Sources Map puts each claim beside the files, data, and research behind it—and makes the limits clear.
See the three engagements| The claim | Support |
|---|---|
| 85% of enrolled participants complete the program | strong |
| Employer partners value the placements | partial |
| Participants leave with more confidence | thin |
For Nonprofits
For nonprofit leaders who need clearer board and public communications, better program decisions, and measurement their teams can maintain.
Field for nonprofitsFor Foundations
For foundations that want to make senior measurement support available through one defined engagement. The grantee sets the question, selects the materials, and decides what is shared.
Field for foundationsWe clarify the board question, public claim, program choice, or measurement decision that needs an answer.
You decide which files, reports, program data, and research belong in the work. Field does not need direct access to your operational systems.
Prova’s AI systems handle the intensive reading and comparison. A Field principal reviews the analysis and brings your program context into the interpretation.
You receive clear, source-traceable work built for the decision or measurement choice in front of you.
Prova.
Field is the practice. Prova is the intellectual architecture—a thesis about what honest evidence requires, what AI changes and what it doesn’t, and why the institutions that produce evidence matter as much as the evidence itself.
Read the thesis and the method behind the workAnswer five practical questions. Field Discovery will suggest a useful place to begin and show the details that matter for that path.
Try Field Discovery