Khenda

Cover Block

PUBLIC

Name	Khenda
Tagline	Data infrastructure for Physical AI, converting industrial video into structured training data for humanoid robots.
Headquarters	Istanbul, Turkey
Founded	2021
Stage	Seed
Business Model	SaaS
Industry	Deeptech
Technology	AI / Machine Learning
Geography	Western Europe
Growth Profile	Venture Scale
Founding Team	Co-Founders (2)
Funding Label	Seed (total disclosed ~$1,750,000)

Links

PUBLIC

• Website: https://www.khenda.com
• LinkedIn: https://www.linkedin.com/company/khenda

Executive Summary

PUBLIC Khenda sells a software platform that uses standard cameras to analyze factory floor video, converting raw footage into structured data for manufacturing efficiency and, more recently, for training humanoid robots [khendarobotics.com]. The company's immediate investor appeal lies in its capital-light approach to a persistent industrial problem, using existing video feeds instead of costly hardware retrofits to deliver cycle time analysis and work instruction generation. Founded in 2021 by brothers Çağkan and Aykan Ekici in Istanbul, the company has built its product around a core of computer vision expertise, with Aykan bringing over eight years of experience in the field [Dealroom.co].

The platform operates on a SaaS model and has demonstrated initial traction, with one cited deployment saving 80% of analysis time and improving line efficiency by 15% for a client [LinkedIn, Aykan Ekici, Retrieved 2026]. To scale, Khenda closed a $1.75 million seed round in 2024 from investors including Diffusion Capital Partners and Simya VC, and has since established a strategic presence in Ann Arbor, Michigan, with local ecosystem support [Khenda Blog, 2024] [Purpose Jobs, Retrieved 2026]. Over the next 12-18 months, the key watchpoints are the validation of its newer positioning as a data infrastructure provider for Physical AI, the conversion of its undisclosed pilot engagements into named enterprise logos, and the execution of its international expansion from its Turkish base.

Data Accuracy: YELLOW -- Core product claims and seed funding amount are confirmed by primary company sources; team background and one client result are corroborated. Conflicting secondary reports on total funding and round structure introduce some uncertainty.

Taxonomy Snapshot

Axis	Classification
Stage	Seed
Business Model	SaaS
Industry / Vertical	Deeptech
Technology Type	AI / Machine Learning
Geography	Western Europe
Growth Profile	Venture Scale
Founding Team	Co-Founders (2)
Funding	Seed (total disclosed ~$1,750,000)

Company Overview

PUBLIC

Khenda Teknoloji A.Ş. was founded in 2021 in Kadıköy, Istanbul, by Çağkan Ekici, who is now listed as co-founder and CEO [CompanyCheck, Dec 2024]. The company's initial focus was on applying computer vision to manufacturing operations, a direction shaped by the co-founders' background in the field. Aykan Ekici, a co-founder and co-CEO, brought over eight years of computer vision project experience to the venture [Dealroom.co].

The company's development has been supported by participation in structured programs, including the KWORKS'20 Hızlandırma Programı at Koç University and the K-Startup Grand Challenge, a South Korean accelerator [Khenda Blog, 2024]. A significant milestone was the closure of a seed funding round in 2024, raising $1.75 million from investors Diffusion Capital Partners, Simya VC, and the Koç Group Companies' First Private Equity Investment Fund [Khenda Blog, 2024]. Following this, Khenda established a strategic presence in the United States, setting up a virtual desk in Ann Arbor, Michigan, with support from the local economic development organization Ann Arbor SPARK to facilitate growth and contacts in the North American market [Purpose Jobs, Retrieved 2026].

Data Accuracy: GREEN -- Confirmed by company press release, corporate registry data, and economic development organization.

Product and Technology

MIXED

Khenda's core offering is a software platform that uses computer vision to analyze standard video feeds from manufacturing floors, aiming to replace manual observation and expensive sensor integrations. The system ingests video from existing cameras or smartphone uploads to automatically generate engineering analyses that would otherwise require significant manual labor. This includes calculating cycle times, distinguishing between value-added and non-value-added work steps, and performing methods-time measurement (MTM) analyses. The company positions this as a pragmatic wedge into industrial operations, turning ubiquitous video into a diagnostic layer without requiring hardware retrofits [Perplexity Sonar Pro Brief].

• Core Modules. The platform is organized into several functional modules: Digital Time Study, Work Instructions, MTM, a Lean Assistant for identifying waste, Line Balancing tools, and a real-time production monitoring system the company calls Vision MES [Perplexity Sonar Pro Brief].
• Client Workflow. A case study posted by co-founder Aykan Ekici details the application for a client, ISAIC. The platform was used to analyze real production video, which reportedly saved 80% of the time compared to manual analysis and improved line efficiency by 15%. The software identified non-value-added steps, optimized standard operating procedures (SOPs), and helped balance workloads [LinkedIn, Aykan Ekici, Retrieved 2026].

Beyond its immediate manufacturing efficiency tools, the company's website articulates a broader, forward-looking thesis: positioning its video-derived data as "data infrastructure for Physical AI." The platform is described as converting industrial video into structured training data for humanoid robots, suggesting the captured workflows and environmental interactions could serve as a foundational dataset for robotics development [khendarobotics.com]. This represents a potential second act for the technology, though no public partnerships or product launches specific to this robotics data layer are yet confirmed.

Data Accuracy: YELLOW -- Product claims are consistent across the company's own materials and a detailed third-party brief, but the robotics data infrastructure angle lacks independent corroboration or detailed technical disclosure.

Market Research

PUBLIC The market for AI-driven manufacturing efficiency tools is expanding as companies seek to digitize legacy processes without heavy capital investment in new hardware. Khenda operates at the intersection of two established but evolving sectors: industrial computer vision and manufacturing execution software. The company's wedge of using standard video feeds to generate structured engineering data positions it to address a pain point that has historically required either manual labor or expensive sensor integration.

A precise TAM for Khenda's specific offering is not available in public third-party reports. However, analogous market sizing provides context. The global market for manufacturing execution systems (MES) was valued at $13.2 billion in 2022 and is projected to reach $27.2 billion by 2030, according to a report from Grand View Research cited by the company [Khenda Blog, 2024]. The broader industrial AI market, which includes predictive maintenance and visual inspection, is frequently sized in the tens of billions. These figures suggest the addressable pool for efficiency-focused software is substantial, though Khenda's initial SAM is likely a fraction of this, focused on discrete manufacturers with assembly lines amenable to video analysis, such as in apparel and automotive.

Demand is driven by several persistent tailwinds. Labor shortages and rising wage costs in manufacturing hubs increase the return on investment for automation that improves workforce productivity. The push for supply chain resilience and nearshoring also creates pressure to maximize output from existing factory floors. Furthermore, the maturation of computer vision models has lowered the technical barrier to extracting actionable insights from video, a shift Khenda explicitly leverages by positioning against "expensive sensors or PLC integrations" [Perplexity Sonar Pro Brief].

Key adjacent markets include traditional time-and-motion study consulting, legacy MES and SCADA systems, and newer IoT platform providers. These represent both potential partners and substitutes. The regulatory environment is generally favorable, with governments in regions like the EU and US promoting industrial digitization initiatives, though data privacy regulations concerning employee video monitoring could introduce compliance considerations for deployments.

MES Market 2022 | 13.2 | $B
MES Market 2030 (projected) | 27.2 | $B

The projected near-doubling of the MES market by 2030 underscores the ongoing digitization of factory floors, but Khenda's success will depend on capturing share from incumbents with its vision-first, lighter-touch approach.

Data Accuracy: YELLOW -- Market sizing is based on an analogous report cited by the company; demand drivers are inferred from industry trends and the company's stated positioning.

Competitive Landscape

MIXED Khenda enters a market defined by two distinct competitive axes: traditional manufacturing software suites and a newer wave of AI-first vision platforms.

Company	Positioning	Stage / Funding	Notable Differentiator	Source
Khenda	AI-powered continuous improvement platform using standard video for time studies, line balancing, and robot training data.	Seed, ~$1.75M	Focus on low-friction deployment via existing cameras; dual positioning in operational efficiency and Physical AI data.	[Khenda Blog, 2024], [Perplexity Sonar Pro Brief]
Landing AI	Computer vision platform for visual inspection and quality control in manufacturing.	Series A, $57M	Strong brand recognition from founder Andrew Ng; focus on defect detection and quality assurance workflows.	[Crunchbase]
SafeAI	Autonomous heavy equipment retrofit kits for mining and construction.	Series B, $47M	Targets heavy industry with full-stack autonomy solutions, a different application of physical AI.	[Crunchbase]
Drishti Technologies	AI-powered video analytics for manual assembly lines to track cycle times and identify bottlenecks.	Series C, $100M+	Deep focus on manual assembly lines in automotive and electronics; extensive patent portfolio in action recognition.	[Crunchbase]

Khenda's competitive map is segmented by both application depth and technical approach. In the broad manufacturing optimization software category, incumbents like Siemens (with its Opcenter suite) and Dassault Systèmes provide comprehensive MES and digital twin solutions, but these are capital-intensive, integration-heavy platforms. Khenda's wedge is its lightweight, camera-first approach, competing on agility and cost of deployment rather than breadth. More directly, it faces AI-native challengers like Drishti, which also uses video to analyze manual assembly lines. Drishti's significant funding and established customer base in automotive represent a formidable head-to-head competitor in the time-and-motion study niche [Crunchbase]. Adjacent substitutes include providers of wearable sensors or IoT puck systems for worker tracking, which offer precise data but require physical hardware changes on the shop floor.

Khenda's current defensible edge appears to be its specific technical focus on generating structured, engineering-grade outputs (MTM analyses, line-balancing) from unstructured video, a workflow that sits between pure observation and prescriptive action. This edge is tied to its proprietary computer vision models trained on industrial video, which constitutes a data moat that could deepen with more deployments. However, this edge is perishable; the core technology of extracting pose and action data from video is not exclusive, and larger competitors with more resources could replicate the feature set. The company's secondary positioning as "data infrastructure for Physical AI" is a forward-looking differentiator not yet directly contested by the named competitors, but it remains an unproven market claim.

The company is most exposed in two areas. First, in direct competition with Drishti, Khenda lacks the latter's reported nine-figure funding, proven enterprise sales motion, and public roster of Fortune 500 manufacturers [Crunchbase]. Second, its model depends on manufacturers being willing to install cameras and share sensitive video data, a cultural and regulatory hurdle that hardware-based sensor companies or offline simulation software do not face. Khenda does not own a protected distribution channel or a regulatory certification that would block competitors.

The most plausible 18-month scenario is one of continued segmentation. If the demand for quick, software-only continuous improvement tools grows faster than expected, Khenda could win as a nimble, specialist provider for small to mid-sized manufacturers in Turkey and its expansion markets like Michigan. The loser in that scenario would be the incumbent heavyweight suites, which are too cumbersome for such targeted use cases. Conversely, if the market consolidates around platforms that offer both video analytics and deeper integration with PLCs and ERP systems, a better-funded player like Drishti or a strategic acquirer would likely capture the value, leaving standalone point solutions like Khenda vulnerable.

Data Accuracy: YELLOW -- Competitor profiles and funding stages are sourced from Crunchbase, but Khenda's own differentiation claims are from its materials and a third-party brief.

Opportunity

PUBLIC The prize for Khenda is a foundational position in the digitization of physical industry, converting the unstructured video of factory floors into a structured data asset that powers both operational efficiency today and autonomous systems tomorrow.

The headline opportunity is for Khenda to become the default data infrastructure layer for Physical AI in manufacturing. This is not merely an aspirational pivot; it is a logical extension of the company's existing wedge. Khenda's core platform already ingests industrial video to generate structured engineering outputs like cycle times and work instructions [Perplexity Sonar Pro Brief]. This process inherently creates a labeled, time-series dataset of human and machine activity. The company's website explicitly frames this output as "structured training data for humanoid robots" [khendarobotics.com]. The plausibility of this outcome rests on the convergence of two trends: the urgent need for manufacturing efficiency, which Khenda already addresses, and the nascent but heavily funded race to develop humanoid robots for industrial labor. By establishing itself as the tool of choice for process optimization, Khenda could capture the video data that will be essential for training these next-generation systems, positioning its platform as a critical, upstream data utility.

Growth could follow several distinct, concrete paths. The following scenarios outline how Khenda might scale from its current seed-stage position.

Scenario	What happens	Catalyst	Why it's plausible
Dominant Continuous Improvement SaaS	Khenda becomes the standard software for lean manufacturing and industrial engineering teams globally, displacing manual methods and legacy tools.	A major partnership with a global industrial automation or ERP provider (e.g., Siemens, Rockwell, SAP) to embed Khenda's video analytics.	The company's documented 80% time savings and 15% line efficiency improvement for a client demonstrates clear ROI [LinkedIn, Aykan Ekici, Retrieved 2026]. Its pitch as a camera-based alternative to expensive hardware retrofits is a compelling wedge [Perplexity Sonar Pro Brief].
Essential Robotics Training Data Vendor	The company pivots its data output, becoming a primary supplier of labeled, real-world activity datasets to robotics companies and AI labs.	Securing a paid R&D partnership with a leading humanoid robotics developer (e.g., Tesla, Figure, 1X Technologies) to use Khenda's platform for data generation.	The company's public positioning already emphasizes this use case [khendarobotics.com]. The dataset generated as a byproduct of its core SaaS business has inherent value for simulation and reinforcement learning.
Verticalization in High-Mix Apparel	Khenda achieves deep penetration in the fashion and apparel manufacturing sector, becoming the operational system of record for complex, labor-intensive assembly lines.	A flagship deployment with a major global apparel brand leads to a multi-site rollout and a formal preferred vendor status.	The company has specifically cited empowering engineering teams in fashion and apparel as a target [LinkedIn, Aykan Ekici, Retrieved 2026]. This vertical has high variability and manual work, maximizing the value of video-based analysis.

Compounding for Khenda would manifest as a data network effect within and across manufacturing sites. Each new production line analyzed adds to a proprietary corpus of industrial activity. This dataset could improve the accuracy of the company's own AI models for time study and anomaly detection, creating a better product that attracts more customers,a classic data flywheel. Early signs of this are suggested by the company's ability to identify non-value-added steps and optimize standard operating procedures for its cited client [LinkedIn, Aykan Ekici, Retrieved 2026]. Furthermore, as the platform is adopted across a manufacturer's global footprint, switching costs rise due to the accumulation of historical process data and trained site-specific models, creating a form of operational lock-in.

The size of the win, should the dominant SaaS scenario play out, can be framed by looking at comparable public companies. Samsara (NYSE: IOT), which provides IoT data platforms for physical operations including manufacturing, achieved a market capitalization of approximately $20 billion following its IPO. While Samsara's model includes hardware, its valuation underscores the market's appetite for software that digitizes and optimizes physical workflows. A more direct, though private, comparable is Drishti Technologies, a competitor also using video AI for manufacturing analytics, which has raised over $100 million [Crunchbase]. If Khenda successfully executes its land-and-expand motion and captures a meaningful share of the global manufacturing efficiency software market, an outcome in the hundreds of millions to low billions of dollars in enterprise value is a plausible upper bound (scenario, not a forecast).

Data Accuracy: YELLOW -- The core product capabilities and early traction metrics are confirmed by company and founder sources. The growth scenarios are logical extrapolations from the company's stated positioning and demonstrated use cases, but the specific catalysts and comparable valuations are not yet supported by direct, public evidence of partnerships or financial benchmarks.

Sources

PUBLIC

1. [khendarobotics.com] Khenda | The Data Infrastructure for Physical AI | https://khendarobotics.com/

2. [Khenda Blog, 2024] Press Release: Khenda Closes $1.75M Seed Round to Transform Manufacturing with AI | https://www.khenda.com/post/press-release-khenda-closes-1-75m-seed-round-to-transform-manufacturing-with-ai

3. [Dealroom.co] Khenda company information, funding & investors | https://app.dealroom.co/companies/khenda

4. [CompanyCheck, Dec 2024] Khenda company profile | https://companycheck.co.uk/company/OC443536/KHENDA-TEKNOLOJI-AS/companies-house-data

5. [Purpose Jobs, Retrieved 2026] Why This Turkish AI Startup Wanted in on Ann Arbor’s Tech Scene | https://www.purpose.jobs/blog/turkish-ai-startup-moves-to-ann-arbor

6. [Perplexity Sonar Pro Brief] Khenda product and market brief | https://www.perplexity.ai/

7. [LinkedIn, Aykan Ekici, Retrieved 2026] Aykan Ekici LinkedIn post detailing ISAIC case study | https://www.linkedin.com/in/aykanekici/

8. [Crunchbase] Landing AI company profile | https://www.crunchbase.com/organization/landing-ai

9. [Crunchbase] SafeAI company profile | https://www.crunchbase.com/organization/safeai

10. [Crunchbase] Drishti Technologies company profile | https://www.crunchbase.com/organization/drishti-technologies