Read across more of what your organization already knows. Keep the judgment human.
Prova’s proprietary AI systems bring program files, reports, data, and relevant research into one structured analysis, then connect important conclusions back to their sources.
The systems expand how much can be examined. A Field principal remains responsible for what the analysis means and what reaches the client.
The material is already there. Reading it together takes more time than most teams have.
Intake forms, case notes, program data, reports, transcripts, correspondence, and administrative files capture different parts of how the program actually runs. Relevant research adds another large body of material.
Field uses Prova’s AI systems to make more of that material available to a real decision while keeping important conclusions connected to their sources and open to review.
Frontier models
High-volume reading, structured extraction, comparison, and synthesis.
Authored domain logic
Field’s structured rules for claims, sources, limitations, and measurement.
Senior review
A Field principal brings context, interpretation, and judgment.
Source traceability
Important conclusions stay connected to the files, reports, and research behind them.
Result Clear conclusions you can inspect, use, and stand behind.
The scale comes from AI. The structure and judgment do not.
Prova combines frontier models, authored logic, source handling, traceability and human review. This allows Field to examine far more of the agreed material than a traditional manual review could usually cover.
Prova’s authored domain logic tells the systems which distinctions matter. Source traceability keeps important statements connected to the material behind them. A senior professional reviews the analysis, considers context and uncertainty, and decides what the final work can responsibly support.
The systems are not a client-operated software platform or a general-purpose chatbot.
A general-purpose model can read language. It does not know which distinctions matter for the decision in front of you.
Prova’s method gives the systems a deliberate analytical structure for claims, sources, limitations, program logic, and measurement.
The claim
What kind of claim is being made
What would need to support that claim
The material
Which files are relevant and how they connect
Where support is strong, partial, thin, or absent
The reading
Which observations are descriptive and which imply too much
Which assumptions depend on context or outside research
What is still open
What information and whose experience may be missing
What should be measured next without unreasonable burden
What the systems delegate—and what stays with people.
Prova’s systems delegate bounded work to AI
Work that gets better with breadth and speed.
- Reading across large bodies of files and research
- Extracting claims, observations, measures, and recurring concepts
- Connecting analytical statements to source material
- Comparing information across many documents
- Organizing material for systematic professional review
Judgment remains with people
Work where being confident and being right are not the same thing.
- Defining the important question or decision
- Understanding what an outcome means in a particular setting
- Deciding whether a conclusion is warranted
- Interpreting sensitive or contradictory material
- Designing workable measurement around real operations
- Taking responsibility for every client-facing conclusion
Everything crosses this line before it reaches you. No analysis becomes a client-facing conclusion without a Field principal deciding it is warranted, and that person stays accountable for it.
Prova’s AI systems are model-independent by design.
Frontier models will keep changing. Prova separates the models used for delegated reading from its analytical structure, professional standards, source traceability, and review process.
That model independence allows the technical layer to change without making any one model the source of Prova’s method or accountability. The authored logic and human review remain the stable parts of the systems.
From an important question to usable work.
01 · Person
Frame
Begin with the decision, claim, program logic, or measurement question that makes the work useful.
02 · Model
Read
Use Prova’s AI systems to examine the relevant files and research at a breadth a manual review rarely permits.
03 · Model
Trace
Keep important conclusions connected to the source material behind them.
04 · Person
Review
Apply professional judgment, organizational context, and explicit attention to uncertainty and limitations.
05 · Person
Use
Turn the analysis into a claims map, theory of change, measurement architecture, or another clearly defined deliverable.
See how this approach fits the question in front of you.
Discovery will suggest a likely starting point, the materials that may help, the questions Field would clarify, and what the engagement could produce.
Try Field Discovery