The Karnataka High Court has disposed of a batch of transfer pricing appeals that had been pending since the Supreme Court's 2023 judgment in SAP Labs sent them back for fresh consideration. The appeals turned on a familiar but consequential question: how much latitude a High Court has to examine the comparability filters, such as turnover bands, related-party-transaction (RPT) thresholds and export-ratio cutoffs, that determine which companies are shortlisted for TNMM or CPM benchmarking.
Under the Karnataka High Court's earlier Softbrands precedent, comparability disputes, including which companies get excluded and which filters apply, were treated as pure questions of fact on which a High Court would not ordinarily interfere. SAP Labs removed that immunity. In this month's ruling, the Court held that it can examine whether the selection of comparables and the choice of filters was made judiciously and on the basis of relevant material, though it will continue to defer to the Tribunal unless the process is shown to violate Section 92C or Rule 10B, or is otherwise perverse. On the facts, the Court upheld a Rs 200 crore upper turnover filter as rational given the size, brand value and economies of scale of the tested party, and held that a 15% RPT filter is ordinarily appropriate. A higher threshold of 20% or 25% is permissible only where the TPO records a specific finding explaining why comparables meeting the lower threshold were unavailable. The Court also held that the tolerance band of plus or minus 5% under Section 92C is not an automatic standard deduction from the arithmetic mean; it is a threshold that determines when an adjustment is required, not a discount to be applied as a matter of course.
In the same week, the Delhi ITAT reached a comparable conclusion in GE India Industrial's case. The Tribunal rejected the TPO's rigid 50% turnover filter and held that functionally comparable companies cannot be excluded on size grounds alone; comparability must be tested against the functions, assets and risks framework under Rule 10B, not against an arbitrary turnover band. Read together, the two rulings direct TPOs and taxpayers to support each filter with a documented, fact-specific rationale tied to the taxpayer's actual FAR profile, rather than applying it as a default setting.
This has a direct bearing on how benchmarking is now conducted. Commercial and in-house benchmarking tools typically ship with default screening logic: an RPT filter set at 15% or 25%, a turnover band expressed as a multiple of the tested party's revenue, an export-ratio cutoff for captive units. Agentic benchmarking tools, which some practices have begun piloting, go further: they select the filter to apply, often based on the vendor's training data or built-in heuristics, rather than on a documented judgment call by the practitioner running the analysis.
Read against the Karnataka High Court's ruling, the choice of filter thresholds, not merely the choice of method, is now a part of the TP file that could attract closer scrutiny. A TPO or a taxpayer's advisor who cannot explain why a tool applied a 25% RPT filter instead of 15%, beyond the fact that this is the platform's default, may find that position harder to sustain in a perversity challenge than it would have been before SAP Labs.
This raises a practical question for practitioners advising on AI adoption in benchmarking. Building an auditable, tool-agnostic justification for each filter choice adds documentation overhead, but it may also be the record that a TPO or Tribunal now expects to see. Conversely, the push toward faster, high-volume automated benchmarking runs could encourage greater reliance on unexamined defaults at the same time as the case law raises the cost of doing so. Industry commentary describing 2026 as the year in which touchless compliance becomes a practical necessity has largely framed that shift around GloBE Information Return and Form 6765 deadlines rather than TP substance, which suggests that current AI-in-tax roadmaps are built primarily for data processing rather than for the judgment-intensive task of justifying filter choices that these rulings have placed back on the table.
For practitioners, the immediate implication is procedural rather than strategic: any benchmarking filter applied by a tool, whether commercial or agentic, should be accompanied by a documented rationale linking the threshold to the taxpayer's FAR profile, so that the choice can be defended as a reasoned exercise rather than a software default if it is questioned.
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