Choose competitive-intelligence software by running it against a real decision and a labeled evidence set. Evaluate source coverage, capture comparability, exact change evidence, review controls, decision routing, provenance, export, and ongoing operating burden. A feature checklist or polished AI summary cannot establish whether the system will improve your decisions.
What this guide covers
- the workload to define before evaluating products;
- eight capabilities that determine evidence quality;
- a proof-of-value test with precision and outcome gates;
- a copyable evaluation scorecard.
Start with one decision workflow
Write a concrete use case:
Monitor five named competitors for plan, allowance, entitlement, and entry-motion changes; review material evidence weekly; route verified movements to the pricing owner; preserve the outcome.
Then define source types, expected volatility, reviewers, decision half-life, and sensitive boundaries. Without this, every demo looks capable because the job remains vague.
Evaluate the whole evidence chain
1. Source fit
Can the system monitor the public pages and source types that define your decision? How does it handle redirects, client rendering, region, login state, and blocked content?
RFC 9309 standardizes the Robots Exclusion Protocol. An evaluation should ask how automated access respects published controls and operational limits, while separately reviewing terms and legal obligations.
2. Capture comparability
Does the record preserve observed time, final URL, locale, currency, billing state, content health, and a usable prior version? A screenshot alone may be insufficient for field comparison; extracted text alone may lose context.
3. Structured evidence
Can you define fields such as plan, price, allowance, entitlement, availability, and audience? Can you inspect the exact before and after values behind a summary?
4. Noise control
Can the system distinguish content movement from navigation, timestamps, experiments, and capture faults? Ask for reviewed precision on your test set, not a generic accuracy claim.
5. AI boundaries
Can generated claims point to exact supplied evidence? Is “not established” an allowed output? Are observation, calculation, interpretation, and unknown separate?
6. Review and audit
Can a reviewer confirm, reject, correlate, or mark an evidence fault with a reason? Are original records immutable? W3C PROV-O supplies a general provenance model; the buying question is whether the product preserves an inspectable chain from source through transformation and review.
7. Decision routing
Can a material movement reach a named owner with a due date and later outcome? Alerts without terminal decisions create activity, not intelligence.
8. Portability and operations
Can you export sources, observations, decisions, and outcomes in useful form? What is the ongoing review burden, retention model, access control, support boundary, and total cost?
Run a labeled proof of value
Build 30–50 cases from your actual sources:
- true material changes;
- true but immaterial changes;
- page noise;
- locale or billing-state differences;
- first baselines;
- failed or blocked captures;
- ambiguous semantic movements;
- reversals and rapid experiments.
Do not reveal all labels before the run. Score:
| Measure | Question |
|---|---|
| Material precision | Of routed items, how many were genuinely decision-worthy? |
| Material recall | Which known important cases were missed? |
| Citation fit | Did each claim point to supporting evidence? |
| Fault handling | Were bad captures blocked from interpretation? |
| Review time | How much expert effort reached a decision? |
| Decision completion | Could the workflow close the loop? |
Copyable evaluation scorecard
# CI software evaluation — [product]
Decision workflow:
Peer set / sources:
Reviewers:
| Dimension | Required outcome | Test result | Evidence | Blocker |
|---|---|---|---|---|
| Source fit | | | | |
| Comparability | | | | |
| Exact evidence | | | | |
| Noise control | | | | |
| AI claim boundaries | | | | |
| Review/audit | | | | |
| Decision routing | | | | |
| Export/operations | | | | |
Precision / recall / review time:
Total operating responsibility:
Exit path:
Decision / owner / review date:Require governance proportionate to the decision
NIST's AI Risk Management Framework emphasizes context, governance, measurement, and management. A sales-content watchlist and a board-level pricing signal may require different review and retention. The product should support your risk boundary rather than impose one universal automation level.
Use EXVIV companies, markets, and Evidence Compare to see the public evidence model; EXVIV pricing establishes the current public offer. The AI monitoring architecture supplies a stricter evaluation contract. Google's people-first guidance is relevant when the output becomes public content: usefulness and verifiable sourcing remain mandatory.
Related EXVIV research
- Monitor competitors with AI without hallucinations
- Website-change monitor versus CI platform
- Competitive intelligence for startups
Sources and further reading
- NIST AI Risk Management Framework
- W3C PROV-O
- RFC 9309: Robots Exclusion Protocol
- Google: helpful, reliable, people-first content
Method note
EXVIV is itself competitive-intelligence software. This framework deliberately avoids a self-awarded winner ranking. Buyers should verify every product, including EXVIV, against their sources, decisions, and operating constraints.