Review of GoComet, Supply Chain Automation Platform

By Léon Levinas-Ménard

Last updated: November, 2025

GoComet is a Singapore-headquartered, Series-A freight software vendor offering a cloud-based multimodal logistics platform that combines freight procurement (RFQs and rate negotiation), shipment visibility, invoice auditing and workflow management for shippers across ocean, air, road and courier. The company positions its product as an AI-powered “supply chain automation” and visibility layer, promising predictive ETAs, port-congestion risk indicators and centralized control-tower workflows for global brands, with marketing material claiming adoption by 500+ enterprises and extensive logos in pharmaceuticals, automotive, chemicals, FMCG and logistics (e.g. Unilever, Honda, General Mills, Qatar Airways, Glenmark, Sun Pharma).¹²³⁴⁵⁶ In practice, GoComet operates much closer to the transportation-execution layer than to end-to-end supply-chain planning: it attempts to digitize and partially automate how shippers select carriers, monitor shipments in transit and reconcile invoices, rather than optimizing inventory or production plans. Its public materials provide concrete evidence of SaaS delivery, multimodal tracking and RFQ automation, but offer limited technical depth on the underlying AI/ML models, optimization routines or data pipelines beyond high-level statements about “data science” and “machine intelligence.”¹⁷⁸⁹

GoComet overview

At a functional level, GoComet delivers a browser-based and API-accessible SaaS platform centered on freight execution: a shipper can run RFQs with carriers, select winning bids, hand off bookings, track containers and air waybills, monitor port congestion and exceptions, and reconcile freight invoices against contracted rates, all in one environment.¹⁷⁶¹⁰ The platform is explicitly multimodal (ocean, air, road, courier) and multi-carrier; GoComet highlights “Track and Trace, Procurement, Invoice Audit, Workflow Management [and] Market Intelligence” as its core solution families, powered by “data science and advanced machine learning intelligence.”¹ Architecturally, GoComet is a multi-tenant, cloud-hosted application delivered as enterprise SaaS, with dedicated free tools (e.g. online container tracking) and a commercial platform gate for paid capabilities.¹¹¹

Commercially, GoComet is a relatively young but non-trivial player: founded in 2016 in Singapore by Chitransh Sahai, Gautam Prem Jain, Ayush Lodhi and Mehul Katiyar,¹²¹³¹⁴ it raised a US$7m Series A in February 2022 led by Rider Global and Atlas Ventures,¹²¹⁵¹⁶¹³¹⁷ bringing total disclosed funding to around US$9–10m depending on the source.¹²¹⁵⁹ Tracxn and similar trackers classify the firm as a “minicorn” Series-A company with roughly US$10.3m raised and a headquarters in Singapore,¹² while some US-focused databases (e.g. CB Insights, Parsers.vc) list a Newark, New Jersey presence and describe the product as an AI-powered transportation visibility platform.¹⁵⁷⁸ Public references indicate customer deployments across North America, Europe, APAC, the Middle East and Latin America, with a mix of Fortune 500 manufacturers, pharma companies, tyre manufacturers, logistics providers and airlines.¹³⁴⁵⁶¹⁸

Technically, GoComet’s most distinctive claims revolve around three themes: (1) predictive ETA and port-congestion analytics for ocean containers, (2) freight RFQ automation and negotiation tooling that promises better rates through competitive bidding and centralization, and (3) automated invoice audit to catch rate discrepancies and billing errors.¹⁷⁶¹⁰¹¹ The vendor repeatedly attributes these benefits to AI/ML and “data science,” but public documentation stops short of specifying model architectures, feature sets, error metrics or optimization objectives. Independent analyst write-ups and funding announcements broadly corroborate that GoComet is in the freight-visibility / TMS (transportation management) space, using AI primarily to enhance tracking and exception management rather than to solve full-blown network optimization problems.¹²¹⁵¹⁹⁷⁸³

GoComet vs Lokad

Functionally, GoComet and Lokad address different slices of the supply-chain decision stack. GoComet is positioned at the transportation execution layer: its focus is on how shipments are procured, booked, monitored and billed once a company has already decided what to ship, from where and when.¹⁷⁶¹⁰ Lokad, by contrast, operates at the planning and decision optimization layer: its platform is designed to compute probabilistic demand forecasts and optimize inventory, production, allocation and pricing decisions across SKUs, locations and time, producing ranked lists of purchase orders, stock transfers or production batches rather than booking freight.²⁰²¹

In terms of data orientation, GoComet is event-centric and shipment-centric. Its core data objects are RFQs, carrier bids, booking confirmations, container IDs or AWBs, shipment milestones, port congestion signals and freight invoices.¹⁷⁶¹⁰¹¹ The goal is to reduce manual coordination between logistics teams and forwarders, provide real-time visibility of in-transit freight and automate checks on whether carriers billed correctly. Lokad, on the other hand, is SKU-centric and inventory-centric. Its data model revolves around sales histories, on-hand and in-transit stocks, BOMs, lead times and cost drivers; the main outputs are optimized decisions like “order 120 units of SKU X to warehouse Y next week” with explicit economic justification under uncertainty.²⁰²¹

Regarding analytics and AI, GoComet publicly claims to use “advanced machine learning intelligence” to compute predictive ETAs and risk indicators (e.g. for Red Sea disruption) and to enrich visibility dashboards,¹⁶¹⁷ but does not publish technical specifics on the models beyond general references to data science and machine intelligence.⁷⁸ Lokad, in contrast, documents a stack that combines probabilistic forecasting, deep learning and stochastic optimization, all expressed via its Envision domain-specific language, and has benchmark evidence (e.g. M5 forecasting competition performance) for parts of that stack.²⁰²² Lokad’s ML is aimed at jointly optimizing forecasts and decisions; GoComet’s ML appears aimed at enhancing transport visibility and risk alerts.

The decision scope differs accordingly. GoComet may suggest which carrier quote to accept, flag shipments at risk of delay or highlight invoices that deviate from contracted rates; these are operational logistics decisions tied to specific shipments and legs.⁶¹⁰²³¹¹ Lokad instead prioritizes decisions on how much to buy, where to position stock, which orders to accept or expedite, or how to set prices, using quantified trade-offs between service levels and economic drivers.²¹²⁰

Architecturally, GoComet is delivered as a relatively closed SaaS application with pre-packaged modules (GoProcure, GoTrack, GoInvoice, GoShipment, GoPlan, Market Intelligence) and a documented API surface for integrations.¹⁶²³¹¹ Lokad exposes a programmable analytics engine where each client’s logic is written as Envision scripts executed on a distributed back-end; the “product” is effectively a bespoke optimization app built on the same engine, rather than a fixed module catalogue.²¹²⁴

From a vendor-selection perspective, GoComet competes primarily with transport-visibility and TMS vendors (Project44, FourKites, Shippeo, etc.) and with freight-procurement / RFQ tools. Lokad competes with advanced planning and optimization systems (APS, demand planning suites, inventory optimization vendors). In a large organization, both could plausibly coexist and even complement each other: Lokad could compute optimal supply plans and replenishment decisions, while GoComet executes those plans by bidding freight, tracking shipments and auditing invoices. The overlap is limited; they are not substitutes for each other.

Company history and funding

Multiple independent sources agree that GoComet was founded in 2016 and is headquartered in Singapore.¹²³¹⁰¹⁴ Tracxn’s profile (updated November 2025) describes GoComet as a Series-A company based in Singapore, founded by Chitransh Sahai, Gautam Prem Jain, Ayush Lodhi and Mehul Katiyar and operating an AI-powered multimodal logistics platform for end-to-end supply-chain digitization.¹² A YourStory company profile similarly describes GoComet as a technology company founded by “four IITians” with a mission to eliminate traditional opacity in supply-chain operations.¹³

Funding for GoComet is anchored by a US$7m Series-A round announced in February 2022. The PRNewswire release and multiple news outlets describe GoComet as a “vertical SaaS platform” providing multi-modal logistics solutions to SMEs and global conglomerates, and state that the Series A was led by Rider Global and Atlas Ventures.¹²¹⁵¹⁶¹³¹¹¹⁷ Those articles also note a presence in Southeast Asia, the US and Europe and list named customers including Sun Pharma, Glenmark, Polyplex, Alliance Tyres (Yokohama), Lupin and ACG.¹²¹⁵¹¹¹⁷ Tracxn reports total funding of roughly US$10.3m as of late 2025,¹² while earlier Series-A coverage quoted cumulative funding “about $9.5m,” implying either follow-on capital or slight discrepancies in counting prior rounds.¹²¹⁵

Other data providers (CB Insights, PitchBook, Parsers.vc) broadly agree on GoComet’s funding stage and product domain but differ on some metadata such as founding year (2016 vs. 2017) and primary office location (Singapore vs. Newark, New Jersey).¹⁵¹⁹⁷⁸ These inconsistencies are common in private-company databases; the primary evidence (founder interviews, press releases and Singapore positioning in Gartner-related coverage) supports the view that GoComet is a Singapore-founded company with US offices rather than a US-headquartered firm.¹²⁸³⁶

Commercial-maturity indicators suggest a vendor beyond early MVP stage but still in growth mode. Tracxn’s “minicorn” label and funding amount indicate a company with some scale but not heavily capitalized compared to major TMS players.¹² Gartner-related industry coverage in 2025 refers to GoComet as receiving “double recognition” in Gartner reports, describing customers spanning Fortune 500 companies, global manufacturers and logistics providers across multiple regions, and listing emissions tracking and ERP integrations as part of the platform scope.³ While such recognition is not a hard technical endorsement, it is consistent with a vendor that has survived early-stage risk and is now competing in mainstream visibility/TMS shortlists.

Product suite and functional coverage

Core execution modules

GoComet’s own materials and independent analyses agree that the platform is structured as a set of functional modules covering procurement, planning, execution and financial control for freight.¹⁷⁶¹⁰²³¹¹ Public pages and case studies highlight the following named components:

GoProcure – freight RFQ and negotiation engine that automates request-for-quote workflows with carriers, centralizes bids and provides comparative analytics so shippers can select winning rates.⁶¹⁰²³
GoPlan – dispatch planning module (mentioned in GoComet’s planning section) for end-to-end shipment planning, though public details on underlying optimization logic are thin.²³
GoTrack – real-time shipment tracking across carriers and modes, consolidating events for containers, AWBs, road and courier shipments into unified dashboards; often paired with predictive ETAs and port-congestion indicators in marketing.¹⁶¹⁰¹¹
GoShipment – shipment execution / control-tower tooling that bundles tracking, exception management and analytics for in-transit freight.⁶¹⁰
GoInvoice (Invoice Audit) – invoice reconciliation module that compares freight invoices against negotiated rates, flags discrepancies and centralizes approval workflows.¹⁷⁶¹⁰
Market Intelligence – tools covering lane-level rate benchmarking, carrier reliability, sailing schedules and port-congestion information.¹⁶²³¹⁷

Essentra’s case study provides one of the clearest concrete deployments: the company implemented GoInvoice to automate invoice management and reduce overpayments; GoProcure to streamline RFQs and consolidate email-heavy processes; and GoTrack/GoShipment to automate shipment tracking and provide central dashboards and analytics.¹⁰ The case study reports improvements in invoice-processing times, better visibility into freight costs and data-driven identification of savings opportunities, but does not expose the exact algorithms behind savings calculations or anomaly detection.¹⁰

Himalaya Wellness’s press release similarly describes using GoComet to optimize shipment tracking and improve on-time deliveries, quoting nearly 60% on-time performance and emphasising proactive communication of delays to customers. It also lists a wider set of pharma clients (Glenmark, Sun Pharma, Baxter, Dr. Reddy’s) and states that GoComet serves “over 500 enterprises” including Bazooka Candy, Lycra, Amcor and General Mills.¹⁸ Here again the operational benefits are described qualitatively (better tracking, proactive alerts, improved satisfaction) with little disclosed about the quantitative models behind ETAs or delay prediction.

Visibility, tools and free tier

Beyond the paid platform, GoComet runs online container tracking tools that allow any user to track up to a small number of containers per month for free, using container or bill-of-lading numbers and providing real-time status updates and notifications via email/SMS.¹¹ The free tools are explicitly positioned as a funnel into the broader platform and are limited in monthly volume; the paid product offers higher limits, deeper analytics and integration into enterprise workflows.

The main marketing site frames the overall solution as an AI-powered visibility platform that lets shippers “track your freight over road, rail, ocean and air in one integrated platform,” receive dynamic predictive ETAs and reduce manual coordination with carriers.¹¹¹ Logos on the homepage and customer-spotlight section include Danone, Honda, Godrej, General Mills, Qatar Airways, Colgate, Wipro, S&P Global, Nippon Express and others, with testimonials citing improved internal efficiency, improved customer communication about shipment status and reduced freight spend.¹⁶

From a functional-coverage standpoint, the evidence points to a fairly typical modern freight-execution / visibility suite with some extra emphasis on RFQ automation and market-intelligence dashboards. The modules appear tightly coupled: case studies consistently describe customers implementing several of them together (RFQ + tracking + invoice audit) to obtain centralization benefits.⁶¹⁰¹⁸

Technology stack and architecture

Public sources agree that GoComet is an enterprise, cloud-based SaaS product. Parsers.vc explicitly calls it an “enterprise cloud-based SaaS product … being used by large manufacturing conglomerates to automate various logistics processes.”⁸ The homepage presents GoComet as a multi-tenant platform accessible via browser, with no mention of on-premise deployments.¹

External analyst summaries (CB Insights, PitchBook) describe GoComet as an “AI-powered transportation visibility platform” that automates and streamlines freight management, including real-time tracking across carriers, automated freight procurement and invoice reconciliation.¹⁵¹⁹⁷ These descriptions are consistent with GoComet’s own taxonomy of modules and case-study narratives.¹⁶¹⁰ However, neither the vendor site nor analyst briefings disclose lower-level architectural details (programming languages, data-store technologies, queueing mechanisms, etc.).

GoComet does expose APIs for integration: the site references API documentation (e.g. for container tracking) and an “Implementation” page that walks through selecting events to integrate, setting up data transfer and mapping fields to client systems.⁹¹¹ The implementation guide emphasises event-driven integration (e.g. trigger outbound notifications when a shipment reaches a certain milestone) and suggests that the platform can be integrated via modern web APIs into ERPs or TMSs, though it stops short of listing supported systems or middleware in detail.⁹

On the AI/ML front, GoComet’s core technical claims are that it uses (a) advanced machine-learning methods for predictive ETAs, including risk-aware visibility during disruptions like the Red Sea crisis, and (b) data science to optimize freight-procurement outcomes.¹⁶¹⁷ Marketing copy talks about “data science and advanced machine learning intelligence” behind multimodal visibility,¹ and various case-study landing pages emphasize “predictive ETAs” and “AI-powered insights,” particularly in the context of port congestion and Red Sea disruptions.⁶¹⁷

However, no publicly accessible technical documentation could be found that specifies:

which ML algorithms are used (e.g. gradient boosted trees vs. neural networks vs. ensembles),
what features power the ETA models (e.g. carrier schedules, historical delays, vessel AIS, weather, congestion indices),
how often models are retrained,
how model accuracy is evaluated (MAPE, MAE, coverage of prediction intervals, etc.), or
how optimization is done for RFQ scoring or invoice anomaly detection (e.g. rule-based, heuristic, constrained optimization).

The absence of such detail does not mean ML is not used; it simply means the strength of the AI claim cannot be independently verified beyond marketing and case-study narratives. That is a common situation among commercial logistics-visibility tools: they tend to treat algorithms as proprietary and expose only high-level descriptions to the market.

Security and compliance: GoComet’s main site and media pages reference serving Fortune 500 companies and highlight ERP integrations and global reach,¹³¹⁸ which implies some level of security and compliance maturity, but there is no dedicated, detailed security or architecture whitepaper publicly available at the time of writing. Independently verifiable attestations (e.g. SOC 2 reports) were not accessible via public links, so any security claims remain marketing-level.

Overall, based on publicly visible information, GoComet appears to be a modern, cloud-native, API-enabled SaaS in the same general technical category as other freight-visibility platforms: multi-tenant web front-end, REST-style APIs and internal ML models for ETAs and risk scoring. The exact implementation and sophistication of those models are opaque to outside observers.

Deployment model and implementation practice

GoComet’s implementation model is described in outline on a dedicated “Implementation” page and illustrated through customer case studies.¹⁰¹⁸⁹ The implementation guide suggests a relatively standard SaaS rollout pattern:

Identify the events or actions in GoComet (e.g. shipment booked, milestone reached, invoice generated) to integrate with external systems.
Configure data transfer for those events (via APIs or file exchange).
Map data fields to the client’s ERP/TMS schemas.
Test the integration and move into production.⁹

The page also embeds user quotes from third-party review platforms (e.g. G2) highlighting use cases such as tracking inter-company shipments and praising GoComet’s support responsiveness.⁹ While useful for colour, such ratings are anecdotal and not a substitute for hard technical evidence.

Case-study narratives offer additional clues. Essentra’s deployment, for example, involved integrating GoInvoice, GoProcure, GoTrack and GoShipment across global locations, suggesting that GoComet can ingest shipment, RFQ and invoice data from multiple systems and present consolidated dashboards and workflows.¹⁰ The described transformation emphasises centralisation (replacing scattered spreadsheets and email threads), earlier visibility of rates and invoices, and faster exception resolution, but does not discuss integration latency, data-volume limits or SLAs.¹⁰

The Himalaya Wellness case describes a two-year usage horizon and claims nearly 60% on-time deliveries and proactive delay communication after adopting GoComet, again focusing on business outcomes rather than the technical implementation details.¹⁸

Taken together, the evidence supports the view that GoComet implementations are moderately involved but conventional SaaS projects: data mappings, API wiring, workflow configuration and user training, typically rolled out over weeks to months depending on scope. There is no sign of a programmable DSL or heavy in-house modelling required from customers, in contrast to Lokad’s more programmable approach; instead, GoComet tends to present pre-built modules that are configured rather than coded.

Commercial traction and customer evidence

GoComet’s customer footprint is better substantiated than many early-stage startups, thanks to explicit case studies, press releases and logo walls:

The main site claims “500+ brands globally” and “over 70 countries,” with a dense logo field including Unilever, Danone, Honda, General Mills, Qatar Airways, Ajinomoto, Bazooka Candy, Sun Pharma, Glenmark, ACG, Colgate, Wipro, S&P Global, Nippon Express and many others.¹⁴⁶
ReadMagazine’s feature on GoComet’s mission lists major US clients including Vita Coco, General Mills, Honda, John Deere, S&P Global, Qatar Airways, Ajinomoto, Borosil and Bazooka Candy, contextualising GoComet’s first US-based customer event.⁵
Himalaya Wellness’s press release confirms its own deployment of GoComet and names additional pharma customers (Glenmark, Sun Pharma, Baxter, Dr. Reddy’s).¹⁸
The Essentra case study provides detailed narrative and indicates use of four major modules across global operations.¹⁰

These are named, verifiable references rather than anonymised claims like “a large European shipper.” For some of them (Essentra, Himalaya) third-party PR wires or external articles corroborate GoComet’s involvement.¹⁸¹¹¹⁷

On the other hand, broader claims such as “trusted by Fortune 500 companies” and “world’s most intuitive AI-powered visibility platform” are marketing slogans that, while plausible given the logos, cannot be independently quantified.¹³ Gartner-related coverage describing double recognition in 2025 reports shows that GoComet is at least visible in analyst evaluations, but such mentions do not in themselves prove technical superiority.³

Overall, the commercial maturity evidence is stronger than the technical transparency: GoComet clearly has a non-trivial enterprise customer base and geographic footprint. The exact depth of adoption (e.g. number of lanes or modes per customer, degree of automation vs. manual use) remains opaque from public information.

Critical assessment of technical claims

From a technical-rigour standpoint, GoComet’s solution can be summarised as follows:

What it delivers, concretely: a SaaS platform to run freight RFQs, track multimodal shipments, monitor disruptions and reconcile freight invoices, with dashboards and workflows for logistics teams. There is solid evidence for these capabilities via product pages, analyst descriptions and detailed case studies.¹¹⁵¹⁹⁷⁶¹⁰¹⁸¹¹
How it achieves this, at high level: modern web application backed by centralized data stores and APIs, combining rule-based workflows with ML-derived ETAs and risk scores, and exposing functionality via modules such as GoProcure, GoTrack, GoInvoice and GoPlan.¹⁷⁸⁶²³¹¹

However, several aspects of the “AI-powered” narrative remain weakly evidenced:

ML/AI details are opaque. There is no technical whitepaper or documentation describing model architectures, feature sets or error metrics for ETAs, risk predictions or anomaly detection. All AI claims come from marketing copy (“advanced machine learning intelligence,” “AI-powered insights”) and high-level case-study narratives, which do not allow independent verification of state-of-the-art status.¹⁸⁶¹⁸
Optimization vs. automation. GoComet clearly automates workflows (RFQ distribution, bid collection, invoice checks), but there is little evidence that it performs deep mathematical optimization beyond standard scoring and filtering. For example, public materials do not show formulations like “optimize carrier selection subject to capacity and service constraints” or “minimize expected logistics cost under uncertainty”; instead, they emphasize visibility, consolidation and faster manual decision-making.¹⁵¹⁹⁷⁶¹⁰
Benchmarking and accuracy. Unlike some forecasting vendors that participate in public competitions or publish accuracy benchmarks, GoComet does not present quantitative evaluations of its predictive ETAs (e.g. average ETA error vs. carrier ETAs or vs. competition). Case studies do not report metrics like reduction in ETA error; they report business outcomes (on-time delivery, customer satisfaction), which, while important, are multi-factor and not uniquely attributable to ML models.⁵¹⁰¹⁸
Breadth of ML application. Most AI references cluster around visibility and disruption-management (e.g. Red Sea crisis),⁶¹⁷ with less evidence that ML is used in, for example, invoice anomaly detection or rate benchmarking; those areas may rely mainly on deterministic rules and analytics rather than learning systems. This is not a flaw per se – many problems do not need ML – but it means the platform is better described as “partially ML-enhanced” than as an end-to-end AI system.

Against that backdrop, GoComet appears technically solid but not demonstrably avant-garde. It is aligned with contemporary freight-visibility tools: cloud-native, API-driven, ML-assisted ETAs, RFQ and invoice automation, visually polished dashboards. There is no public evidence of research-grade innovation (e.g. open-sourced models, academic collaborations, patents on novel ETA algorithms) beyond the vendor’s own marketing statements. This contrasts with Lokad’s more research-visible profile (e.g. probabilistic forecasting at scale, differentiable programming, M5 results).

None of this detracts from GoComet’s practical value: for many shippers, simply centralizing RFQs, tracking and invoices in a single, responsive SaaS can deliver significant savings and fewer surprises in transit. But from a strictly evidence-based, state-of-the-art perspective, GoComet should be seen as a competent, commercially validated freight-execution platform with AI-assisted visibility, rather than as a transparent technical pioneer in ML or optimization.

Conclusion

GoComet is a Series-A logistics software vendor that offers a credible, commercially adopted platform for multimodal freight procurement, visibility and invoice audit. Its strengths lie in workflow consolidation, multimodal tracking, RFQ automation and market-intelligence dashboards, all delivered as an enterprise SaaS package with API-based integration and a growing international customer base, including well-known industrial and consumer brands.¹¹²⁷⁴⁵⁶¹⁰¹⁸ The company clearly sits in the transportation-execution and visibility layer of the supply chain and is not a planning or inventory-optimization system.

Technically, GoComet claims AI and ML capabilities, especially around predictive ETAs and disruption management, but provides limited public detail about the underlying models, training data, evaluation and optimization methods. As a result, its status as “AI-powered” can be accepted at face value only in the weak sense that ML is likely being used somewhere in the stack; stronger claims about being state-of-the-art in predictive logistics cannot be substantiated from public evidence alone. Compared with Lokad, GoComet focuses on tracking and executing shipments, while Lokad focuses on deciding what to produce, stock and move under uncertainty, using a more transparently documented optimization framework.

For practitioners, the implication is straightforward: evaluating GoComet should be done primarily on fit-for-purpose freight workflows, integration ease and empirical performance in visibility and cost control on your lanes, rather than on marketing claims about AI. For companies already investing in quantitative planning platforms like Lokad, GoComet is best viewed as a potential complementary layer that can feed better in-transit and freight-cost information into upstream planning – not as a replacement for probabilistic demand and inventory optimization.