Aug 2026· Zenodo (CERN European Organization for Nuclear Research)
Competitive and Knowledge Intelligence
Abstract
Background: Competitive intelligence work is routinely scattered across company websites, press releases, industry publications, patent and funding databases, and news feeds. Most organizations still assemble this picture by hand, in spreadsheets and static slide decks that are out of date the moment they are finished. Objective: This paper documents Frontier, an AI-enabled platform for continuous competitor and partner intelligence, and its evolution from a single-company research tool into a reusable, general-purpose SaaS application. Frontier lets a user enter the name of any company and receive a live, scored brief covering collaboration candidates, competitors, and market-expansion opportunities, alongside a rolling feed of relevant news. Overview: We describe the system's architecture, its use of large language model (LLM) inference for entity discovery and scoring, and the transparent, documented rubric that underlies every compatibility and opportunity score the platform produces. Limitations of conventional approaches: Manual research does not scale past a handful of competitors, spreadsheets do not capture the reasoning behind a judgment, and periodic reviews (quarterly or annual) miss developments that happen in between. Origin and generalization: Frontier began as a purpose-built research tool for Ecotera Asia's EcoExposure™ platform, with a fixed, hand-researched list of competitors and partners in the environmental-diagnostics and water-quality-monitoring space. It was subsequently rebuilt so that any company name can be analyzed on demand, generalizing the underlying architecture beyond a single industry. Contributions: This work contributes (i) a working, publicly deployed implementation of an on-demand competitor-intelligence pipeline; (ii) a documented, transparent scoring rubric intended to make AI-generated judgments auditable rather than opaque; and (iii) a case study showing how the same architecture served both a narrow, industry-specific need and a general-purpose product. Live application: frontier2.vercel.app Source code: github.com/sanviagarwal7211-a11y/frontier2
The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.
Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al.· IEEE Transactions on Softwar...· 178 citations· ⚡14
Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.
M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al.· e-Informatica Software Engin...· 157 citations· ⚡17
This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.
Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al.· Empirical Software Engineeri...· 127 citations· ⚡15
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
The ongoing work building a Raspberry Pi cluster consisting of 300 nodes is presented, with potential use cases being an inexpensive and green test bed for cloud computing research and a robust and mobile data center for operating in adverse environments.
P. Abrahamsson, S. Helmer, Nattakarn Phaphoom et al.· IEEE International Conferenc...· 110 citations· ⚡7
The results indicate that software developers are a slightly happy population, but the need for limiting the unhappiness of developers remains, and 219 factors representing causes of unhappiness while developing software are identified.
D. Graziotin, Fabian Fagerholm, Xiaofeng Wang et al.· International Conference on...· 84 citations· ⚡6
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduSep 14, 2026
The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.
AI may appear weightless, but every model depends on physical infrastructure. To understand responsible AI, we need to look beyond algorithms and consider the entire lifecycle of the hardware behind them. The post Responsible AI Must Consider Its Afterlife appeared first on GPT-Lab.