From Annual Documentation to Continuous Monitoring: What the Nexdigm-infer360 Alliance Signals for TP Practice

Transfer pricing documentation in India is built after the fact. Benchmarking studies are prepared once a year, local files are frozen at a point in time, and the arm's length position a taxpayer defends before a TPO reflects a snapshot taken months or years after the transactions occurred. The analysis need not be wrong, but it is backward-looking by design: assembled once the business has already priced the transaction, to justify a decision already taken.

A new generation of AI-native platforms is pitching something different: continuous, always-on monitoring of intercompany pricing through the year, rather than faster annual documentation. Nexdigm, the Mumbai-headquartered advisory firm, has recently announced an alliance with infer360, a Singapore-based AI platform built by former Big Four transfer pricing partners. The stated aim is to help clients move from periodic compliance to continuous transfer pricing management: automating documentation, running ongoing risk monitoring, and maintaining an audit-ready trail through the year rather than reconstructing one afterward.

The marketing language is unremarkable; most vendors in this space now describe themselves as AI-native. What is worth noting is that a serious Indian-origin advisory practice with established APA, controversy and operational TP credentials is putting its own resources behind the view that continuous monitoring, rather than faster annual studies, is where the market is heading.

This sits alongside a parallel move on the US side of the industry, where an established transfer pricing technology vendor's benchmarking and documentation AI suite continues to draw trade-press attention as a template for how mid-market and boutique practices are narrowing the technology gap with the Big Four.

The relevance for practitioners lies in the kind of evidentiary record these platforms are built to produce: a clean, time-stamped account of the methodology used and how it performed against comparables through the year. That is the kind of record Indian tribunals have shown they will act on.

In a Delhi ITAT ruling this month in Honda R&D (India)'s case, the taxpayer faced a ₹50.20-lakh adjustment for AY 2020-21 and pointed the Tribunal to a TPO order for the following assessment year, AY 2021-22, in which the department had accepted the identical benchmarking position it was disputing for the earlier year. The Tribunal directed relief consistent with the later year's accepted treatment.

This is not an isolated result. Indian tribunals have repeatedly leaned on a consistency principle when a TPO's own later-year acceptance undercuts an earlier-year adjustment. A continuous-monitoring platform, by construction, produces the kind of multi-year, methodology-stable record that makes this argument easier to run: the more granular and continuous a taxpayer's own TP data trail becomes, the stronger its consistency-principle arguments are likely to be.

This also raises a question about asymmetry between well-resourced multinationals and the tax administration. If sophisticated taxpayers build continuous, audit-ready monitoring systems while the department still audits largely on an annual, backward-looking cycle anchored to Form 3CEB-style filings, an information and preparedness gap could open up between the two sides of the table.

India's income tax apparatus already runs AI-based risk assessment for scrutiny selection at the return level, and the CBDT's APA programme leans heavily on post-agreement monitoring of critical assumptions for bilateral agreements. From there, it is a reasonable question whether the department should build its own continuous-monitoring capability specifically for TP, to match rather than only react to the tooling that advisory firms are now selling to taxpayers.

There is also a discovery-related question worth flagging. As continuous monitoring platforms generate richer contemporaneous records than the old annual documentation model, those records could prove double-edged: they may help taxpayers win consistency arguments in years like this one, but they could also give revenue authorities a more granular trail to probe when a deviation does appear.

Neither the vendors marketing these platforms nor the tribunals applying the consistency principle have had occasion to address this tension yet. As continuous monitoring tools become more common, practitioners advising on TP documentation strategy will need to weigh the benefit of a stronger contemporaneous record against the risk that the same record gives the department more material to examine when a taxpayer's position shifts from one year to the next.

Karnataka HC's SAP Labs Remand Ruling and the Case for Reasoned Comparability Filters in AI Benchmarking

The Karnataka High Court's 2018 decision in Softbrands treated the selection and rejection of comparables as a pure question of fact, which left the ITAT as the final word on comparability disputes; High Courts could not reweigh turnover filters, RPT thresholds, or FAR analyses under Section 260A. The Supreme Court's ruling in SAP Labs India v. ITO rejected the idea that every ALP determination is immune from scrutiny and sent a batch of pending appeals back to Karnataka for fresh consideration on the merits. How the High Court would apply that reopened scrutiny remained unclear until its recent consolidated ruling.

In its 28 August 2026 consolidated ruling, the Karnataka High Court restated the SAP Labs test and attached specific numerical thresholds to it. A ₹200-crore upper turnover filter was held to be rational and logical, not because the statute prescribes it, but because size differences plausibly correlate with brand value, bargaining power, and economies of scale. A 15% RPT (related-party-transaction) filter was endorsed as the ordinary default, with a departure to 20% or 25% permitted only where the authority records a specific, reasoned finding that comparables meeting the lower threshold are scarce. The Court also held that the ±5% tolerance range under Section 92C is a threshold, not a standing deduction that a taxpayer can claim once the transaction price exceeds it. Within days, at least two more Karnataka HC benches cited this ruling to dispose of pending Revenue appeals, including one that upheld the exclusion of Bodhtree Consulting as a comparable by applying the same 200-crore ceiling.

Barely two weeks after the Karnataka HC ruling, ITAT Delhi reached a different outcome in GE India Industrial v. DCIT, rejecting the Revenue's use of a rigid 50% turnover band to strike out lower-end comparables and criticising the DRP for being "guided by a general opinion" rather than the statutory FAR-based comparability test under Rule 10B. Read together, the two rulings point to a common test: a filter survives scrutiny only if the tribunal or officer applying it shows a specific, reasoned link between the chosen threshold and functional comparability on the facts of the case. A 200-crore ceiling with a size-based rationale passes. A blanket 50% band applied mechanically, without engaging the functional analysis, fails.

These rulings have implications for how AI tools are used in TP documentation. Benchmarking platforms, whether Big 4 proprietary tools or newer AI-native SaaS products, increasingly automate comparable searches using statistical filters such as turnover bands, RPT thresholds, and quartile screens. A vendor could hard-code the thresholds recently upheld, such as 200 crore and 15%, as default parameters on the assumption that a threshold accepted once will be accepted again.

That approach risks the failure mode both rulings identify from opposite directions: a threshold applied without a documented, case-specific rationale is what gets struck down, whether the Revenue or the taxpayer relies on it. An AI benchmarking tool that outputs a comparables set with a turnover filter applied, but no accompanying explanation tying that filter to the tested party's specific brand value, IP ownership, or scale economics, is building a study that looks defensible today and may not survive the next remand.

TP teams, and the vendors building the AI layer underneath them, may need to consider whether benchmarking software should generate a reasoned-finding log alongside every filter it applies: not merely that a comparable was excluded because turnover exceeded 200 crore, but a documented basis for why that threshold was chosen for the specific tested party, FAR profile, and data set. The Karnataka High Court's ruling signals that courts are now willing to test that reasoning on the merits rather than treat comparable selection as an unreviewable factual finding.

For AI-assisted TP work, the practical advantage may lie less in the speed of comparable identification than in the tool's capacity to produce explainable, fact-specific justification that a Division Bench applying the SAP Labs test would accept.

ITAT Hyderabad Extends BAPA Margin to Non-Covered AE Transactions on FAR-Identity Grounds

A Bilateral Advance Pricing Agreement negotiated with the CBDT and a foreign competent authority, usually the US given where most AE relationships sit, can cover more than 90 percent of a captive service provider's international transactions. The remainder, revenue earned from AEs in other jurisdictions such as the UK or Singapore, falls outside the agreement's scope. This residual portion must be benchmarked afresh each year and remains open to TPO scrutiny, since the certainty negotiated under the BAPA does not formally extend to it.

The ITAT Hyderabad Bench addressed this fact pattern in Synchrony International Services Private Limited v. ACIT, an order pronounced on 30 March 2026. Synchrony had a BAPA with the US covering roughly 95.75 percent of its revenue as a captive ITeS provider. The remaining 4.25 percent came from non-US AEs and sat outside the agreement. The TPO did not conduct a separate benchmarking exercise for this residual portion and proposed an adjustment on it. The Tribunal held that a BAPA margin negotiated for one country's AEs cannot be restricted to that country alone where the functions, assets and risk (FAR) profile of the non-covered AE transactions is identical, and directed the TPO to apply the BAPA rate across all three assessment years under appeal.

The outcome favours taxpayers: if a captive performs identical back-office work for a UK entity and a US entity, the pricing need not differ merely because only one entity's AE relationship is covered by the signed APA. But the ruling shifts the locus of the next dispute rather than removing it. The burden of proof on non-covered transactions is relocated, not eliminated.

Instead of a fresh comparables search each year, the taxpayer must now build and defend a case that the FAR profile across AEs is genuinely identical, covering service descriptions, decision rights, risk allocation, contractual terms, and reporting lines. This is a different exercise from a standard benchmarking study, and in some respects a harder one, because there is no external database of third-party comparables to draw on. The comparison is intra-group, AE to AE, and the evidence must come from internal documentation: service agreements, organisation charts, SLAs, cost allocation keys, and correspondence showing who directed the work.

Current AI-enabled TP tools, including benchmarking platforms that automate comparable searches and NLP tools that flag inconsistent documentation, are built mainly to address a different problem: finding and screening third-party comparables faster, or checking a local file against a jurisdiction's formatting requirements. Few, if any, are designed to assess whether AE-A's functional profile is identical to AE-B's functional profile within a single multinational group. That is a more bespoke comparability question, closer to internal audit than to database screening, and it now sits at the centre of the scope this ruling has opened.

India's APA programme has crossed 1,034 agreements since inception, with 284 bilateral, a population of taxpayers who may now have grounds to extend negotiated certainty to residual AE transactions. Doing so will require a FAR-identity case capable of withstanding scrutiny on points such as a contractual clause, headcount difference, or decision-rights nuance that could break the claim of identity.

CBDT has not yet addressed BAPA scope in this context. The Board has previously issued administrative clarifications where APA and Safe Harbour regimes interact awkwardly; the March 2026 Office Memorandum permitting taxpayers with UAPAs spanning the Safe Harbour transition to opt into the new regime for later years is a precedent for this kind of housekeeping.

A similar clarification on BAPA scope, an administrative mechanism to formally extend or fast-track non-covered AE transactions where FAR identity is not seriously disputed, could save taxpayers from re-litigating this question bench by bench, year by year, across every captive with a partial BAPA. Absent such clarification, Synchrony is likely to become a citation that captives with partial-scope BAPAs raise routinely, and one that TPOs will need to engage with on the merits rather than dismiss at the threshold.

For practitioners advising captives with partial-scope BAPAs, the practical task is to assemble FAR-identity documentation now, before the TPO raises the issue, rather than treat the Tribunal's reasoning as self-executing.

TR 2026/2: Australia's Software Royalty Ruling and Its Implications for Groups with Indian Operations

On 4 September 2026, the Australian Taxation Office finalised Taxation Ruling TR 2026/2, along with a draft Practical Compliance Guideline, setting out its position on when cross-border payments for software, SaaS access, and related IP arrangements amount to 'royalties' subject to withholding tax. The ruling took five years to finalise, replacing a position that traces back to the withdrawal of an older ruling in 2021. It follows the Australian High Court's decision in PepsiCo, which the ATO has relied on to justify a substance-over-form, purpose-focused test for what counts as a royalty.

The fact pattern is close to one India's Supreme Court resolved in 2021, in Engineering Analysis Centre of Excellence v CIT. That decision ended nearly two decades of litigation by holding that payments to non-resident software suppliers for resale or use under distribution agreements and end-user licences are not royalty payments, because what is transferred is a copyrighted article, not an interest in the copyright itself. India adopted the narrow, taxpayer-favourable reading. TR 2026/2 goes the other way, and further: it takes the position that where IP rights are practically inseparable from the other commercial rights in a software intermediation arrangement, which the ATO treats as the common case, the entire payment is characterised as a royalty, with no workable apportionment pathway in the final text. The draft compliance guideline layered on top classifies most royalty-free distribution structures as medium-to-high risk unless the local margin clears a threshold meaningfully higher than the ATO's own prior benchmark for low-risk distributor margins.

The commercial reality of how software gets distributed cross-border has not changed. What has changed is the doctrinal lens two major tax administrations apply to that reality, and the two now point in opposite directions. This matters because many multinational software distribution structures are built on a common template rolled out across several jurisdictions at once. A group that priced its Indian and Australian distribution entities on the same functional analysis and the same intercompany agreement now faces two different downstream outcomes. In India, the royalty question was settled years ago, so the TP analysis stands on its own. In Australia, the royalty characterisation question sits ahead of the TP question and can override it: if the whole payment is a royalty, the arm's length distribution margin analysis becomes secondary to a withholding tax exposure that was never priced into the original structure. MinterEllison's analysis of the final ruling described this as a 'full-royalty rather than apportioned outcome' as the default assumption, a marked hardening from where the draft guidance started.

Whether this is an Australian idiosyncrasy or an early sign of a broader trend is not yet clear. Some revenue authorities, facing fiscal pressure, have shown greater willingness to use substance-based, purpose-focused tests to recharacterise payments, an interpretive approach not unlike the one behind India's GAAR and its expanded commercial substance doctrines. If more jurisdictions begin reading embedded software royalties this expansively, Indian multinationals licensing software into distribution networks abroad, and Indian subsidiaries of global software groups receiving payments from Indian customers, may need a characterisation review ahead of every TP benchmarking exercise, rather than a periodic refresh of comparables alone. For now, groups with common distribution documentation across India and Australia should map where the two jurisdictions' positions on the same contract diverge, rather than assume that a settled Indian royalty position carries over wherever the same paperwork is used.

When the Service Provider Is an Algorithm: What Chapter VII's AI Problem Means for Indian Captives

A captive Indian entity, such as a GCC, a KPO, or a testing centre like the one at the heart of the Honda R&D India ruling this month, performs a defined function for its overseas parent. The transfer pricing analysis asks three questions: whether a service was rendered, whether the recipient derived a benefit, and what cost-plus markup reflects an arm's length charge for that service.

This is the architecture of Chapter VII of the OECD Transfer Pricing Guidelines, and it underlies most of India's Safe Harbour Rules, most APAs for ITES/BPO structures, and a large share of the ITAT docket. It works cleanly as long as the item being delivered is recognisably a service: bounded, routine, and separable from any underlying intangible.

In June 2026, the OECD's Working Party 6 released a discussion draft proposing a substantive rewrite of Chapter VII, including a new accurate-delineation analysis, an expanded benefit test, a sharper shareholder/stewardship distinction, and new guidance on the boundary between a service and an intangible. Comments closed on 22 July, and the OECD published the full set of responses on 24 August: more than one hundred submissions from businesses, industry bodies, and advisory firms.

A recurring theme across those submissions was a call for clearer distinctions between intra-group services and intangible transfers, particularly for AI-enabled and digital service models. This suggests that practitioners find the existing framework difficult to apply to AI-delivered outputs, a chapter otherwise treated as settled doctrine since the 1990s.

The captive/GCC model is central to India's transfer pricing practice, not a peripheral segment of it. Thousands of Indian entities are remunerated on a cost-plus basis for the kind of routine, human-performed functions that Chapter VII was built to price: testing, market research, back-office processing, and KPO analytics. Many of these entities are, in 2026, incorporating agentic AI into the delivery of these functions, which changes what sits inside the service line item rather than replacing it.

If a GCC's cost-plus-remunerated data analytics service is increasingly produced by an AI agent trained on group data, proprietary models, or a parent's algorithms, the question is whether the Indian entity is still delivering a 'service' in the Chapter VII sense, or something closer to an output derived from an intangible it does not own. That distinction could imply a different pricing method, a different DEMPE analysis, and possibly disqualification from the Safe Harbour margins (now consolidated into the unified 15.5% IT Services band) that assume a routine, low-risk service function.

Two other developments this week bear on the same issue. Turkey's Tax Inspection Board launched an AI system in August built specifically to flag transfer pricing risk in related-party transactions, which suggests some revenue authorities are building AI capability aimed at this kind of transaction. On the advisory side, Nexdigm's alliance with the AI-native TP platform infer360 shows Indian mid-tier firms building AI-TP capability of their own, partly in anticipation of this characterisation issue becoming a live audit question rather than an academic one.

Whether the CBDT should clarify, before the 2027 filing season, whether AI-enabled service delivery inside a GCC/ITES structure remains within the Safe Harbour and cost-plus framework, or whether it needs a separate carve-out, is a question worth raising now. The Chapter VII revision will not be finalised until after the November 2026 Paris consultation, so India has a window to shape its own domestic position rather than import whatever the OECD eventually adopts.

Given how much of India's outbound service economy sits on this fault line, practitioners advising GCC and ITES clients would do well to flag the characterisation risk in current TP documentation, ahead of any assessment order that forces the issue.

Post-SAP Labs Rulings Tighten Scrutiny of Comparability Filters in TP Benchmarking

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.

Agentic AI in Transfer Pricing Benchmarking: What a New Study's Error Rates Mean for Indian Practice

A study published in The International Tax Journal tests, with data rather than assertion, a claim commonly made for AI-driven transfer pricing benchmarking tools: that autonomous systems can produce faster analysis without a corresponding loss of rigour. The findings deserve closer attention from Indian practitioners than they are likely to receive.

The authors ran agentic AI, systems that execute a multi-step workflow such as a benchmarking search or functional analysis without step-by-step human prompting, against 20 simulated MNE cases across three sectors. They compared the outputs against a model of how tax administrations in 11 countries are deploying similar technology. The efficiency gain was substantial: agentic AI cut processing time by roughly 98% while increasing the depth of the functional analyses produced.

The same study reports three failure rates that merit attention before any TP head signs off on an AI-assisted benchmarking pipeline: a 15% error rate in complex functional characterisations, a 22% irrelevance rate in comparable selection, and a 15% hallucination rate in legal citations. The authors propose a governance framework they call "Tracer-Wire," under which every AI-generated conclusion must carry a visible, auditable path back to its source data, with a mandatory human checkpoint before any output is finalised.

Explainability-by-design and human-in-the-loop review are now standard features of AI governance proposals, so the framework itself is not the notable part of the paper. What is notable is the coincidence between the study's error rates and recent Indian tribunal outcomes.

This month alone, three decisions have turned on the same issue the study measures. The Delhi bench of the ITAT excluded two comparables from Dixon Technologies' set for functional dissimilarity, even though the taxpayer had itself flagged the issue years earlier. The Chennai bench devoted an entire order to whether a single internal comparable can still claim the statutory tolerance band. The Karnataka High Court's SAP Labs line of rulings has produced a further set of follow-on decisions this month on whether a TPO may discard a taxpayer's comparables in favour of a "standard set."

Each of these disputes falls within the 22% irrelevance-rate failure mode the study measured. If agentic AI misjudges comparable relevance roughly one time in five even in a controlled simulation, and Indian tribunals are already spending full orders correcting comparable-selection errors made by humans, the practical question for TP practice in 2026 is concrete rather than conceptual: whose signature appears on the local file when an AI-selected comparable turns out, on review by a TPO or an ITAT bench two years later, to be a functionally dissimilar entity such as a plastics manufacturer.

Coverage of AI in transfer pricing tends to avoid a question that is uncomfortable for vendors and practitioners alike: whether an audit trail reduces liability or merely relocates it. A Tracer-Wire log showing that an AI system considered and rejected a comparable for a documented reason does not make that rejection correct. It makes the error more visible, and it arguably shifts accountability toward whoever approved the workflow rather than toward the tool itself.

It is worth asking whether India's Master File and Form 3CEB documentation requirements are structured to capture this kind of AI-decision provenance at all. If they are not, practitioners may be looking at a new category of documentation gap in an area the OECD's Chapter V framework was never designed to address. Firms adopting agentic AI for benchmarking would do well to build a sign-off protocol now, one that fixes responsibility for each accepted comparable before a tribunal does it for them.