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Methodology & accuracy. How Baseline scores, and how we test it.

One fixed rubric, three lenses, thirty-plus sub-dimensions, applied identically to every company so the scores are comparable across the whole universe.

Lens 01

Business Quality

How durable and valuable the business itself is.

  • Mission criticality
  • Barriers to switching
  • Pricing power
  • End-market quality
  • Unit economics
  • Management quality
Lens 02

Competitive Dynamics

Where the company sits against the field, and which way it is moving.

  • Positioning vs named peers
  • Structural forces
  • Share and momentum
  • Threat of substitution
  • Relative advantage
Lens 03

Risk

What could break the thesis. 5 means low, well-contained risk.

  • AI / AGI exposure
  • Other AI-driven risks
  • Red flags & accounting
  • Concentration
  • Regulatory & geopolitical

How scoring works

Polarity is consistent.

5 is most favourable on every axis, including Risk, where 5 means low, well-contained risk. Scores roll up hierarchically: sub-dimensions into dimension scores into an overall read. A score of 0 means no data, never a bad score.

Source-level grounding.

Rationales are built from primary filings and earnings transcripts. We are extending this to inline, page-and-line citations so every claim traces to the exact document it came from.

Accuracy testing.

Outputs are tested against the underlying filings across marquee, mid-cap and long-tail names, and emerging-market long-tail names are flagged as lower-confidence rather than presented with false certainty. Every verification pass is published below in full — including every error it found.

Accuracy verification

Independent verification passes over live product output, graded claim by claim against primary sources. Each report is reissued as the corpus grows.

Every score Baseline publishes rests on a written rationale, and every rationale contains checkable factual claims. This document sets out how those claims were tested, what the testing found, and every error it surfaced, named.

What we tested

21 companies, 97 hard factual claims, drawn from live rationale panels and verified individually against a primary or authoritative source. Coverage was deliberately split across the universe rather than concentrated in well-covered names: 9 marquee developed-market companies and 12 emerging-market and long-tail names, where public data is thinner and error risk is highest. Analyses carried generation dates between March and July 2026.

A hard claim is one that can be checked and can be wrong: a figure, a date, a named transaction, a stated market position. Judgements are not claims and are not counted.

Method

Claims were pulled from live product output through the results API, not from a curated sample prepared for the test. Each was traced to a primary source where one exists, and to an authoritative secondary source where it does not, then graded against what that source states. Grading was done to the source, not to plausibility.

An earlier 18-claim pass from 3 July was retired rather than added to this corpus. It was not sourced per claim, and three of its names were re-verified into this corpus, so including it would double-count and import a shallower standard of checking. This corpus stands alone.

Grading scale

GradeMeaning
VerifiedThe source states what the rationale states.
ImpreciseDirectionally right, but outside what the source supports. Overstated ranges, geography or period mismatches, part presented as whole.
ContradictedThe source states something materially different.
UnverifiableNo source located that confirms or refutes. Counted, not scored.
DatedCorrect at the generation date and since moved. Live market data only.

Results

97
claims checked across 21 companies
73
verified against source
12
imprecise
4
contradicted on durable facts
0
fabricated figures

Every analysis carries a generation date, so live market data is read as of that date rather than as current. Excluding the 3 unverifiable claims and the 5 market-data claims graded dated on that basis, 89 durable checkable claims remain. Of those, 73 verified (82%), 12 were imprecise (13%) and 4 were contradicted (4%).

The finding that matters most is the absence of fabrication. Not one claim in this corpus was invented. The errors that exist are of a different and less damaging kind: real facts attached to the wrong entity, real figures given the wrong label, real ranges overstated at the edges. Every one is listed below.

The corpus

CompanyTickerTierVerifiedImpreciseContradictedUnverif.Claims
Novo NordiskNOVO-B.DKMarquee3115
Naver035420.KRMarquee415
NVIDIANVDA.USMarquee516
Toyota7203.JPMarquee314
ASMLASML.NLMarquee415
Samsung005930.KRMarquee33
LVMHMC.FRMarquee44
AmazonAMZN.USMarquee55
AstraZenecaAZN.GBMarquee516
China Everbright0257.HKEM / long tail2125
Great Wall1210.TWEM / long tail213
HUTCHMED0013.HKEM / long tail11
CK Hutchison0001.HKEM / long tail3126
Bharti AirtelBHARTIARTL.INEM / long tail2114
Uni-President1216.TWEM / long tail44
Taiwan Cement1101.TWEM / long tail415
PCCW0008.HKEM / long tail55
Bajaj FinanceBAJFINANCE.INEM / long tail314
HDFC BankHDFCBANK.INEM / long tail2215
Galaxy Entertainment0027.HKEM / long tail66
WiseTechWTC.AUEM / long tail314
21 companies73127395

A further 2 claims were graded dated and are not attributed to a single company. Total 97.

Every content error

These are all 4 durable content errors the testing found. We publish them in full because an accuracy claim that names no errors is not an accuracy claim.

Novo NordiskNOVO-B.DKInternal duplicate figure
Baseline said
The Catalent fill-finish site purchase is stated as both $11bn and $11.7bn within the same rationale.
The source says
Novo Nordisk's own announcement gives an upfront payment of $11bn. The $11.7bn figure is unsupported.
Why it matters
Two values for one transaction in one panel. A reader checking either figure finds the other contradicts it.
CK Hutchison0001.HKWrong entity
Baseline said
The £10.5bn sale of UK Power Networks, and the HK$14.5bn gain on it, is attributed to CK Hutchison Holdings.
The source says
The sale belongs to a CK Infrastructure-led consortium. The gain is CKI's, not CKH's.
Why it matters
A real transaction attached to the wrong listed company. A CKH holder catches this immediately.
HDFC BankHDFCBANK.INWrong metric label
Baseline said
A ₹22 lakh crore figure is described as the balance sheet. Elsewhere in the same rationale the balance sheet is ₹43 trillion.
The source says
₹22 lakh crore is the combined loan and advances book at the July 2023 merger. ₹43.6 lakh crore is the balance sheet at March 2026.
Why it matters
The underlying figures are real and correctly sourced. The label on one of them is wrong, and the two are inconsistent with each other.
WiseTechWTC.AUOverstated total
Baseline said
The Blume and Envase acquisitions are described as "over $1bn combined".
The source says
The two transactions total approximately US$644m.
Why it matters
An arithmetic overstatement on real, correctly identified transactions.

Imprecise claims

Imprecise claims are not fabrications. In each case the underlying fact is real and correctly sourced; the figure sits slightly outside what the source supports. The pattern is consistent and worth stating plainly, because it points at the fix.

CompanyClaimSourcePattern
Novo NordiskSemaglutide weight loss stated as 15–20%Semaglutide is around 15%. The 20% figure belongs to tirzepatide.Overstated range
ToyotaElectrified vehicles "~47% global, mid-2025"47% is the US figure for that period.Geography mismatch
Bharti AirtelIncremental EBITDA margin ~80%Consolidated incremental margin is around 71%.Overstated figure
CK HutchisonPorts handle ~11% of world container cargoThe company states 9–10%.Overstated range
NaverA Holdings stake ~49%The stake is 50%.Rounding in the wrong direction
HDFC BankHDB Financial IPO ₹10,000 croreThat is the offer-for-sale portion of a ₹12,500 crore issue.Part presented as whole

Four recurring shapes: overstated ranges, geography or period mismatches, currency mislabels, and a part presented as the whole. None involves invented information.

Error classes

ClassDefinitionFoundNote
Fabricated figureAn invented number attached to a real event, or a figure the source documents do not contain.1 instance in the full corpus, in the excluded name. Zero in the published corpus.Highest severity. This is the failure mode the product exists to rule out.
Wrong entityA real fact attached to the wrong company, typically within a group structure.1 instance.High severity in conglomerates and holding structures.
Wrong metric or period labelA real, correctly sourced figure presented under the wrong label.2 instances.Medium severity. The figure is right; the description is not.
OverstatementA real set of facts summed or characterised too generously.1 instance.Medium severity. Detected by arithmetic against the components.
Imprecise claimA figure or range slightly outside what the source supports, without invention.12 instances.Low severity individually. The pattern matters more than any single case.
Dated market dataLive price, market cap, and multiple fields that were correct at generation and have since moved.2 graded as dated, plus 3 contradictions reclassified.Excluded from the error rate, conditional on the generation date being displayed with the year on every panel.

What this does not claim

The corpus is 97 claims across 21 companies out of a universe above 3,600. It is evidence, not proof, and it is not a statistical guarantee about any company we have not tested. The emerging-market long tail is where our confidence is lowest, which is why it is over-represented here rather than avoided.

Baseline surfaces and screens candidates on qualitative grounds. It does not predict returns, and it is not a substitute for an analyst's own modelling, valuation or final judgement. Every error listed above is tracked as an open engineering item with a named fix, and this document is reissued as the corpus grows.

Corpus verified 15 July 2026 · Document generated 23 July 2026