APAC AI Supply Chain: 8 Companies 2026
Eight AI-native supply chain companies in Asia Pacific mapped by funding, acquirer landscape, valuation benchmarks, and M&A strategic rationale for 2026.
The APAC supply chain AI market is at an inflection point driven by three forces that have no equivalent in the US or European market: the China+1 manufacturing diversification trend pushing trillions of dollars of production into Vietnam, India, Thailand, and Indonesia; the post-pandemic supply chain resilience investment wave, which accelerated AI adoption in supply chain planning by an estimated five years in APAC manufacturing sectors; and the regional push by Japanese and Korean industrial conglomerates to digitize supplier networks that are larger and more geographically dispersed than anything managed by Western peers.
This page maps eight AI-native supply chain companies operating in Asia Pacific, covering who they are, what their AI actually does, what they are worth, and who is likely to acquire them. Amafi Advisory advises AI company founders and corporate development teams on M&A and fundraising transactions in Asia Pacific.
The comparison focuses on dimensions that matter for strategic and financial decisions: data moats, acquirer theses, AI differentiation, and valuation benchmarks. General supply chain software companies are not included; every company on this list is selected because AI is integral to the product’s differentiation, not because it has added AI features to a product that existed without it.
Total Funding Raised — USD Millions (approximate)
Why APAC AI Supply Chain Is a Structurally Distinct M&A Category
The supply chain AI acquisition thesis in APAC is fundamentally different from US and European equivalents in three ways that will not converge in the near term.
Multi-tier supplier complexity creates proprietary data that Western platforms cannot access. APAC is the world’s manufacturing base. Supply chains here involve five to eight supplier tiers as a matter of course, not exception — a consumer electronics product assembled in Vietnam draws components from Korean wafer fabricators, Japanese capacitor manufacturers, Taiwanese printed circuit board shops, and Chinese rare earth processors, all of which have their own upstream supplier networks. Mapping this complexity requires AI trained on APAC-specific supplier relationship data, logistics flows, and quality records that simply do not appear in Western supply chain datasets. Companies that have spent years collecting this multi-tier visibility data have a moat that no platform entering the APAC market from North America or Europe can close quickly.
The China+1 diversification trend is generating an AI market with no equivalent elsewhere. Companies globally are rebuilding supply chains to reduce concentration in China. Vietnam, India, Thailand, Indonesia, and Mexico are the primary beneficiaries. For APAC supply chain AI companies, this shift creates a commercial opportunity that is unique in scale: every multinational that is diversifying production needs supply chain intelligence about alternative supplier bases in markets where their procurement teams have years less experience than they have in China. AI platforms with pre-built supplier networks, quality history databases, and logistics maps for Vietnam or India are not competing to sell software; they are selling access to operational intelligence that their buyers have no other way to acquire quickly.
Japanese and Korean industrial buyers are the most sophisticated acquirers in this category. Panasonic’s acquisition of Blue Yonder in 2021 for $8.5 billion was not an outlier. It reflected a strategic calculation that Japanese manufacturing competitiveness increasingly depends on supply chain intelligence at a tier and frequency of visibility that traditional annual supplier audits cannot provide. Hitachi, Fujitsu, NEC, Samsung SDS, and LG CNS have all articulated analogous acquisition mandates. These are not passive investors; they are companies whose competitive position in their own industries depends directly on supply chain performance, and they are prepared to pay strategic premiums for AI capability that their internal development programs cannot replicate in a competitive timeframe.
“The supply chain AI opportunity in APAC is not a regional version of a global category,” says Daniel Bae, Founder and CEO of Amafi Advisory, with over $30 billion in transaction experience. “It is a category defined by problems that only exist at this scale and complexity in Asia Pacific: multi-tier supplier mapping at seven-plus tiers, real-time quality intelligence across five countries and four languages, and the China diversification build-out. The companies that have built specifically for these problems have data moats and customer bases that are genuinely difficult to replicate.”
AI Differentiation Tier Framework
The critical question in any supply chain AI acquisition is the same as in any AI M&A: is the AI the product, or is it an efficiency overlay on a workflow that existed before AI?
Tier 1: AI is the supply chain intelligence. The platform generates supply chain insights that are structurally impossible without AI processing at scale: multi-tier supplier visibility, predictive disruption signals from satellite imagery or weather data, or quality defect prediction before inspection occurs. Acquirers pay 10-16x ARR for Tier 1 platforms because the AI capability is not replaceable by adding analytics to an existing ERP feed. o9 Solutions and Altana operate in this tier.
Tier 2: AI materially transforms supply chain workflow. AI is used to automate demand forecasting, inventory optimization, route planning, or quality inspection workflows in ways that produce measurable working capital improvement or cost reduction. The AI component is not trivially replicable, but the underlying data is available through standard ERP and logistics integrations. RELEX Solutions and Inspectorio sit in this tier, with genuine AI differentiation in specific workflow steps.
Tier 3: AI as logistics operational layer. AI augments routing, scheduling, and capacity planning in ways that improve efficiency but where the core value is the logistics network, not the AI models. Ninja Van and Locus operate partly in this tier for their logistics execution products, though both have Tier 2 demand forecasting capabilities that shift parts of their platform upward.
The Eight Companies
1. o9 Solutions (Dallas / Singapore APAC hub)
Founding: 2009, Dallas. Singapore office is APAC hub with clients across Japan, Korea, Australia, and Southeast Asia. Series D in 2021 raised $100 million; additional $295 million growth equity round in 2022 valued the company at approximately $2.7 billion; total funding exceeds $900 million.
What the AI does: o9 builds an AI-powered integrated business planning platform that connects demand sensing, supply planning, revenue management, and financial planning into a single graph-based data model. The platform’s AI engine — called the Enterprise Knowledge Graph — ingests structured and unstructured data from ERP systems, market intelligence, news feeds, and weather data to generate probabilistic demand forecasts and supply plan recommendations. For APAC clients in electronics, consumer goods, and automotive, this means the platform can incorporate macro signals like Korean semiconductor inventory cycles or Vietnamese factory closure risks into supply planning decisions weeks before those signals appear in order books.
Data moat: The Enterprise Knowledge Graph compounds with each enterprise deployment. Planning decisions, forecast outcomes, and disruption events across o9’s client base of 200+ enterprises (including LG Electronics, Bridgestone, and Unilever APAC) train the platform’s probabilistic models on real-world planning patterns across industries. That cross-client learning is not available to any internal supply chain team relying on its own historical data alone.
M&A readiness: At $2.7 billion, o9 is in the range of a strategic acquisition by a large ERP vendor (SAP or Oracle, both of which have been building supply chain AI capability through acquisitions) or by a large Japanese or Korean industrial holding company seeking to build a supply chain intelligence platform for its own group companies and external clients. A PE-backed leveraged buyout is also plausible given the SaaS revenue quality and the professional management team.
2. Moglix (Singapore / India)
Founding: 2015, Noida and Singapore. Series G in 2022 raised $250 million at a $2.6 billion valuation; total funding exceeds $800 million from investors including Tiger Global, Accel, Sequoia India, and Falcon Edge.
What the AI does: Moglix is an AI-powered B2B industrial supply chain and procurement platform focused on manufacturing and infrastructure sectors. The platform uses AI for demand forecasting across 100,000+ industrial SKUs, supplier discovery and qualification across 50,000+ verified manufacturers in India and Southeast Asia, and dynamic pricing based on commodity input cost signals. For manufacturing companies, the platform functions as an outsourced procurement intelligence layer that reduces procurement cycle times from weeks to days by eliminating the manual supplier qualification and price discovery process.
Data moat: Moglix has accumulated ten years of industrial procurement transaction data across manufacturing, energy, infrastructure, and construction sectors in India and Southeast Asia — the largest proprietary database of industrial B2B pricing and supplier capacity data in the region. This transaction history is not commercially available, covers SKU categories with limited public price discovery, and reflects real supply-demand dynamics across the manufacturing cycles of some of India’s largest industrial groups (Tata, Adani, and Reliance are among its clients).
M&A readiness: Moglix has the scale, the client base, and the data moat to attract strategic interest from a global industrial distributor (Grainger, Fastenal, RS Group) seeking APAC market entry without the decade required to build a comparable supplier network, or from an ERP platform (SAP Ariba, Coupa) seeking to embed live APAC industrial procurement intelligence into its procurement workflow. A consolidation with a Korean or Japanese industrial distribution platform is also plausible given the overlap between Moglix’s supplier network and the APAC supplier bases of Korean and Japanese manufacturers.
3. Zetwerk (Bangalore / Singapore)
Founding: 2018, Bangalore. Series F in 2022 raised $210 million at a $2.7 billion valuation; total funding exceeds $600 million from investors including Greenoaks, D1 Capital, and Tiger Global.
What the AI does: Zetwerk is an AI-powered manufacturing supply chain platform connecting industrial buyers with contract manufacturers across India, Vietnam, Thailand, and the Philippines. The platform uses computer vision AI for incoming quality inspection (automated defect detection on manufactured parts using image recognition), AI-based supplier matching that scores 10,000+ contract manufacturers against order specifications, and predictive capacity planning that gives buyers visibility into lead times across a network of manufacturing partners. For industrial buyers in aerospace, energy, and consumer goods, Zetwerk’s AI replaces the manual RFQ and supplier qualification process with a platform that can return sourcing options and quality risk assessments within hours rather than weeks.
Data moat: Zetwerk has processed over $500 million in manufacturing orders through its platform, accumulating quality inspection records, lead time actuals, and supplier performance data across thousands of order types. That performance data — capturing which manufacturers reliably hit tolerance specifications on which part categories — is not available from any public source and represents years of quality documentation that a new market entrant cannot acquire quickly.
M&A readiness: Zetwerk’s manufacturing marketplace combined with its AI quality inspection capability makes it a compelling acquisition for a global quality management platform (Hexagon, Bureau Veritas, SGS) seeking AI-native capability in India and Southeast Asia manufacturing, or for a Japanese or Korean manufacturer seeking to build a proprietary supply chain intelligence layer for its own Make in India or ASEAN manufacturing investments.
4. RELEX Solutions (Helsinki / Singapore)
Founding: 2005, Helsinki. Raised $500 million in growth equity from TCV and OMERS Growth Equity in 2022 at an implied valuation above $5 billion; earlier rounds bring total capital to approximately $600 million.
What the AI does: RELEX builds AI-powered supply chain planning and retail execution software covering demand forecasting, replenishment planning, space optimization, and workforce management. The platform uses machine learning demand models that incorporate external signals — promotions, weather, events, and competitor pricing — into base statistical demand forecasts, improving accuracy over traditional time-series methods by 10-30% on comparable retail and grocery datasets. For APAC clients, RELEX has built specific demand models for Asian retail calendar patterns including Chinese New Year, Lunar New Year across multiple countries, and the ASEAN promotional event calendar.
APAC relevance: RELEX has expanded aggressively into APAC retail and grocery, with implementations at major supermarket groups in Australia, Singapore, and Japan. The platform’s strength in fresh food and perishable supply chains — where demand forecasting error has direct waste and stockout costs — is particularly relevant in APAC’s wet market and fresh food retail sector. Its acquisition by a global retail or grocery company seeking AI-native supply chain capability across APAC is among the more probable strategic scenarios for the business.
M&A readiness: At $5 billion implied valuation, RELEX is at the upper end of strategic acquisition range for a food or grocery retailer. More plausible buyers are global supply chain software platforms (Manhattan Associates, Blue Yonder now as a Panasonic company, or JDA Software successors), ERP consolidators, or PE firms seeking a supply chain software roll-up platform.
5. Ninja Van (Singapore)
Founding: 2014, Singapore. Series E in 2021 raised $578 million; total funding from multiple rounds exceeds $278 million in disclosed equity. Operations across Singapore, Malaysia, Indonesia, Philippines, Thailand, and Vietnam.
What the AI does: Ninja Van uses AI for last-mile delivery route optimization, dynamic capacity planning, and predictive failed-delivery detection. The platform’s route optimization engine processes real-time traffic data, delivery density maps, and driver behavioral data to generate delivery sequences that reduce per-parcel costs. Its failed-delivery prediction model, trained on several years of Southeast Asian delivery data, identifies parcels likely to fail on first attempt before dispatch, allowing the platform to pre-apply remediation steps (time window communication, alternative location confirmation) that reduce the costly re-attempt cycle.
Data moat: Ninja Van has delivered hundreds of millions of parcels across Southeast Asia since 2014, accumulating delivery outcome data across 6 countries, 20+ climate zones, and urban-to-rural density gradients that no platform entering APAC last-mile from outside the region has access to. That data, particularly the failed-delivery behavioral signatures by geography and parcel type, is the primary AI training asset. No amount of algorithmic sophistication can substitute for years of actual delivery performance data across Indonesia’s archipelago or Vietnam’s mixed urban-rural delivery environment.
M&A readiness: Ninja Van’s primary acquisition thesis is consolidation within APAC logistics, where scale and route density are the primary cost drivers. Potential acquirers include regional logistics platforms (J&T Express, LalamoveA, CJ Logistics), global parcel companies (FedEx, DHL, UPS) seeking SEA density, and e-commerce platforms (Shopee, TikTok Shop, Lazada) that want to own the last-mile rather than depend on external carriers.
6. Altana (New York / Singapore)
Founding: 2019, New York. Singapore office serves APAC enterprise and government clients. Series B in 2023 raised $100 million; total funding exceeds $200 million from investors including Activate Capital and Human Capital.
What the AI does: Altana builds the Altana Atlas, an AI-powered map of the global supply chain that identifies and connects 500 million+ supply chain entities across facilities, companies, and trade flows. The platform uses machine learning to reconcile trade data, business registration records, satellite imagery, and shipping manifests into a unified supply chain graph that surfaces previously invisible supplier relationships, beneficial ownership structures, and supply chain risk exposures. For APAC clients, the primary commercial applications are forced labor compliance (Uyghur Forced Labor Prevention Act compliance for goods transiting China), supply chain diversification mapping (identifying alternative supplier bases in Vietnam and India for products currently concentrated in China), and sanctions compliance for complex multi-tier supply chains.
APAC relevance: Altana’s core commercial use case is strongest in the APAC context. The China+1 supply chain diversification effort — the largest restructuring of global manufacturing geography in decades — creates a direct commercial need for supply chain intelligence that maps both the existing China exposure and the available alternative supplier bases in India, Vietnam, Thailand, and Indonesia. Every multinational with China-concentrated manufacturing that is managing a diversification program needs Altana’s supply chain mapping capability or an equivalent.
M&A readiness: Altana’s most likely acquirers are global trade compliance platforms (Amber Road, which was acquired by E2open), global customs and trade advisory firms (Flexport, Expeditors), or defense and intelligence primes that have active supply chain security programs. The forced labor compliance mandate, which is now a federal import enforcement requirement in the US with APAC compliance implications, creates a government contractor acquisition thesis as well.
7. Inspectorio (Minneapolis / Vietnam)
Founding: 2017, Minneapolis. Vietnam is the company’s primary operational hub. Raised approximately $110 million across multiple rounds including a Series B from IFC and NTTVC.
What the AI does: Inspectorio builds an AI-powered supply chain quality and compliance management platform for brands, retailers, and their supplier factories. The platform uses computer vision for defect detection in factory inspections, predictive quality analytics that identify which supplier facilities are most likely to produce defective batches based on historical quality records and production conditions, and a supplier performance scoring system that aggregates quality, compliance, and social audit data into a unified supplier risk profile. For brands sourcing from Vietnam, Bangladesh, and Cambodia, the platform reduces the average cost-per-inspection by 30-40% by automating what were manual inspection documentation workflows.
APAC relevance: Inspectorio’s Vietnam headquarters is an operational advantage. The company’s founding team has deep manufacturing and factory experience in Southeast Asian garment and electronics supply chains, and the platform has processed millions of inspections from factories across Vietnam, Bangladesh, Cambodia, and Indonesia. Factory floor data from these markets is not available through any commercial data provider; it is collected only through active inspection programs. Companies that have scaled inspection programs across these markets have datasets that are specific to the production characteristics, quality failure modes, and supplier behavior patterns of APAC low-cost manufacturing.
M&A readiness: Inspectorio’s most natural acquirers are global quality management platforms (Bureau Veritas, Intertek, SGS, TÜV SÜD) that are seeking to add AI capability to their inspection business, or global supply chain risk platforms (Resilinc, riskmethods, acquired by SAP) that want to integrate factory-floor quality data into their broader supply chain risk models. A retail technology consolidator (Centric PLM, TraceLink) seeking to extend upstream into the supplier quality layer is also plausible.
8. Locus (Bangalore / Singapore)
Founding: 2015, Bangalore. Raised approximately $65 million across rounds including a $50 million Series C in 2021. Singapore regional office serves APAC enterprise clients.
What the AI does: Locus builds an AI-powered last-mile logistics optimization platform for enterprise clients in FMCG, retail, and e-commerce. The platform uses AI for dynamic route optimization (reassigning routes in real time based on traffic, capacity, and new order inputs), carrier allocation decisions (which orders should go to which delivery partner based on cost, reliability, and service level requirements), and demand-based capacity planning that aligns delivery capacity procurement to predicted volume by geography and time window. For APAC enterprises managing multiple last-mile carriers across diverse urban and rural markets, Locus replaces manual dispatch decisions with an optimization layer that consistently achieves 10-20% delivery cost reduction.
Data moat: Locus has optimized hundreds of millions of delivery decisions for enterprise clients across India, Southeast Asia, the Middle East, and Europe. The platform’s reinforcement learning models, which improve route and carrier allocation recommendations through feedback loops from actual delivery outcomes, have accumulated years of real-world optimization data across highly varied delivery environments. The Southeast Asian delivery data in particular, covering the mixed formal-informal address structures, multiple carrier market fragmentation, and density discontinuities between urban and rural zones, is a genuine APAC-specific training asset.
M&A readiness: Locus’s enterprise FMCG and retail client base (Hindustan Unilever, Nestle, BigBasket) makes it an attractive acquisition for a global logistics SaaS consolidator, a supply chain execution platform, or a last-mile carrier seeking to add an AI optimization layer to its own dispatch operations. At its current scale, it is also an attractive PE acquisition for a logistics software roll-up thesis.
The APAC Supply Chain AI M&A Deal Log
Panasonic acquisition of Blue Yonder (2021) — $8.5 billion. Blue Yonder was a Scottsdale-based supply chain planning platform with approximately $600 million ARR at time of acquisition, implying roughly 14x ARR. Panasonic positioned the acquisition as the anchor of its “Autonomous Supply Chain” strategy, arguing that AI-powered supply chain planning was a durable source of competitive advantage for Japanese manufacturers facing cost pressure and China-linked supply chain disruption. The deal established that a Japanese industrial conglomerate would pay a US software premium for AI-native supply chain capability. Every subsequent supply chain AI acquisition in APAC has been benchmarked against this reference.
Maersk acquisitions of supply chain technology (2020-2023). Maersk, the world’s largest container shipping company, executed seven acquisitions of supply chain technology companies between 2020 and 2023 as it repositioned from pure container shipping toward an integrated logistics platform. The acquisitions included Visible SCM (supply chain visibility), Pilot Freight Services (US inland logistics), Martin Bencher (project logistics), and Senator International (air freight). The strategic thesis — that supply chain AI capability would allow Maersk to capture logistics margin beyond the container — is directly applicable to APAC, where Maersk has an active acquisition mandate for supply chain intelligence platforms serving its East Asian trade lane clients.
Thoma Bravo acquisition of Coupa Software (2023) — $8 billion. Coupa was a spend management and supply chain collaboration platform with $100M+ ARR at acquisition, implying approximately 20x revenue for a platform positioned at the intersection of procurement and supply chain AI. The Thoma Bravo acquisition demonstrated that PE buyers are willing to pay growth-stage software multiples for supply chain AI platforms with strong NRR and embedded enterprise workflows. APAC supply chain AI platforms with comparable ARR quality and NRR are within the acquisition range of the same PE buyer pool.
SAP acquisition of Signavio (2021) — approximately $1.2 billion. Signavio’s business process intelligence platform, which uses AI to analyze and optimize enterprise processes, was integrated into SAP’s supply chain process management offering. The acquisition established SAP’s willingness to pay nine-figure premiums for AI-native process intelligence capabilities to embed into its supply chain product suite. This acquisition thesis — buying AI capability to embed into a large ERP platform’s supply chain module — remains active for both SAP and Oracle.
Acquirer Landscape
Japanese industrial conglomerates remain the most strategically motivated APAC acquirers in this category. Hitachi’s Lumada platform, Fujitsu’s supply chain AI program, and NEC’s manufacturing AI initiatives all have explicit acquisition mandates. These companies are not buying supply chain AI as a financial investment; they are buying capability that their own manufacturing operations and those of their manufacturing clients need to remain competitive against Chinese and Korean peers that have moved faster on supply chain digitization. Strategic premiums of 2-3x over financial-buyer multiples are characteristic of these transactions.
Korean chaebols and their technology subsidiaries represent an underappreciated acquirer category. Samsung SDS is the supply chain technology arm of Samsung Group, managing supply chain AI for Samsung’s sprawling electronics and semiconductor manufacturing network across Korea, Vietnam, China, and India. LG CNS performs the same function for LG Group. Both companies have acquisition programs to bring external AI capability into their platforms and have the balance sheet to complete substantial acquisitions. Their primary targets are platforms with APAC manufacturing data moats that Samsung or LG’s internal development programs cannot replicate quickly.
Global logistics platforms continue executing supply chain AI acquisitions as they move up the value chain. Maersk, DB Schenker, Kuehne+Nagel, and DHL all have technology acquisition programs motivated by the same strategic thesis: logistics as software, not just physical movement. For APAC supply chain AI companies, these buyers bring an APAC customer base, a global infrastructure to leverage, and the operational context to integrate supply chain intelligence into live logistics execution.
ERP consolidators represent the most structurally active acquisition channel for supply chain planning AI. SAP has acquired Ariba (procurement), Signavio (process intelligence), and multiple supply chain optimization companies. Oracle has acquired JD Edwards and built supply chain cloud through acquisitions. Both companies have APAC-specific supply chain acquisition programs focused on platforms that address manufacturing and retail supply chain complexity not covered by their current modules.
PE consolidators offer a credible alternative for founders not ready for a full strategic acquisition. Thoma Bravo, Francisco Partners, and Vista Equity Partners each have supply chain software portfolios and the operational capability to scale APAC supply chain AI companies into international markets before a strategic exit.
Valuation Framework for APAC AI Supply Chain Platforms
Supply chain AI companies trade on three primary metrics: ARR quality and NRR, the proprietary data moat, and working capital improvement quantification.
ARR quality and NRR. Supply chain software has structurally high NRR in enterprise accounts where the platform is embedded in daily planning or execution operations. Companies with NRR above 115% in supply chain planning demonstrate that customers are expanding usage as they see ROI. NRR below 100% signals either implementation-heavy deployments with low user adoption or that the AI recommendations are not being adopted at sufficient rates to create stickiness.
The proprietary data moat. Acquirers differentiate sharply between supply chain AI companies that own proprietary data (multi-tier supplier networks, quality inspection records, delivery performance databases) versus those that aggregate ERP and logistics data that is available through standard integrations. Proprietary data commands a 2-4x multiple premium because it cannot be replicated, while aggregated data products face competitive pressure from any platform with the same integrations.
Working capital improvement quantification. The primary CFO-level ROI for supply chain AI is working capital reduction: lower safety stock levels through better demand forecasting, reduced write-offs through quality improvement, and lower emergency freight costs through better supply disruption prediction. Companies that can present quantified working capital improvement data across their client base — expressed as days-of-inventory-outstanding reduction or inventory write-off percentage improvement — are able to command premiums because the ROI case is concrete rather than qualitative.
Against these metrics, APAC AI supply chain platforms are trading at the following ranges: AI-native supply chain planning platforms with cross-client learning at 10-16x ARR, quality management AI with inspection data moats at 5-10x ARR, last-mile logistics optimization software at 6-12x ARR on the software revenue component, and supply chain intelligence platforms with proprietary data graphs at 12-20x ARR where the data moat is clearly documented and defensible.
Related Analysis
For Japanese and Korean corporate acquirer context in supply chain and adjacent categories, Japanese Companies Acquiring AI Startups and Korean Chaebol AI Acquisitions cover the strategic rationale and deal structure preferences of the most active APAC buyers. For the broader AI-native company acquisition landscape, Who Buys AI Companies in Asia Pacific maps the full acquirer universe. Supply chain AI platforms are evaluated against the same AI company valuation framework applied to other enterprise AI categories, with the data moat quantification being the primary premium driver. For founders preparing for an acquisition process, How to Prepare Your AI Company for Acquisition covers the documentation and positioning work that produces the best transaction outcomes. Ninjacart’s agri-fresh demand forecasting sits at the intersection of supply chain and retail AI — for the full retail and commerce AI acquisition landscape, including Walmart’s strategic context as a Ninjacart investor, see APAC AI Retail & Commerce: 8 Companies 2026.
Amafi Advisory advises AI company founders and corporate development teams on sell-side M&A, buy-side acquisitions, and fundraising advisory across Asia Pacific. If you are evaluating a supply chain AI transaction or preparing for a sale process, talk to our team. For advisors running APAC deal processes, Amafi.ai provides deal origination and execution support infrastructure.