Review of DeepVu, Supply Chain Software Vendor

By Léon Levinas-Ménard

Last updated: November, 2025

DeepVu (Vufind, Inc.) is a California-based AI vendor founded in 2016 by Moataz Rashad and Prof. Walid Aref, evolving out of an earlier computer-vision startup, Vufind. Its stated goal is to deliver “autonomous resilient planning” for manufacturers and retailers by combining deep learning, multi-agent reinforcement learning, digital twin simulation and a proprietary supply chain knowledge graph called VuGraph.¹²³⁴ The core product family centres on VuDecide “AI planning agents”, trained on top of VuSim digital twins to generate scenario-based recommendations for demand planning, inventory optimisation, order fulfilment, production and risk management under shocks such as COVID-style disruptions, commodity price spikes, port congestion or trade restrictions.⁵⁶⁷⁸⁹ These agents are fed not only by the customer’s own transactional data but also by a large stream of external macro- and micro-economic signals—interest rates, CPI, commodity prices, currency rates and more—embedded in VuGraph.¹⁰⁶³ DeepVu positions this stack as a full-stack AI layer that sits on top of ERP suites such as SAP, Microsoft Dynamics, Oracle and Infor, promising real-time or near-real-time decision support and “shock-resilient” planning.⁵¹¹¹² However, public technical documentation remains thin: there are no detailed algorithmic descriptions, no open code, no formal architecture diagrams, and no quantitative benchmarks beyond marketing claims and a small number of partner and directory write-ups.¹³⁹¹⁴¹⁵ As a result, while the vocabulary—deep reinforcement learning, generative AI decision models, digital twins, knowledge graphs—is fully aligned with current AI fashion, the extent to which DeepVu’s implementation is genuinely state-of-the-art versus a more conventional ML-plus-rules system wrapped in contemporary language is difficult to verify purely from public evidence.

DeepVu overview

DeepVu is a privately held AI startup headquartered in the San Francisco Bay Area (San Ramon / Berkeley), founded in November 2016 by Moataz Rashad and Prof. Walid Aref.¹¹⁶¹⁷¹⁸ The company explicitly positions itself as an “AI-powered autonomous resilient supply chain planning” provider for manufacturing and retail enterprises, with a focus on volatility, shock scenarios and sustainability.⁵²⁶¹⁴¹⁵ Its own “About Us” page describes DeepVu as a for-profit AI startup using AI “for the good of the planet and humanity”, emphasising social and environmental awareness, and noting prior experience of the founders in hardware, imaging, and large-scale data systems.¹¹⁹

From a product perspective, DeepVu markets several tightly connected components:

VuDecide – multi-agent AI “planning agents” trained with deep reinforcement learning (DRL) on top of a digital twin simulator (VuSim) to recommend decisions.⁵⁶¹¹⁹
VuSim – a digital twin simulator that can replay shock and normal scenarios (COVID delays, port congestion, droughts, trade constraints, etc.) for training the agents.⁵⁶⁸
VuGraph – a “scalable supply chain knowledge graph” combining customer data with hundreds of external macro-economic and other signals used as features for forecasting and decisioning.¹⁰³⁴
A set of SaaS modules (or “AI agents”) packaged for specific use cases: shock-resilient demand planning, inventory auto-replenishment, order fulfilment, freight and truckload planning, procurement BoM optimisation, and other supply-chain risk and sustainability scenarios.⁵¹¹⁸⁹³

The overall promise is to provide an AI layer that continuously learns from historical decisions and outcomes, simulates shocks in digital twins, and outputs recommended actions that planners can accept or override. DeepVu stresses that the system remains “AI-assisted decisioning” rather than fully autonomous execution, keeping human planners in the loop.⁵¹¹ This is conceptually coherent, but technical specifics—state representations, action spaces, reward functions, training regimes, convergence properties—are not disclosed. Independent evidence of performance is limited to a few university project collaborations,⁵¹¹²⁰ marketplace listings,⁶⁷ and generic positive commentary in third-party reviews.¹³⁹¹⁴ No patents specific to the claimed reinforcement-learning-for-supply-chain methods appear prominently in public directories, and there are no published benchmark studies comparing DeepVu’s algorithms to alternative approaches.

Commercially, DeepVu is a small funded company (seed-stage according to Golden and Tracxn) with a limited but non-zero footprint in supply chain analytics, including mention of engagements with “tier-1 manufacturers” in the US and Asia and with brand-name companies such as American Express, Kohl’s and SAP.¹⁵¹¹²¹¹⁷¹⁵ However, it provides very few detailed client case studies, and most references are either anonymised verticals (“manufacturing enterprises”) or generic directory listings. This suggests an early-stage vendor with technically ambitious positioning but still limited public evidence of large-scale, multi-year deployments.

DeepVu vs Lokad

DeepVu and Lokad both address supply chain planning, but they do so with markedly different technical philosophies and levels of public transparency.

Planning paradigm: multi-agent RL vs probabilistic optimisation. DeepVu frames its core value as “autonomous resilient planning” achieved via multi-agent reinforcement learning agents (VuDecide) trained on a digital twin (VuSim) to handle normal and shock scenarios.⁵²⁶⁸ The emphasis is on scenario-based decisioning: agents simulate a range of shocks (e.g., commodity spikes, COVID-style demand shifts, port congestion) and then propose candidate decision policies whose KPI impacts are compared for the planner to choose among.⁵⁶⁸²² By contrast, Lokad’s approach centres on probabilistic forecasting and optimisation, where the system computes full predictive distributions for demand and lead time, and then directly optimises economic objectives (expected profit, inventory cost, service penalties) to produce ranked lists of orders, transfers or schedules.²²²³²⁴²⁵²⁶ In Lokad’s case, the optimisation logic is expressed in a domain-specific language (Envision) and solved using stochastic optimisation algorithms (e.g., Monte Carlo plus bespoke discrete search) rather than black-box RL; the planning pipeline is a single probabilistic model from raw data to decision, not an explicit digital-twin-plus-agent stack.²⁴²⁵²⁷
Transparency and programmability. DeepVu exposes no public technical documentation on how VuDecide agents are architected or trained: no formal description of state/action spaces, reward shaping strategies, or off-line vs on-line training regimes. Its blog posts and marketing pages emphasise the conceptual idea of AI decisioning agents, but remain at narrative level.⁶⁸²² In effect, the system is presented as a closed appliance: customers see dashboards and agent outputs, not the underlying models. Lokad, in contrast, has extensive public documentation for Envision (syntax, semantics, examples), plus technical articles and lectures explaining its probabilistic models and optimisation methods, including how its M5 competition model works.²⁵²⁸²⁹³⁰²⁷ Lokad explicitly expects its customers (via “Supply Chain Scientists”) to read and even modify the code driving their optimisation; DeepVu expects them to configure agents and consume recommendations from a largely black-box engine.
Data modelling: knowledge graph vs tabular DSL. DeepVu’s technology story leans heavily on VuGraph, a supply chain knowledge graph enriched with hundreds of external signals (macro-economic indicators, commodity prices, weather, tariffs, etc.).¹⁰⁶¹⁴³⁴ VuGraph provides contextual features for both forecasting and RL agents; the knowledge graph metaphor is central to its positioning. Lokad instead works primarily with tabular datasets and a programmatic DSL: external signals (e.g., macro indicators) are added simply as additional tables, and any “graph-like” logic is encoded in Envision code, not in an explicit knowledge-graph platform.²⁴²⁵ In other words, DeepVu formalises the data model as a graph; Lokad formalises the decision logic as code with probabilistic primitives.
Recovery of shocks: digital twin simulation vs probabilistic distributions. For resilience, DeepVu uses VuSim to simulate shock scenarios (consumer spend shocks, droughts, labour shortages, trade restrictions) and then trains agents on these multi-scenario trajectories.⁵⁶⁸ The output is a set of scenarios with associated KPI outcomes. Lokad instead integrates shocks into its probabilistic distributions, for example by allowing demand and lead times to have fat-tailed or multimodal distributions and by optimising expected profit over those distributions directly; shocks are treated as rare events with non-zero probability in the distributions rather than separate scenarios in a digital twin.²³²⁶²⁷ This difference is conceptual more than purely technical, but it affects how users reason about risk (scenario selection vs distribution-aware optimisation).
Evidence base and independent validation. DeepVu can point to university collaborations (Berkeley Data-X projects), marketplace validations (Microsoft AppSource), and directories like Tracxn, Craft and Gust, along with a small set of named “engagements” (American Express, Kohl’s, SAP) and described tier-1 manufacturers.⁵⁶¹¹²¹²⁰¹⁴¹⁵ However, there is little quantitative evidence on forecasting or optimisation accuracy, no participation in public competitions, and no peer-reviewed publications. By contrast, Lokad’s forecasting methods were externally tested in the M5 Forecasting Competition, where its team ranked 6th overall out of 909 teams and 1st at the SKU level.³¹³²³³²⁹[^21Lok] While competitions are not perfect proxies for real-world value, they provide some independent evidence that Lokad’s probabilistic forecasting is technically competent. Lokad also provides numerous public case studies with named clients (e.g., Air France Industries, retailers, manufacturers), whereas DeepVu’s case-study detail is limited.
Commercial maturity and delivery model. Both companies offer cloud-based SaaS plus expert support, but with different scales and emphasis. DeepVu is a seed-funded startup with a strong focus on professional services (knowledge-graph enrichment, custom models) billed at hourly rates, and explicitly offers to “build a custom solution for you using our AI + Knowledge Graph platform”.¹⁰³ Lokad is a more mature vendor with a larger client base; its model also relies on its own experts (“Supply Chain Scientists”) but operates atop an internally consistent, publicly documented platform (Envision) rather than a mix of off-the-shelf modules and bespoke consulting.²⁴²⁵³⁴[^17Lok] From a buyer’s perspective, both require collaboration with the vendor’s experts, but the long-term risks differ: DeepVu’s platform is less transparent but potentially more opinionated around RL/digital twins; Lokad’s is more open but requires some willingness to embrace a DSL-centric modelling approach.

In short, DeepVu and Lokad both aim to automate and augment supply chain decisions under uncertainty, but DeepVu frames this as multi-agent RL over digital twins plus a knowledge graph, whereas Lokad frames it as probabilistic forecasting plus optimisation expressed in a DSL. DeepVu’s public materials lean heavily on contemporary AI labels with limited technical exposition; Lokad’s materials offer more engineering visibility and external validation. This does not prove that DeepVu’s technology is weak, only that it is harder for an external observer to assess rigorously.

Company history and evolution

Origins: Vufind and pivot to supply chains

DeepVu traces its origins to Vufind Inc, a prior startup founded by Moataz Rashad that focused on computer vision and augmented reality applications such as object recognition APIs and an AR geo-social game (vuHunt).¹⁶³⁵¹⁹ Public profiles of Rashad describe him as a hardware and software technologist with numerous patents in GPU/DSP and imaging, and note that Vufind developed products like vuMatch, vuStyle and vuGraph for e-commerce.¹⁶³⁵¹⁹ Around 2016, Vufind’s founders pivoted from AR/e-commerce into supply chain analytics, reusing data-science capabilities to build a deep-learning engine for manufacturers.

DeepVu (Vufind, Inc. doing business as DeepVu) is reported by Golden, Craft and Tracxn as founded around 2016–2017, with headquarters in San Ramon and a presence in Berkeley, CA.¹³⁶¹⁷⁴ DeepVu’s own “About Us” page states that it was founded in November 2016 by Rashad and Aref (a Purdue professor with a background in databases and Microsoft Research), and positions the company as a for-profit AI venture with a socially and environmentally conscious ethos.¹

By 2018, DeepVu appeared in the UC Berkeley Data-X ecosystem as a partner on projects focused on commodity price forecasting and supply chain optimisation for manufacturers, indicating early experimentation with deep learning and forecasting for industrial use cases.⁵¹¹²⁰ Data-X project descriptions characterise DeepVu as “a deep-learning startup focused on optimising supply chains for manufacturers” working with tier-1 manufacturers in the US and Asia, with use cases including forecasting commodity prices for bill-of-materials optimisation.¹¹²⁰

Funding and corporate status

Public funding information is limited. Golden lists Vufind (DBA DeepVu) as having a seed funding type,¹⁷ and Tracxn describes DeepVu as a “funded company” without disclosing round size.¹³⁶¹⁵ No major venture capital rounds or acquisitions involving DeepVu appear in mainstream tech news archives, suggesting a relatively modest funding scale. Directories such as Craft and Tracxn list DeepVu as a private, active company in the AI / supply chain management SaaS category with several hundred competitors in similar spaces.¹³⁶¹⁵⁴

No evidence could be found of DeepVu acquiring other companies, and there is no indication that it has itself been acquired. Corporate information sites and addresses show typical small-startup footprints rather than large multi-national operations.¹⁷⁴

Positioning shift: from “deep-learning as a service” to “autonomous resilient planning”

Early descriptions of DeepVu emphasised “deep-learning as a service” for maximising margins and supply chain intelligence for manufacturers, including use cases such as supply chain risk management, inventory forecasting, event prediction, cost optimisation and defect recognition.²²¹¹⁴ Over time, the branding shifted towards supply chain resilience, autonomous planning and agentic AI. Press releases and the company blog now describe DeepVu as “pioneering a new category called autonomous resilient planning” and repeatedly highlight “AI decisioning agents” trained on digital twins and enriched by VuGraph knowledge graph signals.¹³⁵⁶⁷⁸³⁷²²

This evolution is broadly consistent with the wider market trend: many AI vendors reframed generic ML capabilities into “AI agents”, “digital twins” and “knowledge graphs” as these became favoured buzzwords. The challenge, from a technical assessment standpoint, is that the underlying level of innovation is difficult to deduce from marketing language alone.

Product and use cases

Product line and packaging

DeepVu’s public web pages and marketplace listings suggest two main delivery modes:

SaaS subscriptions for specific planning agents – such as “AI Agent for Shock Resilient Demand Planning” available on Microsoft AppSource, and similar VuDecide agents for inventory, fulfilment and production planning.⁵⁶⁷⁸⁹
Professional services engagements – where DeepVu’s team builds custom AI + knowledge-graph solutions on top of VuGraph and VuSim for client-specific supply chain challenges, billed at an hourly rate.¹⁰³

The professional-services page explicitly states that DeepVu will “build a custom solution for you using our AI + Knowledge Graph platform and prebuilt models”, and lists use cases such as AI applications for end-to-end supply chain optimisation, deep-learning forecasting models for demand and capacity, inventory ageing, satellite-image analysis of ports and DCs, digital twin simulations with shock scenarios, and supplier risk intelligence.¹⁰³

A third-party review site (Nerdisa) summarises DeepVu as an AI-driven supply chain platform best suited for mid-market to enterprise manufacturers and retailers, emphasising “multi-agent AI decisioning” and “scenario-based recommendations” for planners.¹³⁹ While such reviews are not primary sources, they corroborate DeepVu’s own description of the product as a planning decision engine rather than a pure forecasting tool.

Demand planning and S&OP

The flagship S&OP offering appears to be VuDecide AI Agent for Shock Resilient Demand Planning, as outlined in a press release and blog article announcing availability on Microsoft AppSource.¹³⁶⁷ According to these sources:

A baseline demand forecast is taken from Dynamics 365 Sales or internal forecasts.
AI decisioning agents then integrate shock scenario planning, generating multiple shock-resilient forecasts (e.g. incorporating macroeconomic shocks to consumer spending).
Agents are enriched with hundreds of external signals from VuGraph, such as interest rates, unemployment, wages, commodity prices, export/import volumes and forex rates.⁶⁸³
Human planners can choose among the forecast scenarios, trading off KPIs such as OTIF, freight cost, inventory holding cost, labour cost, etc.⁶⁸

The blog emphasises a conceptual shift away from “traditional forecasting-centric approaches” and inconsistent MAPE-driven models towards AI decisioning agents that optimise business outcomes across scenarios.⁸ However, the implementation details—specific model architectures, time-series treatment, cross-sectional structure, error metrics, or how the RL agents interact with baseline forecasts—are not described.

Inventory optimisation and auto-replenishment

DeepVu provides dedicated pages for inventory and order fulfilment, describing VuDecide agents that learn from historical inventory levels, purchase orders (B2C and B2B), fulfilment decisions, promotions and logistics data, combined with VuGraph signals.¹¹⁹ These agents claim to:

Recommend auto-replenishment quantities at store/DC level to minimise holding cost while meeting demand.
Optimise order fulfilment decisions (which DC to ship from, whether to split orders, what shipping method to use) against KPIs such as freight cost, promised delivery dates, split-order penalties and OTIF penalties.¹¹⁹
Optimise aging stock to reduce forced promotions and liquidations by redeploying lots to higher-demand DCs while managing freight costs.¹¹
Optimise OTIF scores per retail customer DC/store per SKU per month.¹¹

Again, the narrative is plausible: RL or other ML approaches could, in principle, learn from historical fulfilment decisions and constraints to improve policies. However, no quantitative performance metrics, policy representations, or constraint-handling mechanisms are disclosed.

Procurement, production planning and risk management

The supply-chain AI page and professional-services description extend DeepVu’s scope to procurement BoM optimisation, production risk management and resilience.⁵⁸³¹² Claimed capabilities include:

Optimising bill of materials by modelling multiple suppliers, parts, price fluctuations and reliability to minimise BoM costs.⁵
Production planning decisioning intelligence that “honours product deliveries, meets profitability goals and ensures supply chain continuity”, using VuGraph signals and unstructured data feeds from factories and suppliers.⁸
Digital twin environments for assessing risk scenarios, such as pandemic disruptions, port congestion, container backlogs and trade constraints.⁵⁸
Computer-vision models for satellite imagery of ports, DCs and farms, plus product image analysis.¹⁰³

Public materials treat these as applications of the same underlying AI + knowledge-graph platform, rather than standalone products. The absence of detailed examples makes it hard to know which of these use cases are in production versus aspirational.

VuGraph: supply chain knowledge graph

VuGraph is described as a “scalable supply chain knowledge graph platform” and is arguably DeepVu’s most concrete technical artefact.¹⁰¹⁴³⁴ According to DeepVu and third-party descriptions:

VuGraph aggregates large numbers of external macroeconomic signals (CPI, PPI, unemployment, GDP ratios, interest rates, exchange rates, etc.) and potentially micro-level signals such as store sales indices.¹⁰⁶³
These signals are used to augment demand planning models and AI agents, presumably as exogenous regressors or context features.¹⁰⁶¹⁴³⁴
VuGraph is offered as a standalone “kick the tires” environment for exploring how macro signals could improve demand planning models.¹⁰³
Knowledge-graph enrichment / augmentation and visualisations are part of DeepVu’s professional services.¹⁰

What is not disclosed is the internal representation of the graph (nodes, edges, schema), the method of assigning “predictive weights” to signals, or how graph-structured learning (if any) differs from using a large table of features. Nonetheless, VuGraph does provide a somewhat more tangible asset than the generic “AI agents” narrative.

VuSim: digital twin and shock scenarios

VuSim, the digital twin simulator, is mentioned in DeepVu’s supply-chain AI and homepage content as the environment on which VuDecide agents are trained.⁵² It is said to simulate:

Normal operations.
Shocked environments such as COVID delays, demand spikes, port congestion, container backlog, geopolitical constraints, commodity price spikes, consumer spend shocks, droughts, labour shortages and trade restrictions.⁵²⁶⁸

The stated idea is “In a perfect storm of supply chain risks, to mitigate you have to simulate!”, with RL agents trained to act robustly across these simulated worlds.⁵² However, no modelling details are provided: how physical constraints are represented, how shocks propagate through the twin, how calibration to real data is performed, or how simulation errors are controlled.

Technology, architecture and AI claims

Cloud stack and integrations

DeepVu runs its model training and dashboards on cloud infrastructure, with references to both Azure and GCP clusters.¹⁰³ The platform claims “seamless integration” with ERP platforms including SAP, Microsoft, Oracle and Infor, presumably via connectors or APIs.⁵¹² This is broadly in line with standard practice for modern SaaS vendors.

Several directory listings (AppEngine, Tracxn, Craft, SuperAGI) describe DeepVu as a SaaS solution for supply chain management / wholesale / AI in the Bay Area, emphasising cloud-based deployment and multi-tenant usage.¹²³⁸¹⁴¹⁵⁴ However, none provide independent architectural diagrams or deep technical assessments.

Deep learning and reinforcement learning

DeepVu’s marketing consistently highlights deep learning and deep reinforcement learning:

The homepage calls DeepVu a “full stack” solution where “Deep Reinforcement Learning (DRL) is the most advanced, scalable, self-tuning type of Generative AI decisioning agents”.²
The supply-chain AI page refers to “multi-agent AI (Reinforcement Learning) decision models (VuDecide)” trained on top of VuSim.⁵
AppEngine and other directories mention reinforcement learning for S&OP, inventory optimisation, stockout forecasting and similar tasks.²¹⁴
Blog posts speak of “AI decisioning agents” leveraging reinforcement learning with human feedback.⁸

Despite this, there is no public description of the RL framework (e.g. policy gradient vs Q-learning, on-policy vs off-policy, continuous vs discrete actions, reward design), nor any discussion of typical RL pitfalls (sample inefficiency, non-stationarity, safety constraints) in a supply chain context. No code repositories, academic papers or patents describing DeepVu’s RL methods were found in public search.

The simplest consistent interpretation is that DeepVu uses some combination of supervised learning and RL-style policy optimisation (potentially offline RL from historical data) within proprietary models, but, from the outside, it is impossible to determine how far this goes beyond more conventional predictive models plus heuristic rules. The claims are plausible but not verifiable.

Generative AI decision models

Several pages now describe VuDecide agents as “generative AI decision models”, with language that aligns with the broader generative-AI trend.²¹¹ In context, “generative” appears to mean:

Generating decision recommendations (e.g. replenishment quantities, fulfilment choices) rather than text or images.
Possibly generating multiple scenarios (shock vs base case) for planners to select among.⁶¹¹⁸

There is no indication that DeepVu uses large language models (LLMs) as the core planning engine; generative AI here is a marketing term for RL-style decision models, not for natural-language generation.

Knowledge graph and external signals

The use of a knowledge graph is one of DeepVu’s more concrete differentiators. VuGraph aggregates a wide set of external signals:

Macro-economic indicators (CPI, PPI, unemployment, GDP ratios, interest rates, currency exchange rates).¹⁰⁶³
Sector-specific signals such as chain-store sales indices.¹⁰³
Potentially other data (e.g., satellite imagery processed by CV models, supplier filings and disclosures).[^\4]³

AppEngine and other directories highlight that DeepVu’s models leverage external signals such as commodity prices, GDP, weather, gasoline prices and tariffs in addition to internal data.²¹⁴ In principle, systematically incorporating such signals could improve forecasting and planning if they are indeed predictive; at minimum, VuGraph provides a structured repository to experiment with.

Again, the missing piece is methodology: there is no description of how signal selection, feature engineering, or regularisation is performed to avoid overfitting to noisy macro data, nor any evidence of out-of-sample performance uplift attributable to VuGraph.

Evidence vs buzzwords

Taken together, DeepVu’s technology story is dense with contemporary AI terminology—deep learning, deep RL, multi-agent decisioning, generative AI, digital twins, knowledge graphs. While none of these claims are obviously false, the public evidence is thin:

No open benchmarks, competitions or peer-reviewed publications.
No detailed tech blog posts explaining model architectures or engineering trade-offs.
No public SDKs or APIs that expose internal modelling constructs.
No explicit discussion of limitations, failure modes or negative results.

By contrast, the non-marketing evidence is limited to:

The Data-X collaborations at Berkeley, which show that DeepVu has engaged in genuine forecasting projects and had access to real industrial data.⁵¹¹²⁰³⁹
The Microsoft AppSource listing and associated press release, indicating a basic level of due diligence by Microsoft (though primarily commercial, not technical).[^^2]⁶⁷
Directory summaries (AppEngine, Gust, Craft, Tracxn, Golden) which align with DeepVu’s self-description but are not independent technical audits.¹²²¹¹⁴¹⁷¹⁵⁴
A small number of third-party review articles that provide product-manager-level evaluations but no algorithmic inspection.¹³⁹

From a sceptical standpoint, the most reasonable conclusion is that DeepVu has built a genuine machine-learning–based decision engine with some advanced elements (external signals, scenario planning, possibly RL), but the exact level of technical sophistication cannot be determined from public information and should not be assumed to match the strongest possible interpretation of its marketing phrases.

Deployment, delivery model and methodology

SaaS plus high-touch services

DeepVu’s deployment model appears to mix SaaS modules with high-touch professional services:

Customers can subscribe to VuDecide agents for specific use cases (demand planning, inventory, fulfilment) as cloud services, sometimes via marketplaces like Microsoft AppSource.¹³⁶⁷⁹
At the same time, DeepVu offers broad professional-services coverage across “most supply chain use cases”, including data engineering, automated cleansing, knowledge-graph enrichment and custom modelling, at rates of $400–450/hour depending on complexity.¹⁰³

This suggests that many engagements will be custom projects where DeepVu’s team builds solution logic on top of the platform, rather than purely self-service tooling. In this sense, DeepVu resembles a hybrid of software vendor and AI consultancy.

Data integration and digital-twin setup

The supply-chain AI page indicates that DeepVu integrates with existing ERP platforms (SAP, Microsoft, Oracle, Infor), extracting data and insights from existing supply chain systems into its AI models.⁵¹² Professional services include data engineering and cleansing for massive datasets, implying that DeepVu participates actively in building ingestion pipelines and cleaning historical data.¹⁰³

Setting up VuSim digital twins likely involves:

Modelling the client’s supply chain network (plants, DCs, ports, suppliers).
Calibrating shock scenarios (e.g., historical port congestion patterns, commodity price trajectories).
Validating simulated KPIs against historical periods.

None of this is spelled out in public documentation, but such steps are necessary to make digital twins credible. Given the complexity, it is reasonable to assume multi-month implementation projects for substantive deployments.

Human-in-the-loop decisioning

DeepVu repeatedly emphasises that its system remains AI-assisted decisioning:

VuDecide agents “recommend decisions” and provide their KPI impacts across scenarios; human planners select or override the recommended action.⁵²⁶¹¹⁸
Blog posts frame planners as “orchestrators” wielding AI superpowers, defining shock scenarios and letting the AI map them across the value chain.⁸

This is broadly aligned with best practice in high-impact decision support: full automation of complex planning under uncertainty is rarely realistic or desirable. However, the lack of public UI walkthroughs or documentation makes it hard to assess how usable or interpretable the decision recommendations actually are.

Clients, references and commercial maturity

Named clients and sectors

The Gust profile for DeepVu lists “leading companies with engagements” including American Express, Kohl’s and SAP.²¹ Data-X project write-ups mention “tier-1 manufacturers in the US and Asia” as DeepVu partners.¹¹²⁰ AppEngine and other directories describe manufacturers as the primary customer base, with some focus on FMCG, industrials and healthcare.⁵²¹⁴¹⁵

However:

DeepVu’s own site does not provide detailed, named case studies.
The listed engagements may be pilots or PoCs rather than long-term production deployments.
There is no public quantification of achieved savings, improved OTIF scores, or inventory reductions.

As such, the client evidence is suggestive but weak. A buyer should treat named logos mentioned in generic profiles as soft evidence and seek direct references.

Market positioning and competition

Tracxn positions DeepVu among several thousand AI and supply chain startups, listing over a thousand active competitors ranging from large players like Palantir and Quantexa to numerous smaller firms.¹³⁶¹⁵ Craft and Golden likewise categorise DeepVu as a small private company in the deep learning / supply chain management SaaS sector.¹⁷⁴

There is no sign of broad analyst-firm coverage (e.g., Gartner, IDC) or inclusion in major industry quadrants. This is consistent with an early-stage company with some notable partnerships (e.g., Microsoft AppSource listing) but not yet a widely recognised category leader.

Overall commercial maturity

Taking all evidence together:

Age: ~8–9 years since founding (2016).
Funding: Seed-stage; no large public rounds.
Scale: Small team across California, France and Canada (per professional-services page).¹⁰³
References: Limited public named clients; more emphasis on verticals and engagement types.
Visibility: Present in niche AI directories and university ecosystems; modest broader market visibility.

This supports a classification of DeepVu as an early-stage, commercially immature vendor with serious technical ambitions but limited public track record. For risk-averse buyers, this implies higher vendor risk and the need for careful proof-of-concepts and contractual safeguards.

Conclusion

What does DeepVu’s solution deliver, in precise terms?

Based on public information, DeepVu delivers:

A cloud-based AI decision engine (VuDecide) that produces recommended actions for supply chain planning problems—demand planning, inventory replenishment, order fulfilment, production planning and risk mitigation.
These recommendations are generated via models that learn from historical transactional data, past decisions and outcomes, augmented by external macro and micro signals stored in VuGraph.
The decision models are trained and evaluated against multi-scenario digital twins (VuSim) that simulate both normal and shock conditions.
Human planners consume these recommendations via dashboards or integrations with ERP systems, selecting or overriding actions based on KPI impacts.

In other words, DeepVu’s product is best described as an AI-driven, scenario-based decision-support system for supply chains, rather than a pure forecasting tool or classical optimisation solver.

Through what mechanisms and architectures are these outcomes achieved?

DeepVu claims to use:

Deep learning for forecasting and pattern recognition.
Deep reinforcement learning (multi-agent) for decision policies.
Generative AI decision models that propose candidate actions.
A knowledge graph (VuGraph) to structure external signals and contextual data.
A digital twin simulator (VuSim) to generate shock and normal scenarios.
Cloud infrastructure (Azure / GCP) for training and serving models.
Integrations with ERP platforms for data ingestion and action execution.

However, there is no public, technically detailed description of:

Model architectures and training procedures.
RL setup (state/action space, reward functions, exploration strategies, safety constraints).
Knowledge-graph schema and learning mechanisms.
Digital-twin calibration and validation.
System architecture (microservices, data flows, latency characteristics).

Thus, while the conceptual mechanisms are clear and plausible, the implementation details are opaque, and external observers cannot confidently assess whether DeepVu’s system is meaningfully more advanced than other ML-enhanced planning tools.

Commercial maturity and market presence

DeepVu is:

A small, seed-funded startup founded in 2016, with offices in California and some presence in France and Canada.¹¹⁰³¹⁷
Active primarily in manufacturing and retail supply chains, with engagements reported with tier-1 manufacturers and a few named companies (Amex, Kohl’s, SAP), but with limited public detail.¹¹²¹²⁰
Positioned as a full-stack AI + knowledge graph platform with both SaaS modules and consulting services, charging premium hourly rates for custom work.¹⁰³

From a buyer’s perspective, this translates into higher vendor risk (compared to established APS or supply chain vendors), but potentially greater flexibility and innovation if the technology delivers as promised. In the absence of independent performance evidence, due diligence should include:

Careful evaluation of pilot results on the buyer’s own data.
Verification of reference customers and production deployments.
Clarification of IP ownership, model portability and exit strategies if the vendor fails or is acquired.

Overall assessment

DeepVu presents a coherent and ambitious vision for AI-enabled supply chain planning: multi-agent RL agents trained on digital twins, enriched with a supply chain knowledge graph and external signals, delivering scenario-based recommendations that explicitly address shocks and resilience. The choice of conceptual tools—digital twins, RL, knowledge graphs—is consistent with modern AI research directions, and DeepVu’s engagements with academic programmes (e.g., Berkeley Data-X) and marketplaces (Microsoft AppSource) indicate that there is substantive work behind the marketing slogans.

However, from the outside, DeepVu remains a black box. Without technical documentation, code, benchmarks or detailed case studies, it is impossible to validate the depth and robustness of its implementation. A sceptical but fair reading is that DeepVu has built real ML infrastructure and some specialised models, but that its public communication is heavily buzzword-driven, and buyers should not infer state-of-the-art RL or digital-twin sophistication solely from the vocabulary used.

In relative terms, compared to vendors such as Lokad that expose their modelling stack and have external validation in international competitions, DeepVu’s technology is harder to assess and should be treated as promising but unproven at scale. For organisations with a strong appetite for innovation and the ability to run rigorous pilots, DeepVu may be worth investigating as a high-risk/high-potential partner in resilient planning. For those seeking fully de-risked, transparently documented solutions, the lack of verifiable evidence is a material concern.