Minimum necessary data
Begin with one bounded commercial question. Request only the fields needed to test it and exclude direct personal identifiers wherever practical.
Trust, client data & AI use
Evidence should become more useful without becoming less controlled.
The Pricing Intelligence Pilot is being designed around a simple rule: use the least data required, make every material conclusion traceable and keep a qualified person responsible for what the client receives.
The operating principles
Controls before claims.
These are design commitments for the pilot. The exact controls for an engagement are confirmed in its signed documents before any client information is transferred.
Evidence before synthesis
Reproducible calculations establish the facts. AI may help organize, compare and explain approved evidence; it does not manufacture the economics.
Humans remain accountable
A RevSeekr expert reviews every material finding, limitation and recommendation before it is released or discussed as a client conclusion.
The controlled data path
No black-box upload. No automatic price changes.
- 01
Agree
Define the business question, permitted sources, required fields, prohibited data, decision owner and success criteria.
- 02
Transfer
Use a client-approved secure exchange. Do not send datasets through the website contact form or ordinary email.
- 03
Calculate
Validate, transform and analyze the scoped extract in a client-specific workspace using deterministic analytical methods.
- 04
Synthesize
Use an approved enterprise AI service only on the material permitted for that purpose, with direct identifiers and unnecessary detail removed.
- 05
Review and close
Challenge findings with the client, release an expert-reviewed package and delete or return information on the agreed schedule.
Technology approach
Different tools do different jobs.
The pilot is designed as a controlled analytical workflow, not a single model with unrestricted access. Exact products, providers and processing locations are disclosed and approved for each engagement.
Client-approved transfer
An access-controlled data room, managed file exchange or another method approved in the engagement documents.
SQL and analytical code
Data-quality tests, peer-group construction and pricing calculations that can be rerun and inspected.
Approved enterprise services
Synthesis of permitted evidence and redacted interview material. Consumer AI accounts are not an approved pilot workflow.
Evidence-linked reporting
Findings, sources, confidence, limitations, unresolved questions, owners and recommended next decisions.
RevSeekr commits to
- Use client information only for the agreed engagement purpose
- Keep each client's workspace and analysis logically separated
- Not use client data to train a general-purpose or cross-client model
- Disclose approved service providers before data transfer
- Restrict access to the people who need it for the engagement
- Document retention, deletion and incident responsibilities
- Keep a human reviewer accountable for released conclusions
Important boundaries
- No system can promise perfect security or error-free output
- Raw client data is not sent to an AI model by default
- Any exception requires a documented need and client approval
- The pilot does not automatically set, approve or change prices
- Findings depend on the scope, definitions and evidence supplied
- Material business decisions remain with the client
- Vendor-specific advice includes disclosure of relevant commercial relationships
Before data moves
The controls become contractual.
A public trust statement is useful; it is not enough. Every pilot must define the operating details that matter for that client's information and jurisdiction.
01Mutual confidentiality and a signed pilot scope
02Data categories, permitted uses and prohibited fields
03Security controls, AI use and approved providers
04Access, retention, deletion and incident response
05Output limitations, client validation and decision ownership
This page explains RevSeekr's pilot design in plain language. A signed agreement controls if it differs from this page. See the Privacy policy and Terms of use. RevSeekr's internal controls are informed by the NIST AI Risk Management Framework.