Inventive AI's accuracy claim needs a reproducible test

Define accuracy and hallucination at the answer level

Inventive AI's page presents a quantified accuracy statement and a categorical zero-hallucination statement. Those phrases need an operational definition before a proposal team can compare them with its risk. An answer can be factually grounded yet nonresponsive, stale, attributed to the wrong legal entity, inconsistent with the solicitation, outside approved past performance, over the page limit, or contradicted by another volume. A fabricated fact is only one failure class.

Create a scoring rubric for supported factual claims, source citation, source freshness, solicitation responsiveness, amendment alignment, instruction compliance, entity and contract identity, quantitative consistency, approved reuse, uncertainty disclosure, and prohibited content. Define the unit of analysis: answer, sentence, claim, citation, requirement, or complete volume. State whether partial credit is allowed and how abstentions, missing evidence, conflicting sources, and reviewer disagreement affect the result.

Freeze the test set and every source version

Build a representative test set from sanitized questions that match the contractor's work: technical approach, management, staffing, past performance, corporate experience, resumes, security, pricing narrative, representations, and compliance matrices. Include ambiguous questions, compound requirements, amendment changes, unavailable evidence, conflicting approved records, expired credentials, similarly named contracts, and facts that must not cross organizational or customer boundaries. Keep the questions hidden from configuration work until the test begins.

For every run, retain the solicitation and amendment set, requirement decomposition, knowledge sources and hashes, access controls, model and application versions, prompt or workflow configuration, retrieval results, draft, citations, confidence or abstention, reviewer identity, edits, final disposition, and timestamp. A later content-library cleanup must not overwrite the evidence used by the earlier answer. Without that frozen lineage, the team cannot reproduce either a success or a miss.

Report the error distribution, not one headline percentage

Publish the test population, sample design, number of questions and claims, scoring rule, reviewer qualifications, inter-reviewer agreement, and results by failure class. Separate unsupported facts, wrong citations, stale content, incomplete coverage, instruction violations, entity confusion, numerical inconsistency, and appropriate abstention. Report false confidence and severe errors individually even when an average score is high. A 95 percent figure can conceal one disqualifying representation or material past-performance error.

Repeat the evaluation after model, connector, indexing, permission, content, or workflow changes. Use a controlled regression set and a new holdout set so the system cannot simply fit known questions. Monitor production drafts separately, because user behavior, novel solicitations, permissions, and live content differ from a benchmark. Provider-published metrics can identify a diligence question, but they do not establish the contractor's configured performance or a government evaluator's response.

Keep human approval and submission authority explicit

A test result should route each answer to an accountable subject-matter, contracts, pricing, security, legal, or executive reviewer according to content and risk. Record accept, edit, reject, escalate, or abstain with reason. Approval of reusable source content is not approval of its use in every solicitation, and approval of one draft answer is not authority to sign or submit an offer. The controlled proposal record must preserve the final reviewed text and the exact solicitation version it answers.

GovCon Market Brief reviewed the registered Inventive AI page on September 16, 2026. It supports the provider's current statements about connected knowledge, content maintenance, RFP drafting, and the displayed accuracy and hallucination claims. It does not establish the evaluation method, independent accuracy, a contractor's configuration, source permissions, solicitation compliance, reviewer approval, proposal quality, government evaluation, award probability, or contract outcome.

Enterprise buyer test

Translate this change into the exact population, record type, workflow stage, decision owner, effective date, and evidence that could be affected. Ask current or prospective providers to demonstrate the named workflow with representative data and an exception—not a polished feature tour. Record what official documentation establishes, what a provider states, what the team observes, and what remains unresolved.

A defensible review also identifies the dependency outside the product. Authority interpretation, policy configuration, data quality, integrations, human judgment, approval rights, release governance, training, and retained evidence may remain customer or service responsibilities. The evaluation should preserve those boundaries instead of treating a technology claim as the complete operating model.

What we will watch next

GovCon Market Brief will watch the named source and affected market records for later evidence that changes status, scope, availability, implementation timing, workflow consequence, or the limits of the initial report. A later announcement does not silently overwrite this dated account; the change ledger preserves the sequence.