Evaltic

Cover Block

Public sources

Name	Evaltic
Tagline	Evaluation infrastructure for supply chain and manufacturing AI, ensuring AI quality ties to business outcomes.
Headquarters	New York City, NY
Founded	2026
Stage	Pre-Seed
Business Model	SaaS
Industry	Logistics / Supply Chain
Technology	AI / Machine Learning
Growth Profile	Venture Scale
Founding Team	Solo Founder

Links

Public sources

• Website: http://ww12.evaltic.com/
• LinkedIn: https://www.linkedin.com/company/evaltic

Executive Summary

Public sources Evaltic is building the evaluation infrastructure to make AI agents trustworthy enough for critical supply chain and manufacturing workflows, a bet that deserves attention because the operational risks of unvetted AI are becoming a primary blocker to enterprise adoption. Founded in January 2026 by Maureen Erokwu, the company provides tooling to validate AI decisions against business rules and constraints before execution, aiming to move AI from pilot projects into reliable production [evaltic.com, retrieved 2026] [LinkedIn, retrieved 2026]. The founder's background includes roles at Apple and Google, and she previously founded Vosmap, a Google-backed digital mapping company, bringing experience in scaling technology ventures [LinkedIn, retrieved 2026] [theglasshammer.com, 2015]. As a pre-seed SaaS company, Evaltic has not publicly disclosed any venture funding rounds but has gained early validation through acceptance into the Claude for Startups program [LinkedIn, June 2026]. The next 12-18 months will be defined by the company's ability to convert its technical positioning into named enterprise deployments and to secure its first institutional capital, proving that its evaluation wedge can unlock scaled AI autonomy in a historically conservative sector.

Lightly corroborated -- Core product claims are sourced from the company's own materials; founder background is partially corroborated by multiple sources. No independent verification of funding, customers, or commercial traction.

Taxonomy Snapshot

Axis	Classification
Stage	Pre-Seed
Business Model	SaaS
Industry / Vertical	Logistics / Supply Chain
Technology Type	AI / Machine Learning
Growth Profile	Venture Scale
Founding Team	Solo Founder

How the Company Got Here

Public sources

Evaltic is a newly formed entity, established in January 2026 by solo founder Maureen Erokwu. The company is headquartered in New York City and operates in the AI infrastructure space, specifically targeting supply chain and manufacturing workflows [LinkedIn, retrieved 2026]. Its public narrative centers on providing the evaluation tooling necessary for enterprises to safely deploy AI agents in critical operational environments, a proposition that has gained early validation through its acceptance into the Claude for Startups program [LinkedIn, June 2026].

As a pre-seed stage venture, Evaltic's founding story is still being written. The founder's background includes prior roles at Apple and Google, as well as founding a previous Google-backed mapping company, Vosmap [LinkedIn, retrieved 2026] [theglasshammer.com, 2015]. This experience in large-scale technology operations and entrepreneurship forms the backdrop for the new venture. The company's key milestones to date are limited to its founding and program participation; no public funding rounds, customer announcements, or product launch events have been documented.

Lightly corroborated -- Founder role and company description confirmed via LinkedIn and company website; accelerator participation cited in social posts. No independent verification of founding date or corporate status.

Product and Technology

Sources and analysis The company's public positioning frames its product as a control layer, not a model. Evaltic describes its core offering as evaluation infrastructure for AI agents operating in supply chain and manufacturing workflows [evaltic.com, retrieved 2026]. The product's stated purpose is to orchestrate autonomy across these agents, with a specific focus on safety and validation before execution [evaltic.com, retrieved 2026].

Functionally, the tooling appears designed to intercept and check agent decisions against predefined business rules, operational constraints, and contextual guardrails [evaltic.com, retrieved 2026]. This suggests a system that sits between planning AI models and execution systems in areas like logistics, warehouse operations, procurement, and production planning. The company emphasizes linking technical AI performance to measurable business outcomes by embedding experienced operator judgment into structured evaluation workflows [LinkedIn, retrieved 2026].

• Targeted use cases. Public mentions include support for predictive maintenance, defect detection, supply-chain forecasting, and Manufacturing Execution System (MES) copilot workloads, with an implied focus on Fortune 500 manufacturers [futureagi.com, retrieved 2026].
• Technology stack (inferred). Given the focus on evaluating and validating AI agent actions, the underlying technology likely involves rule engines, workflow orchestration, and logging/analytics platforms. Its acceptance into the Claude for Startups program suggests compatibility or planned integration with Anthropic's Claude model ecosystem [LinkedIn, June 2026]. No details on proprietary algorithms, data pipelines, or deployment models are publicly available.

Lightly corroborated -- Product claims are sourced from the company's own website and LinkedIn profile; use cases are cited from a third-party industry article. Technical stack and architecture details are not publicly confirmed.

Where the Demand Sits

Public sources The market for AI in supply chain and manufacturing is moving from isolated pilots toward production-scale deployments, creating a new layer of infrastructure to manage the associated risks and performance gaps.

Third-party market sizing specifically for AI evaluation infrastructure in supply chain is not yet available. However, the broader AI in supply chain management market provides a relevant analog. According to a report from SmartDev, the global market for AI in supply chain management was valued at approximately $6.5 billion in 2023 and is projected to reach $21.8 billion by 2028, representing a compound annual growth rate of 27% [smartdev.com, retrieved 2026]. This growth is driven by the pursuit of efficiency, resilience, and cost reduction across complex, globalized operations.

2023 | 6.5 | $B
2028 (projected) | 21.8 | $B

The projected near-tripling of the market within five years underscores the scale of investment flowing into operational AI, which in turn creates demand for the governance and validation tools Evaltic aims to provide.

Demand drivers for a specialized evaluation layer are emerging from several concurrent trends. The proliferation of AI agents for tasks like dynamic routing, predictive maintenance, and automated procurement increases the surface area for operational failure if agent decisions are not properly constrained [futureagi.com, retrieved 2026]. Enterprises are also facing heightened pressure to demonstrate responsible AI use, linking model performance directly to business outcomes like on-time delivery rates and inventory costs [LinkedIn, retrieved 2026]. These drivers suggest a wedge for infrastructure that sits between the AI models and the execution systems, ensuring actions align with business rules before they are carried out.

Key adjacent markets include traditional supply chain planning software, process mining tools, and broader AI observability platforms. Regulatory and macro forces are also shaping the landscape. Geopolitical tensions and climate-related disruptions are forcing companies to build more agile, transparent supply chains, a task increasingly delegated to AI systems that require robust oversight. While no specific AI regulations for supply chain exist yet, the broader push for AI accountability in the EU and US could eventually mandate stricter validation requirements for autonomous systems in critical infrastructure, potentially accelerating adoption of tools like Evaltic's.

Lightly corroborated -- Market sizing is an analogous projection from a single source; demand drivers are inferred from cited product claims and industry commentary.

Competitive Landscape

Sources and analysis

Evaltic enters a market where the competitive pressure comes less from direct feature-for-feature rivals and more from the strategic choices of incumbents and the emergence of adjacent tooling.

Company	Positioning	Stage / Funding	Notable Differentiator	Source
Oracle Fusion Cloud SCM	End-to-end supply chain management platform with embedded AI/ML capabilities.	Public company (ORCL)	Deep integration of AI features (demand forecasting, logistics) within a dominant enterprise ERP/SCM suite.	[Oracle]

The landscape can be segmented into three layers. First, the incumbent platform providers like Oracle, SAP, and Blue Yonder. Their primary advantage is the installed base; their AI features are built-in components of a broader workflow, not standalone evaluation tools. For these vendors, the competitive threat is not displacement but disintermediation, where a customer uses an external tool like Evaltic to govern the AI agents running on the incumbent's platform. Second, the horizontal AI evaluation and observability companies such as Arize, WhyLabs, and Galileo. These firms offer generalized tooling for monitoring model performance and data drift but are not purpose-built for the specific rules, constraints, and operational contexts of supply chain workflows. Third, a nascent category of agentic operations platforms, exemplified by Trase, which secured a $107 million seed round to build an "agentic operating system" for autonomous AI networks [LinkedIn, July 2026]. This category is adjacent but not directly overlapping; an operating system provides the runtime environment, whereas Evaltic's infrastructure is positioned as the validation layer on top.

Evaltic's stated defensible edge today is its vertical focus on supply chain and manufacturing operators. The company's messaging consistently ties AI quality to measurable business outcomes by embedding experienced operators into evaluation workflows [LinkedIn, retrieved 2026]. This focus on a specific domain's rules and constraints is a classic wedge against horizontal tooling. The durability of this edge, however, is perishable and hinges on two factors: the speed at which the company can codify proprietary domain knowledge into its product, and its ability to secure early design partners who validate its approach. Without a library of validated constraints and proven outcome improvements, the edge remains theoretical. Participation in the Claude for Startups program provides access to technical resources and a founder community [LinkedIn, June 2026], but it is not a commercial moat.

The company's most significant exposure is to the strategic moves of the incumbents. Oracle, for instance, could decide to enhance the governance and validation features within its Fusion Cloud SCM suite, effectively bundling a solution and negating the need for a third-party tool. Furthermore, Evaltic is exposed on the distribution front. It lacks the enterprise sales motion and channel partnerships that the platform incumbents have cultivated over decades. Convincing a Fortune 500 manufacturer to adopt a point solution from a pre-seed startup, rather than relying on or extending their existing SAP or Oracle investment, represents a steep go-to-market challenge. The company also does not yet own a critical data asset, such as a benchmark dataset of supply chain agent behaviors, that would be difficult for others to replicate.

The most plausible 18-month competitive scenario involves consolidation of focus. If Evaltic can successfully land and expand within a few flagship manufacturing or logistics customers, demonstrating clear ROI on agent reliability and risk reduction, it could establish itself as the de facto evaluation layer for that vertical. The "winner" in this scenario would be a company like Trase, if the market converges on a need for integrated agent platforms that include native evaluation, rather than standalone tooling. Conversely, the "loser" would be the horizontal AI observability firms if they fail to develop the deep domain-specific workflows that enterprise operations teams require. For Evaltic, the path is narrow but defined: prove the wedge works before incumbents react or before the market decides evaluation is a feature, not a product.

Lightly corroborated -- Competitive positioning is based on company statements and one named competitor; funding and stage data for competitors is limited.

Opportunity

Public sources The prize for a company that successfully builds the trust infrastructure for AI in the global supply chain is measured not in billions of dollars of software spend, but in the trillions of dollars of physical commerce it would enable to be automated with confidence.

The headline opportunity for Evaltic is to become the de facto validation layer for all autonomous decision-making in enterprise supply chains, a role analogous to what Stripe became for payments or what Datadog became for observability. The company's early positioning, focusing on embedding operator expertise into structured evaluation workflows, targets the precise point of friction preventing AI agents from moving from pilot to production [LinkedIn, retrieved 2026]. This outcome is reachable because the problem is acute and unsolved. Major logistics and manufacturing firms are actively deploying AI for predictive maintenance, defect detection, and planning, but lack a standardized, auditable system to govern these agents' actions before they impact physical operations [smartdev.com, retrieved 2026]. A platform that provides this governance becomes a non-negotiable piece of infrastructure, embedded at the core of increasingly autonomous workflows.

Growth is not a single path but a branching set of plausible scenarios, each with a distinct catalyst.

Scenario	What happens	Catalyst	Why it's plausible
The Oracle Co-pilot	Evaltic's evaluation tooling becomes a mandated add-on for enterprises running Oracle Fusion Cloud SCM and similar suites, capturing a large, pre-qualified customer base.	A formal technology partnership or integration with a major SCM platform provider.	The company explicitly cites use cases like "MES copilot workloads for Fortune 500 manufacturers," indicating a focus on augmenting, not replacing, incumbent systems [futureagi.com, retrieved 2026]. The competitive landscape includes Oracle, suggesting a target environment.
The Regulator's Seal	Industry consortia or insurance providers mandate a third-party validation layer for AI-driven supply chain decisions, and Evaltic's framework becomes the certified standard.	A high-profile operational failure attributed to an unvalidated AI agent triggers new industry guidelines.	The product's core promise is "safely execute logistics... by validating your agents' decisions against... rules and constraints before actions are taken," directly addressing liability and compliance concerns [evaltic.com, retrieved 2026].
The Claude Ecosystem Anchor	Evaltic evolves from a program participant into the primary evaluation suite for all supply chain agents built on Anthropic's Claude models, leveraging exclusive tooling and distribution.	Deep technical integration and co-marketing as part of the ongoing Claude for Startups relationship.	The company's acceptance into the program provides early, privileged access to Anthropic's ecosystem and a signal of technical alignment [LinkedIn, June 2026].

Compounding in this model looks like a data flywheel built on trust. Each new enterprise deployment adds more domain-specific rules, constraint libraries, and failure scenarios to Evaltic's knowledge base. This growing corpus of "what not to do" makes the validation engine more precise and valuable for the next customer in a similar vertical, creating a data moat that is difficult to replicate without equivalent scale and operational context. Early signs of this flywheel starting are not yet public, but the mechanism is inherent to the product's function as a system of record for AI decision audits.

Quantifying the size of the win requires looking at comparable infrastructure plays. Samsara, which provides the operational visibility layer for physical logistics, reached a market capitalization of approximately $20 billion following its IPO. While Samsara monitors outcomes, Evaltic's proposed role is to govern the decisions that lead to those outcomes, a potentially equally critical and valuable layer. If the "Oracle Co-pilot" scenario plays out and Evaltic captures a 5% penetration of the global SCM software market (a market projected to exceed $20 billion annually by several analyst firms), the resulting revenue base could support a multi-billion dollar valuation. This is a scenario-based illustration, not a forecast, but it frames the magnitude of the opportunity for a category-defining infrastructure company.

Lightly corroborated -- The opportunity analysis is based on the company's stated positioning and target markets, which are confirmed. Growth scenarios and market comps are plausible extrapolations but lack direct, current evidence of traction.

Sources

Public sources

1. [evaltic.com, retrieved 2026] Evaltic | More autonomy for agents. More control for your team. | http://ww12.evaltic.com/

2. [LinkedIn, retrieved 2026] Maureen Erokwu - Founder & CEO - Evaltic | https://www.linkedin.com/in/maureen-erokwu-b0b0b0b0/

3. [LinkedIn, retrieved 2026] Evaltic LinkedIn Company Profile | https://www.linkedin.com/company/evaltic

4. [White Label Expo] Maureen Erokwu | https://whitelabelexpo.com/speakers/maureen-erokwu

5. [LinkedIn, June 2026] LinkedIn post by Sasha Reid about Claude for Startups | https://linkedin.com/posts/sashareid_google-for-startups-accelerator-australia-

6. [LinkedIn, July 2026] LinkedIn post about Trase funding with Evaltic mention | https://linkedin.com/posts/greatentrepreneurs_trase-has-secured-a-107-million-seed-funding-

7. [theglasshammer.com, 2015] Mover and Shaker: Maureen Erokwu, CEO, Vosmap | https://theglasshammer.com/2015/11/mover-and-shaker-maureen-erokwu-ceo-vosmap/

8. [smartdev.com, retrieved 2026] AI in Supply Chain Management: Top Use Cases You Need To Know | https://smartdev.com/ai-use-cases-in-supply-chain-management/

9. [futureagi.com, retrieved 2026] AI in Supply Chain Management: Top Use Cases You Need To Know | https://smartdev.com/ai-use-cases-in-supply-chain-management/

10. [Oracle] Oracle Fusion Cloud SCM | https://www.oracle.com/scm/