Technology

Fine-Tuning the Future of AI: Argonautic is proud to support the evolution of the AI ecosystem and the entrepreneurs powering the innovation

Published

2 years ago

September 30, 2024

SEATTLE, Sept. 30, 2024 /PRNewswire/ — In the evolving landscape of artificial intelligence and machine learning, foundation models – the backbone of predictive tasks – have captivated the tech world. Their ability to perform learning tasks are transforming the way we approach natural language processing, computer vision, and signal processing. At Argonautic, while we acknowledge the pivotal role foundation models play in the AI value chain, it is our perspective that the verticalization of these models under direction of teams with unparalleled subject matter expertise and access to proprietary training data aligns with our capital efficient thesis versus new generalized foundation models which require high upfront training costs initially and face price commoditization in the long run.

Foundation models are pre-trained deep learning models that serve as the versatile and general base for computationally-intensive predictive tasks. Foundation models are then ‘fine-tuned’ to perform function specific tasks for a given use case. The term was coined by Stanford Academics in 2021 and surged in popularity in 2022 to describe the models which were breaking out at the time. Foundation models are especially recognized for their ability to perform ‘zero-shot’ and ‘few-shot’ learning tasks, where a task is performed with few or zero examples previous given to the model.

Argonautic believes that while the evolution of foundation models is crucial for the advancement of AI and technology overall, business models which focus on building these baseline models have high capital requirements and likelihood of commoditization over time. Instead, we believe teams building models with strong subject matter expertise and knowledge of the problem to be solved (which may be built on these generalized models) will reliably come out ahead in solving the most important problems. These teams have unique access to proprietary data that can be used to train their fine-tuned models and unique distribution channels that more seamless inserts AI-powered tools into business workflows.

Foundation models are commonly used for a variety of natural language processing, computer vision, and signal processing tasks. Open AI’s “GPT-N” series captured the general public’s attention with its ability to craft coherent seeming text given a wide range of prompts. At its core, GPT-N simply predicts the next word in a sentence, which when scaled produces coherent seeming responses. It is trained on a large “corpus” of text data sourced mainly from the open internet but also from, forums, publications and books.

Argonautic maintains a strategic focus on industries with use cases that require verticalized models. While recognizing the importance of foundation models in advancing AI and machine learning, it is clear that the concentration on constructing baseline models carries inherent limitations. Instead, Argonautic partners with teams with strong subject matter expertise to build models tailored to specific problem domains, leveraging their unique access to proprietary data and distribution channels. In the context of foundation models, Argonautic acknowledges the widespread applicability in natural language processing, computer vision, and signal processing tasks. By emphasizing the importance of fine-tuning to achieve verticalization, Argonautic underscores the ability of companies to specialize their models while benefiting from the underlying foundation model’s conversational interface. In this landscape, Argonautic positions itself against significant capital deployment in general foundation models due to diversification risks and concerns about the potential disruption posed by open-source models and new architectures.

For a generalized foundation model to become ‘verticalized’, it must be “fine-tuned” by passing in an extra set of domain specific data to tailor the generalized model for a use case. This allows companies to specialize their models while still benefiting from the conversational interface of the underlying foundation model. OpenAI’s ChatGPT, Alphabet’s Gemini, Meta’s Llama and others are foundation model driven businesses, which enables teams to build while avoiding billions of dollars of initial training costs.

Training a foundation model from scratch is a large, expensive, and important data engineering undertaking. The architecture of these models typically rely on transformers, which have been the industry standard for a number of years. Where it differs is the scale. The success of foundation models depends on the ability to seamless aggregate vast amounts of data with trillions of parameters. At the time of writing, this costs in the order of billions of dollars and will only grow as customer demands outpace the cost trends of computation and storage.

Argonautic does not believe general foundation models to be an area of capital deployment given our investment style. First, they require large checks which create diversification risk for our investors. Second, we are wary of the risk of open-source foundation models and new architectures disrupting the economics of proprietary models. For example, Retrieval Augmented Generation has changed the way enterprises look at retraining. Staying on are ahead of the curve is expensive and risky.

Pre-dating the explosion of interest in private sector machine learning models, Argonautic believes the value of a model comes from a few areas: (1) unique architecture which gives it a technical or economic advantage (2) proprietary data which lets the model produce unique insights (3) ability to integrate seamlessly into existing workflows. Unique architecture is often spun off from academic institutions with heavy financial backing. As such our area of interest is in teams who have demonstrated the ability to use their unique insight to solve a specific problem. Teams in the space tend to work on verticalized foundation models which take general foundation models a step further with proprietary expertise.

For example, our portfolio company Cognaize, which automates financial spreading for large financial institutions, has accumulated years of financial data which allows it to fine-tune a defensible, verticalized foundation model in the financial technology space. Similarly, Document Crunch, which analyzes construction contracts for conflicting language, uses a corpus built over a number of years to produce exceedingly accurate results for its customers. ConCntric’s platform allows it collect data which will eventually inform its own powerful predictive model. The specific problems our protein engineering teams solve cannot be adequately addressed by a general model. The model’s differentiation for our teams is only possible because of the expertise of the overall team and is not reliant on a lasting technical edge.

As such, more important than ever, Argonautic is interested in teams that know the problem and market they are solving better than anyone else. This also protects companies from future disruption. Even with the next generation of trends, such as automated ‘AI agents’, we believe that teams with strong subject matter expertise are equipped to stay ahead of the pack. It is our view that general foundation models will never be able to solve a specific more reliably than a combination of an elite team that understands a problem and verticalized foundation model.

Argonautic is proud to have been deploying into AI since our founding. As technologists, we are excited to watch the field continue to change the world and as investors we see the opportunity to support this growth.

About Argonautic:

Founded in 2017, Argonautic is a AI/ML B2B venture capital fund investing across Fintech, Construction Tech and Biotech. Argonautic invests in entrepreneurs who are redefining the future of technology and innovation.

For more information, visit argonauticventures.com.

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SOURCE Argonautic

Technology

Samsung Selects Chandigarh University Student as ‘Punjab AI State Topper’

Published

15 minutes ago

July 18, 2026

CU Student Devesh Panwar Wins Rs 1 Lakh Award for Developing AI-Based Document Search System

CHANDIGARH, India, July 18, 2026 /PRNewswire/ — A Bachelor of Computer Applications (BCA) student of Chandigarh University, Devesh Panwar, has been named the “Punjab State Topper” in the Artificial Intelligence (AI) under the Samsung Innovation Campus (SIC) AI Program organised by Samsung in collaboration with Telecom Sector Skill Council (TSSC) and training partner Focal Skill Development (Focalyt) to equip Indian youth with industry-relevant in-demand skills in AI, loT, Big Data, and Coding & Programming, preparing them for future careers.

A final-year student of Bachelor of Computer Applications (BCA) at University Institute of Computing (UIC) of Chandigarh University, Devesh Panwar also won a cash award of Rs 1 lakh along with a laptop, and exclusive Samsung rewards for achieving this remarkable milestone under the Samsung Innovation Campus AI Program.

“Being named ‘AI State Topper’ was a moment of profound pride and validation for me. Hard work, intense technical training and a passion for AI culminated in the immense honor of the Punjab State Topper title. Besides getting access to state-of-the-art tools to fuel my future research and development endeavor, I gained world-class knowledge during the Samsung Innovation Campus (SIC) AI Program. It helped me in gaining hands-on experience and applied AI to solve real-world challenges, especially in aeronautical and technical domains,”

During Samsung Innovation Campus (SIC) AI Program, Devesh and his team worked on innovative AI capstone project which was presented before the jury panel as part of the final assessment.

“Our team developed the advanced AI-powered system AI Research Agent, a Retrieval-Augmented Generation (RAG) platform designed for intelligent querying of private document collections. Organizations, researchers, legal professionals, and HR teams often work with large collections of documents. Traditional search methods rely heavily on keyword matching and require significant manual effort to locate relevant information. The project was developed to address the limitations of traditional document search systems, which often struggle with contextual understanding, retrieval accuracy, learning adaptability, and response speed,” said Devesh.

“The developed system demonstrated significant improvements over traditional document retrieval approaches with faster response generation with integration of modern AI techniques, full-stack development, database systems, and intelligent automation into a single practical solution. By combining modern RAG architecture with innovative retrieval and reasoning mechanisms, this system provides a fast, intelligent, and user-friendly solution for knowledge discovery and resume evaluation. This project not only enhanced our technical expertise in Artificial Intelligence and Full-Stack Development but also strengthened our problem-solving, teamwork, and professional communication skills. It stands as a significant achievement of our Samsung Innovation Campus 2025 journey and reflects our commitment to building impactful AI solutions for real-world challenges,” he added.

Congratulating Devesh Panwar for being named “Punjab State Topper” AI under the Samsung Innovation Campus Samsung Innovation Program, Deepinder Singh Sandhu, Senior Managing Director, Chandigarh University, said, “Devesh’s achievement reflects Chandigarh University’s focus on experiential learning and industry-academia collaboration. Through the Samsung Innovation Campus AI Program, CU students gain practical exposure to AI and building solutions for real-world challenges. This accomplishment reflects not only Devesh’s commitment to excellence but also the growing culture of innovation and industry-oriented learning at Chandigarh University,”.

“Since its inception, Chandigarh University has set benchmarks for world-class education with its dynamic hands-on experiential learning model, industry-aligned programs, dynamic fraternity, state-of-the-art infrastructure facilities and impeccable placements. Samsung Innovation Program is also part of our initiatives to develop a future-ready talent pool equipped with advanced capabilities in AI, loT, Big Data, and Coding and Programming. By bridging the gap between theoretical learning and real-world application, this collaboration helps in providing CU’s computer science and computing students exposure to cutting-edge and emerging technologies. CU’s this partnership with Samsung is focused on building future-ready talent equipped with industry-relevant skills in AI and emerging technologies,” Sandhu said.

He said Chandigarh University’s Institute of Computing (UIC) prepares students for a successful career in computing, to create and disseminate computing knowledge and technology. “Chandigarh University carries a vision of crafting next-gen IT professionals who can take up industry challenges effectively and our Institute of Computing (UIC) prepares students for a successful career in computing, to create and disseminate computing knowledge and technology. UIC’s hands-on approach paves the way for a smooth transition to the workforce after graduation. Our students are equipped with the best knowledge, skills and passion to succeed in any number of computing careers. CU’s Institute of Computing renders cutting-edge education ranging from the expertise in traditional software development -to- modern computing technologies. Fully-equipped industry-sponsored labs, industry-aligned curriculum, and accreditations and validations by top companies such as Intel, Microsoft, Google Android, Red Hat etc. give our students an exclusive edge over others,” the Chandigarh University CMD said.

Sandhu said the latest edition of QS World University Rankings has yet again reaffirmed Chandigarh University global standing as a top educational institution. “Continuing its remarkable rise among the world’s leading higher education institutions, Chandigarh University (CU) has made impressive strides in the latest edition of prestigious QS World University Rankings 2027 by securing an overall world rank of 526, an increase of 49 ranks as compared to 575th rank in QS’ 2026 Rankings. This is for the fifth consecutive year that Chandigarh University’s global rankings have witnessed an impressive surge with CU’s world rank going up by an impressive 274 ranks — from the 800th rank in 2023 to 526th in 2027’s Rankings,”.

“As per the latest QS World University Rankings, with All India Rank of 13 among all universities in the country as compared to 16th rank in 2026’s rankings, Chandigarh University now ranks among the top 1% of universities in India and the top 2% of universities in the World, underscoring its growing reputation as a leading institution of higher learning, both in India and globally,” he added.

About Chandigarh University

Chandigarh University is a NAAC A+ Grade University and QS World Ranked University. This autonomous educational institution is approved by UGC and is located near Chandigarh in the state of Punjab. It is the youngest university in India and the only private university in Punjab to be honoured with A+ Grade by NAAC (National Assessment and Accreditation Council). CU offers more than 109 UG and PG programs in the field of engineering, management, pharmacy, law, architecture, journalism, animation, hotel management, commerce, and others. It has been awarded as The University with Best Placements by WCRC.

Website address: https://www.cuchd.in/

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Technology

Samsung Selects Chandigarh University Student as ‘Punjab AI State Topper’

Published

15 minutes ago

July 18, 2026

CU Student Devesh Panwar Wins Rs 1 Lakh Award for Developing AI-Based Document Search System

During Samsung Innovation Campus (SIC) AI Program, Devesh and his team worked on innovative AI capstone project which was presented before the jury panel as part of the final assessment.

About Chandigarh University

Website address: https://www.cuchd.in/

View original content to download multimedia:https://www.prnewswire.com/in/news-releases/samsung-selects-chandigarh-university-student-as-punjab-ai-state-topper-302828991.html

Technology

Black Lake Technologies Shortlisted as SAIL Award TOP30 Finalist and Selected as Global Industrial AI Flagship Case, Showcasing Latest Industrial Agent at WAIC 2026

Published

1 hour ago

July 18, 2026

SHANGHAI, July 18, 2026 /PRNewswire/ — The 2026 World Artificial Intelligence Conference (WAIC) opened in Shanghai on July 17. Shanghai Blacklake Technologies Co., Ltd. (“Black Lake”), an industrial AI company, is showcasing a portfolio of industrial AI agents at the conference. The company has also been named to the Top 30 shortlist for the 2026 WAIC Super AI Leader (SAIL) Award and selected as a Trusted Partner under the Global Call for Trusted Partners for Industrial AI in the Global South.

The accreditations highlight Black Lake’s latest progress in bringing AI into critical manufacturing decision-making workflows and deploying industrial AI capabilities on the shop floor around the world.

This year’s conference attracted over 1,100 exhibiting companies and showcased more than 3,000 exhibits, setting a new record for exhibition scale. The conference delivered a clear signal: as artificial intelligence becomes a common priority across global industries, attention is moving beyond model capabilities toward practical applications in real-world operating environments.

Manufacturing provides a particularly demanding test for this transition. Factory operations are governed by multiple constraints, including process specifications, equipment capabilities, material availability, production capacity, delivery schedules and quality requirements. Therefore, AI has to do so much more than simply comprehend information input. It must make reliable judgments within clearly defined business rules and operational constraints.

Black Lake has focused on industrial digitalization and industrial AI for years, developing and deploying AI applications in a range of factory environments.

At WAIC 2026, the company is presenting industrial AI agents covering order splitting and process planning, quotation and pricing, procurement, production scheduling, quality inspection, and order tracking. These applications are designed to move AI beyond an auxiliary role and into critical manufacturing decision-making workflows.

Traditional industrial software is primarily responsible for data recording, digital workflows, and worker coordination. However, critical decisions such as how to split an order, determine pricing, schedule production, and assess quality risks still depend heavily on the experience of engineers and frontline workers.

Industrial AI agents are intended to convert fragmented industrial knowledge and production experience into decision-making capabilities that can be invoked, reused and continuously refined by software systems.

Order decomposition and process planning are representative examples. After receiving an engineering drawing, a factory typically relies on experienced engineers to identify components, materials and dimensions, define the required manufacturing processes and technical specifications, and establish a basis for subsequent quotation and quality inspection.

The process is highly dependent on individual expertise and represents one of the first critical decision points after an order is received.

Black Lake Technologies’ CAD-to-Process Agent can understand product drawings and, taking into account the factory’s equipment capabilities, process requirements, and production practices, rapidly generate process steps along with the corresponding technical requirements. Drawing analysis that once took hours can now be completed in approximately one minute, achieving an accuracy rate of over 95% in real deployment and providing engineers with stable, efficient decision support. Currently, the industrial agents developed by the company cover core processes including design, scheduling, production, and quality inspection, and have entered the stage of large-scale deployment.

Founded in 2016, Black Lake serves nearly 40,000 factories worldwide. Its customers span more than 30 industries, including food and beverage, automotive components and equipment manufacturing.

By working across factory order management, production and fulfillment workflows, Black Lake has accumulated the technical capabilities and industry knowledge required to support decision-making in complex industrial environments.

In April 2026, Black Lake completed a Series D funding round of nearly RMB 1 billion. The company said the proceeds would primarily be used to accelerate the deployment of its industrial AI products and support its international expansion.

AI-related products are becoming a new source of growth for the company. In a recent interview, Black Lake founder and CEO Zhou Yuxiang said that the company had recorded significant growth in AI-related revenue since the beginning of 2026. He also said that manufacturing customers were taking less time to make purchasing decisions for industrial AI agents.

Zhou expects AI adoption among Chinese factories to increase substantially over the next three to four years.

Unlike consumer-facing AI, which is primarily associated with content generation and personal productivity, industrial AI agents can directly affect production costs, capacity utilization, delivery performance, and product quality. Their commercial value therefore depends largely on whether they can perform specific tasks reliably in complex production environments.

During WAIC 2026, Black Lake was named to the Top 30 shortlist for the 2026 Super AI Leader (SAIL) Award. The SAIL Award is one of WAIC’s major awards and recognizes achievements in technological breakthroughs, application innovation, and industrial value.

Black Lake was also selected as a Trusted Partner under UNIDO’s Global Call for Trusted Partners for Industrial AI in the Global South.

The Global Call was launched under the guidance of the United Nations Industrial Development Organization (UNIDO), in partnership with the Shanghai Artificial Intelligence Research Institute, and in connection with the work of UNIDO AIM Global and its Shanghai-based Centre of Excellence.

The initiative aims to build a curated pool of leading partners to co-develop scalable industrial AI solutions and public goods for the Global South.

For Black Lake, the two accreditations underscore the growing importance of reliability, explainability, and scalability in the evaluation of industrial AI, in addition to the capabilities of AI models.

Global expansion will be a major priority in the company’s next phase of development. Black Lake is currently focusing on Southeast Asia, Latin America and Eastern Europe, adapting its industrial AI agents to the industrial structures, production processes and management requirements of different markets.

Although manufacturing operations vary across countries and regions, manufacturers share similar concerns about efficiency, quality, delivery reliability and production flexibility.

Black Lake is transforming industrial AI capabilities that have been validated in complex factory environments into configurable and deployable products. Through these products, the company aims to work with manufacturers worldwide to explore more efficient, flexible and intelligent approaches to production.

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SOURCE Black Lake