How Verinode data is built.

How a Verinode number is built

Every Verinode number in the platform draws on two independent pillars. Neither pillar speaks for itself; triangulation is structural, not optional.

The two pillars combine at read time into the published number. Click any benchmark in the product to see the breakdown (Research vs Network vs Combined) and the trust tier earned by the data behind it.

Trust tiers

Every benchmark in the product carries a trust tier. The tier reflects the underlying evidence: not what the number says, but what the data behind it can support.

Indicative

2 distinct publishers

Two independent methodologies agree within a narrow band, but the third publisher hasn’t joined yet, or one source carries dominant weight. Use directionally; pair with judgment.

Verified Research

3+ distinct publishers, balanced weight

Three or more independent methodologies converge with no single publisher dominating. Defensible in a leadership meeting. The synthesizer’s confidence is high.

Combined

Research + Network

Verified Research layered with live Operator-network data from a sufficient peer cohort. The strongest signal Verinode publishes: your peers’ actual behavior weighted with the broader industry research.

When a metric has fewer than 2 distinct publisher methodologies, we do not publish a Verinode Research benchmark for it. Single-source pass-through is never published as Verinode Research; that would be rebranding someone else’s data, not synthesizing.

Disagreement between sources is surfaced openly: when contributing sources disagree by more than 30% on a metric, the benchmark carries a visible caveat tag.

Peer benchmarks

A peer benchmark is the Network pillar in action: a median and middle range computed from the live, anonymized data of Operators like you. A benchmark is only worth as much as the data behind it, so the rules that decide which data may move a number are stated here in plain terms.

Machine-grounded data only

Verinode benchmarks the data an Operator does not type. Figures are machine-extracted from real documents and connected systems: an invoice, a profit and loss export, a job report, a linked mailbox or accounting sync. Every contributed value is tagged with its origin, and only values with a verifiable origin, or a bounded attested rating such as a one-to-five satisfaction score, are allowed to move a peer benchmark. A number a person keyed by hand with no document behind it is kept for that Operator’s own analysis and never enters a peer cohort.

How the cohort is computed

No benchmark is shown below a floor of distinct Operators, so a single voice is never the benchmark and can never be read back out of one. The floor is not one number: it rises for sensitive financial categories such as margins, wages, and labor cost, where the risk of working a figure back to one business is highest. Each Operator contributes one vote no matter how many records they submit, so volume cannot buy influence. Benchmarks report the median and the middle range rather than the average, so one extreme value, honest or not, cannot drag the number. Demo and test data are held in cohorts fully separate from real Operators. A new Operator’s data joins peer cohorts only after a short maturation window.

Operator identity enters the network only as a one-way cryptographic hash, with no path from a published benchmark back to a contributor. How the network aggregates that data for network-level views, and the full data-protection commitments around it, are documented in the Data Use Policy.

These same rules, the neutral administrator, the aggregation and anonymization, the participation floor, and the historical lag on sensitive figures, are also what keep the benchmark on the right side of competition law. Why that is, and the design principles behind it, are set out in Antitrust and Fair Benchmarking.

Peer ratings

Peer ratings are the verified experience of working Operators with the tools and partners they actually use: a satisfaction score on a vendor, an outcome on a renegotiation, an action taken on a signal. They feed the Peer Intelligence dimension of the Verinode Score and stand alone as their own peer benchmark.

Ratings follow the same protections as every other Network number:

• Bounded and attested. A rating is a structured value (for example, a one-to-five score) tied to a known Operator profile, not free-form text scraped from the open web.
• One Operator, one vote. An Operator cannot move a rating by submitting it repeatedly.
• Cohort floor.A peer rating is shown only once enough distinct Operators have rated, so no individual’s opinion is exposed or identifiable.
• Anonymized. Contributions are hashed before any aggregate is published, and ratings are never sold to carriers or the vendors being rated.

Until a vendor or partner has enough distinct Operator ratings to clear the floor, the peer-rating slot stays empty and its weight is carried by the broader market-trust signals described below. The platform never invents a rating to fill a gap.

The Verinode Score

The Verinode Score is how the Research pillar reads vendor software. It is a composite intelligence score, a single number between 1.0 and 10.0, published in the Restoration Software Intelligence Report. Each of ten dimensions scores on a 1.0 to 5.0 scale, and the composite is the dimension-weighted average scaled to the 1.0 to 10.0 reading scale. It is not a recommendation. It is a structured, reproducible read of what each platform does on the dimensions that matter to Restoration Operators.

A vendor is scored in three steps. The research team determines capability presence against the published capability universe. The Operator Research Panel sets how much each capability matters, so fit outweighs feature volume. And every named vendor has a right of factual correction before and after publication. The Panel and the correction process are described under Research processes and governance below.

The ten dimensions

Each dimension answers one question and carries a fixed weight in the composite. Expand a dimension to see the evidence it draws on.

• Feature Depth

  Does the platform actually do the work?

  17% weight

  Capability presence is determined against a feature universe defined per category. Each capability is recorded as present, partial, or absent. Capabilities are then weighted by Operator Research Panel importance, so the features that matter most to a working Restoration Operator contribute the most weight to the dimension score.

  Evidence considered

  • Vendor product documentation (feature pages, help articles, knowledge base)
  • Demo recordings and direct product testing where access is granted
  • Vendor responses to a structured capability questionnaire issued at the start of every research cycle
  • Customer case studies that confirm specific feature presence in deployment
  • Operator interviews with active users of the platform

• Integration & Ecosystem

  Does it connect to the platforms an operation depends on?

  14% weight

  Integrations are weighted by depth, not count. A native bidirectional carrier integration scores higher than a one-way file export. Hardware partnerships, API openness, and breadth of named integrations all contribute, but the depth of carrier and accounting connectivity is the dominant signal.

  Evidence considered

  • The vendor's published integration page and partner directory
  • Reciprocal listings on the integrating partner's directory
  • Native vs. middleware classification (direct API vs. third-party connector)
  • Carrier-side integration depth (line-item work-order import vs. file attachment support)
  • Hardware partnership disclosures (moisture meters, capture devices, asset tracking)
  • Public availability and accessibility of API documentation

• AI & Innovation

  Is the vendor building toward defendability?

  12% weight

  Forward-looking platform investment is evaluated on two layers. The first is the AI Resilience Benchmark, a category-by-category assessment of how much of each vendor's feature surface could plausibly be replicated by current-generation models without that vendor's incumbent investment. The second is hard signals of vendor R&D commitment.

  Evidence considered

  • AI Resilience Benchmark scoring (1.0 to 5.0 per vendor; surfaced as the AI Disruption Risk indicator on each vendor card)
  • The vendor's published roadmap and recent product announcements
  • Engineering hiring trajectory across public hiring pages
  • Release cadence over the trailing twelve months
  • Patent and technical-paper filings where applicable

• Peer Intelligence

  What do Operators like the buyer actually experience?

  12% weight

  Peer Intelligence captures verified buyer experience from working Operators. The dimension activates as the Verinode platform user base accumulates direct experience data; until activation, its weight is allocated to Market Trust. Member contributions are anonymized before any aggregate is published.

  Evidence considered

  • Verinode platform Operator satisfaction ratings (Members rate the tools they actually use)
  • Cross-Operator pricing data, anonymized and contributed voluntarily
  • Renegotiation outcomes shared by Members
  • Action rates on signals related to a specific vendor
  • Verified experience reviews tied to known Operator profile attributes

• Market Trust

  What does the broader market think?

  10% weight

  Independent third-party ratings, weighted by platform relevance for the category. Volume and twelve-month trend direction matter alongside star averages. Reviews are read for substantive themes such as onboarding friction, support responsiveness, and contract surprises, not just numerical aggregates.

  Evidence considered

  • G2 verified buyer reviews
  • Capterra ratings and review volume
  • Software Advice scores
  • Google business reviews
  • Trustpilot ratings where applicable
  • Better Business Bureau ratings and complaint history
  • Twelve-month review trend direction (improving / flat / deteriorating)

• Cost Position

  Is the vendor fairly priced and honest about it?

  10% weight

  Pricing transparency is the dominant signal. Vendors who require sales engagement to access tier pricing take a scored transparency penalty regardless of what the price turns out to be. Beyond transparency, pricing structure (per-user vs. per-job vs. flat-fee), renewal increase frequency, and contract term flexibility shape the score.

  Evidence considered

  • Published pricing tier visibility on the vendor's site
  • Pricing model disclosure (per-user, per-job, per-transaction, flat-fee)
  • Contract term structure (month-to-month, one-year, multi-year)
  • Renewal increase frequency, sourced from Operator interviews
  • Demo-gating posture (transparency penalty applied where pricing requires sales engagement)
  • Auto-renewal and notice-window terms in published agreements

• Operational Fit

  How does this platform actually land in an operation?

  8% weight

  Operational Fit is the implementation reality, how a platform performs in deployment rather than how it demos. Stated implementation timelines are weighed against Operator-reported actuals. Support model, training depth, mobile UX, and onboarding burden each contribute.

  Evidence considered

  • Vendor-stated implementation timeline
  • Operator-reported actual implementation duration
  • Support model (chat, phone, dedicated CSM, community-only)
  • Training resources (in-product training, knowledge base depth, certification programs)
  • Onboarding complexity (data migration, configuration burden, change-management lift)
  • Mobile UX maturity, weighted by category (more critical for field-heavy software)

• Risk & Compliance

  Can the Operator trust this vendor with operating data?

  8% weight

  The dimension where opacity costs the most. Vendors with documented enterprise-grade compliance posture clear this dimension cleanly. Vendors who claim certifications without substantiation take a scored penalty. Litigation history and contract fairness are the trailing signals.

  Evidence considered

  • SOC 2 Type I / Type II attestation status (verified through the trust portal or direct disclosure)
  • ISO 27001 certification
  • GDPR posture (Data Processing Addendum published, EEA-resident data handling disclosed)
  • HIPAA / PHI handling where applicable
  • Encryption standards (in-transit and at-rest)
  • Uptime SLA published in the master subscription agreement
  • Litigation history, sourced from public court records and regulatory action filings
  • Contract fairness terms (data ownership, indemnification, force majeure language)

• Industry Alignment

  Does this vendor understand Restoration?

  7% weight

  The restoration-fit dimension. Industry Alignment is what separates restoration-native vendors from horizontal field-service platforms with restoration adoption. The signals are concrete: industry association membership, carrier and TPA program participation, restoration customer concentration, and named restoration logos in vendor materials.

  Evidence considered

  • Restoration Industry Association (RIA) member directory listing
  • IICRC (Institute of Inspection, Cleaning and Restoration Certification) affiliation
  • Participation in the major carrier and TPA claims-network program rosters
  • Franchise program supplier or technology partner status
  • Restoration customer share, sourced from operator interviews and case study analysis
  • Named restoration logos in vendor materials (verified, not just stock placements)
  • Restoration-specific feature presence ratio against the category capability universe

• Switching Cost

  How hard is it to leave if things change?

  2% weight

  A small weight by design. Switching Cost is surfaced as a separate, named factor on every vendor card so Operators can read it directly rather than have it disappear into a composite. Multi-year terms, auto-renewal language, data export rights, and hardware lock-in are the four vectors that compound year over year.

  Evidence considered

  • Contract term length disclosure (1-year, multi-year, month-to-month)
  • Auto-renewal language and opt-out notice-window length
  • Early-termination clauses and remaining-payment obligations
  • Data export rights (machine-readable formats, retention windows post-termination)
  • Data ownership clauses (does the vendor claim any rights to Customer Data?)
  • Hardware dependency lock-in (platforms that require proprietary hardware)

The capability universe

Every report defines a capability universe specific to the software category, built from industry-standard taxonomies where they exist, the prior knowledge of working Operators on the Research Panel, and a structured review of vendor materials across the category. The first issue, Job Management Software, defines eleven features and one hundred and ten capabilities. Each capability is scored present, partial, or absent per vendor, and the full list ships with every issue’s methodology appendix so Operators and vendors can audit presence claims against the published universe.

The Verinode Quadrant

The headline composite answers “how good is this vendor overall?” The Quadrant answers a different question: where the vendor sits on the two decisions an Operator is actually making. It uses purpose-built X and Y composites. For Job Management, the X axis is Product Capability (Feature Depth, Operational Fit, Integration & Ecosystem, AI & Innovation) and the Y axis is Restoration Position (Industry Alignment, Risk & Compliance, Market Trust, Cost Position, Switching Cost). The position-heavy Y axis is the structural choice: a horizontal vendor with strong general trust but weak restoration alignment lands in Capable Outsiders, not Industry Leaders.

Industry data and sources

The following sources contribute inputs to Verinode Research and to the platform's industry intelligence, including the peer benchmarks, the Industry Data tab, and the demand-forecasting layer in the Forecasting section. They are listed alphabetically and treated equally on this page. Exact weights and source mixes are not published; that is part of the Verinode Research methodology, calibrated by the Operator Research Panel and reviewed by the Operator Advisory Council.

A source listed here is not necessarily contributing to every metric. Each source covers a different slice of the Restoration intelligence surface.

• ADP Research

  Comp and labor

  ADP National Employment Report

  ADP payroll-data-derived monthly employment and wage trends. Operational data from the largest payroll processor; methodology disclosed.

  Cadence: monthlyCoverage: USPublisher site →

• AHRI

  Trade directory

  AHRI Certified Equipment Directory

  Air-Conditioning Heating and Refrigeration Institute equipment certification directory. Manufacturer-attested specs; deterministic and verifiable.

  Cadence: continuousCoverage: US · CAPublisher site →

• AM Best

  Carrier data

  Financial Strength Ratings

  AM Best carrier financial strength ratings. Trust signal for carrier program decisions.

  Cadence: continuousCoverage: USPublisher site →

• AM Best

  Carrier data

  P&C market segment commentary (premium growth)

  AM Best property & casualty market commentary, year-over-year direct premium written growth. Context for carrier pricing pressure on operators. Powers the Forecasting demand outlook (distinct from the AM Best Financial Strength Ratings used for carrier trust signals).

  Cadence: annualCoverage: USPublisher site →

• Associated Builders and Contractors

  Industry consultant

  Construction Backlog Indicator

  ABC Construction Backlog Indicator — average months of work under contract. A capacity-tightness signal: healthy backlogs mean subcontractor and trade availability is constrained. Powers the Forecasting demand outlook.

  Cadence: monthlyCoverage: USPublisher site →

• BLS

  Government

  Occupational Employment and Wage Statistics

  U.S. Bureau of Labor Statistics annual wage data by occupation × state × metro. Already used by the Verinode compensation benchmarker.

  Cadence: annualCoverage: USPublisher site →

• BLS

  Government

  Employment Cost Index

  BLS quarterly compensation cost index. Used for wage-inflation deflation and forward comp projections.

  Cadence: quarterlyCoverage: USPublisher site →

• BLS

  Government

  Survey of Occupational Injuries and Illnesses

  BLS annual workplace injury and illness incidence rates by industry. Drives Verinode safety benchmarks.

  Cadence: annualCoverage: USPublisher site →

• BLS

  Government

  Producer Price Index (construction inputs)

  Bureau of Labor Statistics Producer Price Index for construction inputs. A margin-pressure signal: rising rebuild input costs relative to general inflation. Powers the Forecasting margin note.

  Cadence: monthlyCoverage: USPublisher site →

• BLS

  Government

  Consumer Price Index

  Bureau of Labor Statistics Consumer Price Index, all items. The general-inflation baseline operators measure cost increases against. Powers the Forecasting economic context.

  Cadence: monthlyCoverage: USPublisher site →

• C&R Magazine × KnowHow

  Industry survey

  2026 State of the Industry Report

  Annual restoration industry self-report survey covering revenue, AR, TPA reliance, AI adoption, workforce, M&A, and vendor preferences. Free with email gate. Methodology not fully disclosed; sample size not published. Covers FY2025 calendar year.

  Cadence: annualCoverage: US · CAPublisher site →

• C&R Magazine × KnowHow

  Industry survey

  2025 State of the Industry Report

  Annual restoration industry self-report survey. Covers FY2024 partial-year data.

  Cadence: annualCoverage: US · CAPublisher site →

• C&R Magazine × KnowHow

  Industry survey

  2024 State of the Industry Report

  Annual restoration industry self-report survey. Covers FY2023 partial-year data.

  Cadence: annualCoverage: US · CAPublisher site →

• C&R Magazine × KnowHow

  Industry survey

  2025 State of the Industry Pulse Check

  Mid-year recalibration survey from the SOTI authors. Smaller sample than the full annual; lower validity weight.

  Cadence: point in timeCoverage: US · CAPublisher site →

• CFMA

  Industry consultant

  Construction Industry Annual Financial Survey

  Construction Financial Management Association annual financial benchmark covering AR aging, days sales outstanding, and balance-sheet ratios. Construction-industry breadth (broader than restoration), used for AR / collection benchmarks where restoration cuts are sparse.

  Cadence: annualCoverage: USPublisher site →

• Cleanfax

  Industry survey

  2026 Restoration Benchmarking Survey Report

  Cleanfax / ISSA Media annual restoration benchmarking survey covering gross margins, insurance payment wait time, starting wages, service-line profitability, pricing-tool adoption, AI adoption, turnover, and growth outlook for calendar year 2025. Respondents skew toward smaller and cleaning-adjacent firms vs the restoration-vertical SOTI sample. Methodology not disclosed; sample size not published; results not based on audited financial statements. Independent of C&R Magazine × KnowHow, so it triangulates SOTI on shared metrics.

  Cadence: annualCoverage: US · CAPublisher site →

• Freddie Mac

  Government

  Primary Mortgage Market Survey (PMMS)

  Freddie Mac 30-year fixed mortgage rate survey. A discretionary-remodel demand signal; insurance-driven restoration is less exposed than retail reconstruction. Powers the Forecasting economic context.

  Cadence: weeklyCoverage: USPublisher site →

• IBBA

  Broker research

  Market Pulse Quarterly Report

  International Business Brokers Association quarterly small-business M&A survey. Broker self-reported; lower validity weight reflects limited methodology transparency.

  Cadence: quarterlyCoverage: USPublisher site →

• Indeed Hiring Lab

  Comp and labor

  Posted Wage Trends

  Indeed posted-wage tracker. Real-time leading indicator of comp shifts vs lagging BLS data. Methodology partially disclosed.

  Cadence: monthlyCoverage: US · CAPublisher site →

• Insurance Information Institute

  Carrier data

  Insurance industry aggregate statistics (claims frequency)

  Insurance Information Institute aggregate homeowners and commercial property claim-frequency statistics. A demand-side signal: rising claim frequency points to more work entering the channel. Powers the Forecasting demand outlook.

  Cadence: annualCoverage: USPublisher site →

• JD Power

  Carrier data

  U.S. Property Claims Satisfaction Study

  JD Power consumer-side property claims satisfaction rankings. Independent triangulation of carrier reputation. Methodology partially proprietary.

  Cadence: annualCoverage: USPublisher site →

• NACM

  Industry consultant

  Credit Managers' Index

  National Association of Credit Management monthly survey of credit conditions across construction and other sectors. Useful for AR / collection-velocity directional signals.

  Cadence: monthlyCoverage: USPublisher site →

• NAIC

  Carrier data

  Market Conduct Annual Statement / Complaint Index

  National Association of Insurance Commissioners regulatory data. Carrier-level complaint ratios and claim payment timeliness. Mandatory reporting, independent of carrier marketing.

  Cadence: annualCoverage: USPublisher site →

• NOAA

  Weather

  Storm Events Database

  NOAA observational database of every CAT event with location and damage estimate. Drives revenue-forecasting and CAT-corridor signals.

  Cadence: monthlyCoverage: USPublisher site →

• NOAA Climate Prediction Center

  Weather

  Seasonal hurricane and climate outlooks

  NOAA CPC pre-season Atlantic named-storm forecasts and seasonal outlooks. A forward demand signal for coastal operators (pre-position equipment and subcontractor capacity ahead of peak season). Powers the regional Forecasting demand outlook.

  Cadence: seasonalCoverage: USPublisher site →

• NOAA NCEI

  Weather

  Billion-Dollar Weather and Climate Disasters

  NOAA National Centers for Environmental Information count of billion-dollar weather and climate disasters. A demand-side signal for large-loss volume by region. Powers the Forecasting demand outlook.

  Cadence: monthlyCoverage: USPublisher site →

• RIA

  Industry consultant

  RIA Cost of Doing Business 2025

  Restoration Industry Association annual benchmark on operating costs, margin, COGS, and AR. Accountant-validated submissions; methodology disclosed publicly. Covers FY2024 data.

  Cadence: annualCoverage: USPublisher site →

• SHRM

  Industry survey

  SHRM Benchmark Surveys

  Society for Human Resource Management annual benchmark surveys covering compensation, benefits, turnover, healthcare coverage, and burnout policy practices. Methodology disclosed; sample sizes published.

  Cadence: annualCoverage: USPublisher site →

• Swiss Re Institute

  Broker research

  sigma — natural catastrophe insured losses

  Swiss Re sigma series on global natural-catastrophe insured losses. A demand-side signal: rising insured losses point to more mitigation and rebuild demand. Powers the Forecasting demand outlook.

  Cadence: annualCoverage: US · CAPublisher site →

• U.S. Census Bureau

  Government

  Construction Spending (Value of Construction Put in Place)

  Census monthly residential vs commercial put-in-place values. Independent triangulation of restoration service-mix shifts.

  Cadence: monthlyCoverage: USPublisher site →

• Verinode | Research

  Verinode Research

  Verinode Research evaluations and methodology studies

  Verinode | Research is the publication arm of the Restoration Operator Trust. Outputs include adjuster scorecard methodology, per-carrier days-to-pay retrospectives, canonical-COA mapping evaluations, vendor ROI evaluations, and process supplement-to-SOP anchor evaluations. All Research outputs are reviewed by the Operator Research Panel before contributing weight.

  Cadence: continuousCoverage: US · CA

New sources are added through the Operator Research Panel approval process described below. When a new source contributes weight to a published benchmark, it appears here before its data flows.

The Restoration COA Standard

Margin benchmarks only mean something if everyone’s books are read the same way. Two Operators of identical size and identical real profitability can report gross margins fifteen points apart, simply because one books field labor, job vehicles, and owned drying equipment as cost of goods sold while the other parks them in overhead. Both are internally consistent. Neither is wrong on its own terms. But a peer median built from books that disagree on what counts as a job cost compares nothing to nothing.

The Restoration COA Standard is the canonical chart of accounts Verinode maps every Operator’s raw financials onto before any comparison is computed. It does not change how you keep your own books. It is a lens applied on top of whatever you already use, so that the benchmark you see is built from financials that have all been normalized to one structure. It is anchored on the RIA Cost of Doing Business structure, IRS Schedule C contractor accounting, the insurance industry’s own direct-cost-plus-overhead pricing model, and the account defaults of the major restoration ERPs.

The COGS versus SG&A boundary

The single most consequential rule, and the one Operators most often diverge from, is job-traceability: a cost belongs in COGS when it is incurred to deliver a specific job or scales directly with job volume, and in SG&A when it would exist whether or not the next job came in. Applied to the items most commonly booked the other way:

• Field labor is COGSwhenever the time is job-traceable, not SG&A personnel.
• Owned-equipment depreciation is COGS. A dehumidifier you own and run on jobs is economically identical to one you rent.
• Job-dedicated vehicle cost is COGS. Fuel and maintenance on trucks that exist to get crews to losses.
• Bad debt is an SG&A expense, not a reduction of revenue, so gross revenue stays comparable across Operators.

When these are booked the other way, reported gross margin reads higher than the economics support. That gap does not survive contact with a lender, a broker, or an acquirer, all of whom reconstruct the normalized figure anyway. The Standard does that reconstruction up front, and transparently: every line shows what it was mapped to and how confidently, and any Operator can correct a mapping for their own books.

Correctness versus outlier

Verinode draws a hard line between two things that look alike. An accounting-correctness finding is objective: books mapped with high confidence that still hold direct costs in overhead diverge from the Standard, and we say so plainly. A distribution outlier is not a correctness claim: a ratio far from the peer median can reflect a real structural difference, such as heavy subcontracting or an all-reconstruction mix, so we treat it as a question. And we never tell an Operator their books are wrong on the strength of a mapping we are not confident about. When too much of a profit and loss maps at low confidence, the finding is framed as “confirm this” and routed to the Operator’s own review, because the symptom could be ours rather than theirs.

The Restoration COA Standard is versioned and reviewed with the Operator Advisory Council. This describes version 1.0. It governs only how Verinode reads your financials for comparison; it is not accounting or tax advice.

Research processes and governance

Operator Research Panel

The Operator Research Panel is the body of working Restoration Operators that calibrates Verinode Research. The Panel ratifies the validity-scoring framework, reviews drift signals on published benchmarks, approves new sources before they contribute weight, and sets how much each software capability matters for the Verinode Score. The Panel does not rate vendors; vendor capability is the research team’s responsibility. The Panel rates capabilities, telling the methodology which features matter most to running a Restoration business, which is what lets the score reward fit over feature volume.

Membership is by application, capped per cycle, and reviewed annually. Members commit to no vendor relationships that would compromise scoring independence. The Panel is distinct from the Operator Advisory Council, which reviews Verinode’s data-use policy and publishes its concerns.

Quality controls

Right of factual correction

Every vendor named in a publication has a right of factual correction in writing. Corrections that produce a verified change are applied, the affected score is recomputed, and the change is logged in the public correction log. The original publication is updated in place; the log preserves the prior reading and the rationale.

Methodology version control

Every Issue cites the methodology version under which it was produced. Methodology changes between issues are recorded with rationale. A vendor's score moving between issues is therefore decomposable into methodology-driven movement and product-driven movement.

Pre-publication review

Vendors with structured documentation responses receive a pre-publication review of their dimension scores. Factual corrections raised during review are processed before publication. Editorial commentary is reviewed by the Operator Research Panel methodology review subgroup.

Source citation

Every score-affecting datapoint carries a source category and a freshness timestamp. The published methodology lists the evidence base for each dimension. Operators evaluating a specific score can request the source-level breakdown for that vendor.

Resistant to gaming

No vendor can pay for score weighting, and no score-affecting figure rests on a single source. Independent sources must converge before a datapoint carries weight. Where a published figure draws on operator-contributed data, that data is admitted only when it is machine-grounded, counted once per operator, and clears a distinct-operator floor, so neither a vendor nor a single operator can move a reading by submitting selectively.

Independence commitments

Three commitments, published in full and binding:

No vendor pay

No vendor pays for placement, sponsorship, or scoring influence. No carrier commissions the methodology. No franchise group commissions a vendor’s score. The methodology is funded by the Verinode platform subscription model and Operator membership.

Operator data is never sold to carriers

Operator data contributed to Verinode is anonymized before any aggregate is published or shared. The data use policy is published in full and binding. It cannot be quietly changed without notice to contributors.

Reproducible next quarter

The same data and methodology applied next quarter produces the same number, plus or minus the changes the vendors themselves have shipped. Scores move when the product moves, not when the editorial moves.

Contact

• Methodology questions and factual corrections: [email protected]
• Operator Advisory Council inquiries: [email protected]