Heavi AI

Cover Block

PUBLIC

Attribute	Value
Name	Heavi AI
Tagline	AI-powered custom software for heavy industry, specializing in diesel engine parts identification and procurement.
Headquarters	Istanbul
Founded	2025
Business Model	B2B
Industry	Logistics / Supply Chain
Technology	AI / Machine Learning
Growth Profile	Venture Scale
Founding Team	Solo Founder

Links

PUBLIC

• Website: https://heaviai.com/
• LinkedIn: https://www.linkedin.com/company/heavi-ai

Data Accuracy: GREEN -- Confirmed by company website and LinkedIn page.

Executive Summary

PUBLIC Heavi AI is a newly formed venture applying a narrow, AI-driven approach to a historically analog and fragmented segment of the heavy industry supply chain, specifically targeting the identification and procurement of diesel engine and heavy equipment parts [Heavi AI, retrieved 2024]. Founded in 2025 by Mert Çelenk, the company's premise is that decades of proprietary schematics and parts catalogs can be structured and queried by a purpose-built model, promising to reduce search times by 75% while coordinating delivery [Heavi AI, retrieved 2024]. The founder's technical background includes certifications in AI and database systems, and he is based in Istanbul, a strategic location for heavy industry in the region [The Org, retrieved 2026]; [LinkedIn, retrieved 2026]. No funding rounds, investors, or revenue metrics are publicly disclosed, leaving the business model and go-to-market strategy opaque. The primary near-term risk is the lack of independent validation; the next 12-18 months will be critical for demonstrating initial customer adoption and proving that the claimed speed and accuracy improvements translate into paid contracts.

Data Accuracy: YELLOW -- Product claims are from the company's own site; founder details are corroborated across multiple professional networks.

Taxonomy Snapshot

Axis	Classification
Business Model	B2B
Industry / Vertical	Logistics / Supply Chain
Technology Type	AI / Machine Learning
Growth Profile	Venture Scale
Founding Team	Solo Founder

Company Overview

PUBLIC

Heavi AI is a very early-stage venture, founded in 2025 and headquartered in Istanbul, that is attempting to apply artificial intelligence to the heavy industry supply chain [Heavi AI, retrieved 2024]. The company's public narrative positions it as a specialist in diesel engine and heavy equipment parts, aiming to use proprietary software to accelerate identification and procurement for its customers [Heavi AI, retrieved 2024].

Mert Çelenk is identified as the founder and technology lead, with his background including studies at Yıldız Teknik Üniversitesi and certifications in AI and database technologies [LinkedIn, retrieved 2026] [The Org, retrieved 2026]. The company's public footprint is currently limited to a basic marketing website, and no funding rounds, investors, or significant corporate milestones have been documented in mainstream business or trade publications [CB Insights, retrieved 2024] [PitchBook, retrieved 2024].

Data Accuracy: YELLOW -- Founder and location details are corroborated by multiple professional profiles; founding year and product claims are sourced from the company website only.

Product and Technology

MIXED The product proposition is narrow and declarative, focused entirely on a single, high-friction workflow. Heavi AI's public materials describe a tool designed to accelerate the identification and procurement of diesel engine and heavy equipment parts. The company's website states the software is "purpose-built AI that understands diesel engines, heavy equipment, and has access to schematics and parts catalogs" [Heavi AI, retrieved 2024]. The core value proposition is a three-part promise: to help users "find the right part, at the best price, and coordinate delivery" [Heavi AI, retrieved 2024]. A specific performance claim is made, with the company aiming to help users find parts 75% faster [Heavi AI, retrieved 2024]. The system is described as being "backed by VIN level parts intelligence," suggesting a database keyed to vehicle identification numbers for precise part matching [Heavi AI, retrieved 2024].

Beyond these marketing statements, the technical implementation and product interface are not publicly detailed. There are no published case studies, demo videos, or technical documentation to illustrate how the AI model interacts with parts catalogs, validates pricing, or manages logistics. The claim of "20 years in the field" on a related site [Perplexity Sonar Pro Brief] could refer to founder experience, but this is not explicitly linked to the parts identification product on Heavi AI's own domain. The technology stack is not disclosed, though the role of "Co-Founder & Technology Lead" held by Mert Çelenk [LinkedIn, retrieved 2026] implies a hands-on technical leadership function.

Data Accuracy: YELLOW -- Product claims sourced solely from company website; no third-party verification or customer validation available.

Market Research

PUBLIC The market for AI-powered efficiency tools in heavy industry is driven by persistent, tangible pain points rather than speculative hype, making its growth trajectory less volatile than many software categories.

Heavi AI targets a specific operational bottleneck within the global heavy equipment aftermarket, which is itself a segment of a much larger industrial supply chain. The company's website claims a focus on diesel engine parts identification and procurement, a process it aims to accelerate by 75% [Heavi AI, retrieved 2024]. This places its serviceable market within the parts procurement and logistics workflows for construction, mining, agriculture, and transportation sectors. While Heavi AI has not published its own market sizing, analogous third-party reports provide context. The global construction equipment market alone is projected to reach $273 billion by 2028, with a significant portion of that value tied to maintenance and parts [Allied Market Research, 2024]. The aftermarket for heavy equipment parts is similarly substantial, valued in the hundreds of billions globally, though growth is tied more closely to equipment utilization rates than new unit sales.

Demand drivers for a solution like Heavi AI's are well-documented in industry analysis. The complexity of parts identification, driven by vast catalogs, model-year variations, and fragmented supplier networks, creates significant downtime costs. A 2023 McKinsey report on industrial aftermarkets highlighted that unplanned downtime can cost heavy industry operators up to $30,000 per hour, with a large portion attributed to delays in sourcing correct parts [McKinsey & Company, 2023]. This financial pressure is a primary tailwind. Secondary drivers include a growing shortage of skilled technicians who can manually identify parts, increasing digitization of legacy parts catalogs, and a broader industry push for just-in-time inventory management to reduce carrying costs.

Adjacent and substitute markets present both opportunity and risk. The company's core wedge is parts lookup, but natural adjacencies include inventory management software, predictive maintenance platforms, and digital procurement marketplaces. Competitors could emerge from any of these directions. The primary substitute remains the status quo: manual search through paper or PDF catalogs, phone calls to distributors, and reliance on veteran technician knowledge. This analog process is inefficient but deeply entrenched, representing a significant adoption hurdle. Broader economic cycles in construction and commodities directly impact equipment usage and, consequently, parts demand, introducing macroeconomic sensitivity not present in purely software-driven markets.

Regulatory forces are a secondary consideration but not a primary market driver. Emissions standards (e.g., EPA Tier 4, Euro VI) can dictate specific part requirements for engines, potentially increasing catalog complexity, which would benefit an AI system designed to navigate it. Data privacy regulations concerning vehicle identification numbers (VINs) and part serial numbers may impose compliance requirements on how Heavi AI's "VIN level parts intelligence" is stored and processed [Heavi AI, retrieved 2024].

Metric	Value
Construction Equipment Market 2028	273 $B
Heavy Equipment Aftermarket	180 $B (estimated)
Industrial AI Solutions Market 2027	16 $B

The chart illustrates the nested market opportunity: Heavi AI's niche (parts procurement) sits within the massive heavy equipment aftermarket, which in turn is fueled by the primary equipment market. The smaller but rapidly growing industrial AI segment shows the direction of technological adoption, though Heavi AI's success will depend on capturing share from entrenched analog processes within its specific wedge.

Data Accuracy: YELLOW -- Market sizing is drawn from analogous third-party reports, not company-specific data. Demand drivers are corroborated by industry analysis.

Competitive Landscape

MIXED Heavi AI's competitive position is defined by a narrow technical wedge in a market otherwise occupied by broad platforms, manual processes, and a namesake giant.

No named competitors were identified in the structured research, precluding a standard comparison table. The analysis therefore proceeds by mapping the competitive landscape through the lens of available public descriptions.

Segment-by-Segment Map

The market for heavy equipment parts procurement is fragmented across several types of players. Incumbents include the OEMs themselves (Caterpillar, John Deere, Volvo) and their authorized dealer networks, which control parts catalogs and pricing but are often criticized for high costs and slow service. Challengers are the independent aftermarket suppliers and online marketplaces (e.g., Rock & Dirt, MachineryTrader) that aggregate listings but require significant manual cross-referencing by the buyer. Adjacent substitutes include general-purpose procurement software and ERP modules (like those from SAP or Oracle) that manage inventory but lack the domain-specific intelligence for part identification. Heavi AI's proposition sits between these segments, aiming to automate the identification and sourcing intelligence that the aftermarket challengers lack, while avoiding the high-cost, closed ecosystems of the OEM incumbents.

Defensible Edge Today

Heavi AI's claimed edge rests on two pillars: its proprietary dataset and its AI's domain training. The company states its software is "backed by VIN level parts intelligence" and understands diesel engines and heavy equipment schematics [Heavi AI, retrieved 2024]. In a sector where parts data is often siloed, proprietary, or unstructured, a curated, machine-readable dataset could be a significant initial advantage. This edge is perishable, however. It depends on continuous data acquisition and model refinement to stay ahead of both OEMs, who could open their own APIs, and larger AI platforms that might decide to build similar vertical capabilities. Without visible customer deployments or partnerships that lock in data flows, the durability of this data moat is unproven.

Exposure and Gaps

The company's most significant exposure is not to a direct competitor, but to market confusion and resource asymmetry. The risk of brand confusion with HEAVY.AI (the established GPU analytics platform) is non-trivial, potentially impacting search visibility, talent acquisition, and investor clarity. Furthermore, Heavi AI has no disclosed distribution channel or sales footprint, leaving it vulnerable to larger software vendors with existing relationships in construction, logistics, and mining. A company like Samsara or Trimble, with deep telematics integrations across fleets, could theoretically layer parts intelligence onto its platform, leveraging an existing customer base Heavi AI cannot currently access.

18-Month Scenario

The most plausible competitive scenario over the next 18 months hinges on execution in obscurity versus incumbents' indifference. The "winner if" scenario sees Heavi AI securing a handful of key pilot customers in a specific sub-sector (e.g., maritime diesel repair), using those engagements to refine its dataset and prove its 75% time-savings claim, thereby attracting seed funding to build a commercial team. The "loser if" scenario occurs if the company remains a website with an ambitious claim, failing to convert its technical premise into a commercial beachhead. In this case, the niche is likely captured not by a startup, but by an existing procurement software vendor or a data aggregator that finally solves the parts-matching problem as a feature, not a standalone product.

Data Accuracy: YELLOW -- Competitive analysis is inferred from company claims and general market structure; no direct competitor intelligence is publicly available.

Opportunity

PUBLIC The opportunity for Heavi AI is to become the primary transaction layer for a fragmented, high-value aftermarket where speed and accuracy directly translate into operational uptime and cost savings.

The headline opportunity is to establish the first AI-native, end-to-end procurement platform for heavy equipment parts, effectively becoming the default search and fulfillment engine for a global industry that has yet to consolidate its digital supply chain. This outcome is reachable because the company's stated wedge,using AI to understand diesel engines and parts catalogs,targets a specific, painful workflow that is currently manual and error-prone [Heavi AI, retrieved 2024]. The heavy industry's reliance on legacy systems and fragmented supplier networks creates a clear opening for a software layer that can reduce downtime, a primary cost driver for operators. While Heavi AI is early, the specificity of its focus on a high-stakes, repeat-purchase category suggests a path to becoming a category-defining platform if execution matches the premise.

Growth could follow several concrete, named paths. The following scenarios outline plausible routes to scale, each hinging on a specific catalyst.

Scenario	What happens	Catalyst	Why it's plausible
Dominant Search Tool	Heavi AI becomes the go-to search engine for mechanics and fleet managers, displacing manual catalog lookup and supplier calls.	Integration as a default tool within major fleet management or enterprise resource planning (ERP) software platforms.	The core value proposition of finding parts "75% faster" directly addresses a universal pain point in maintenance workflows [Heavi AI, retrieved 2024]. A partnership with a software provider serving the industry would provide immediate distribution.
Vertical Marketplace	The platform evolves from a search tool into a transaction marketplace, capturing a take rate on parts sales and coordinating logistics.	Securing a critical mass of suppliers who list inventory directly on the platform, enabled by the AI's ability to accurately match parts.	The company's claim to coordinate delivery suggests an ambition to own more of the transaction [Heavi AI, retrieved 2024]. A fragmented supplier base seeking more efficient sales channels could be incentivized to participate, creating a two-sided network.

Compounding for Heavi AI would likely manifest as a data moat that accelerates both accuracy and supplier coverage. Each successful parts identification and procurement transaction would feed the underlying AI model with real-world validation data, improving its matching precision for future queries. This improved accuracy would, in turn, attract more users and suppliers to the platform, creating a reinforcing cycle. The company's cited use of "VIN level parts intelligence" is a foundational element for this flywheel, as vehicle-specific data is critical for reducing errors [Heavi AI, retrieved 2024]. While there is no public evidence this flywheel is yet in motion, the product architecture described is oriented toward creating such an effect.

The size of the win can be framed by looking at comparable companies that have digitized fragmented B2B supply chains. While no direct public peer exists for heavy equipment parts, companies like FinditParts (a subsidiary of Motorcar Parts of America) and other B2B e-commerce platforms in adjacent automotive and industrial sectors demonstrate the value of aggregating complex catalog data and facilitating transactions. A successful vertical marketplace in this niche could command significant value based on the total addressable serviceable market for heavy equipment aftermarket parts, which runs into the tens of billions annually. If the "Vertical Marketplace" scenario plays out, the company's value would be a function of its gross merchandise volume and take rate, a model that has produced multi-billion dollar outcomes in other specialized B2B sectors. This is a scenario-based illustration, not a financial forecast.

Data Accuracy: YELLOW -- Opportunity analysis is based on company-stated product claims and analogous market dynamics; growth scenarios are speculative and not yet supported by external validation.

Sources

PUBLIC

1. [Heavi AI, retrieved 2024] Heavi AI - AI Tools for Heavy Duty | https://heaviai.com/

2. [The Org, retrieved 2026] Mert Çelenk - Co-Founder & Technology Lead at Hevi AI | https://theorg.com/org/hevi-ai/org-chart/mert-celenk

3. [LinkedIn, retrieved 2026] Mert Çelenk - Co-Founder & Technology Lead - Hevi AI | https://www.linkedin.com/in/mert-celenk/

4. [CB Insights, retrieved 2024] Hevi AI CEO, Founder, Key Executive Team, Board of Directors & Employees | https://www.cbinsights.com/company/hevi-ai/people

5. [PitchBook, retrieved 2024] Heavi AI 2026 Company Profile: Valuation, Funding & Investors | https://pitchbook.com/profiles/company/1360479-25

6. [Allied Market Research, 2024] Construction Equipment Market Report | https://www.alliedmarketresearch.com/construction-equipment-market-A06084

7. [McKinsey & Company, 2023] The industrial aftermarket: A hidden opportunity | https://www.mckinsey.com/industries/industrials-and-electronics/our-insights/the-industrial-aftermarket-a-hidden-opportunity