Deepbloo Reviews and Details

Questions & Answers

1. Who are some of the biggest customers of Deepbloo?

   • Engie
   • Terralpha (SNCF)
   • EDF
   • General Electric
   • Siemens
   • Idex
   • Coriance
   • TSG Solutions
   • Alphee
   • Newheat

2. What makes Deepbloo unique?

   Deepbloo stands out by focusing on high-quality, structured intelligence rather than simple tender aggregation in Energy and infrastructure markets

   Its key differentiators are:

   • Deep coverage of the French market , combined with high coverage for international and donor-funded opportunities
   • Advanced data structuring, making each opportunity directly usable (sector, buyer type, project context)
   • Full-text analysis of documents, not just titles or CPV codes, to capture highly relevant tenders
   • Detection of upstream signals (projects, investments, authorizations) before tenders are publishe
   • Decision-oriented approach, helping teams quickly identify, prioritize, and act on the most strategic opportunities

   In short, Deepbloo is designed to reduce noise and surface high-value opportunities earlier, enabling more efficient and strategic business development.

3. Why should a person choose Deepbloo over its competitors?

   A company should choose Deepbloo over other tendering platforms because it is designed to deliver more relevant, decision-ready insights with a superior user experience, especially in complex sectors like energy.

   • User-centric interface: Deepbloo is built for fast navigation and clarity, allowing users to quickly access, filter, and understand opportunities without being overwhelmed by noise.
   • Energy-sector specialization with AI models: Dedicated AI models analyze technical criteria such as installed capacity, technology type (solar, wind, storage), and project characteristics directly from documents, making it far easier to identify truly relevant opportunities.
   • Advanced understanding of the French ecosystem: Deepbloo provides structured insights on public buyers, including local authorities and state entities, helping users understand who is behind each project and how the administrative landscape is organized.
   • Higher relevance, less noise: Through full-text analysis and smart filtering, users spend less time sorting through irrelevant tenders and more time focusing on high-value opportunities.

   In short, Deepbloo combines ease of use, sector-specific intelligence, and deep market understanding to provide a more efficient and strategic alternative to traditional platforms.

4. How would you describe the primary audience of Deepbloo?

   The primary audience of Deepbloo consists of professionals involved in business development, sales, marketing, and strategic decision-making, particularly in sectors driven by public procurement such as energy and infrastructure.

   • Sales Directors / Commercial Teams use Deepbloo to access comprehensive and structured information on tenders, enabling them to respond more effectively and ultimately increase win rates and revenue.
   • Business Development Managers rely on early-stage intelligence (upcoming projects, local authority decisions, investment signals) to position themselves upstream, well before tenders are officially published.
   • Marketing Managers use the platform to assess market potential, especially in export markets, by identifying opportunity volumes, key geographies, and sector dynamics.
   • Strategy and Executive Teams leverage Deepbloo for competitive intelligence (who won what, where, and why), as well as for understanding market size, trends, and positioning.

   In short, Deepbloo is designed for teams that need both operational visibility on tenders and strategic insight on markets to drive growth.

5. What's the story behind Deepbloo?

   Deepbloo was founded in 2020 by Alexandre Guillemot, a former Business Development Director at General Electric and Alstom.

   During his time developing international business through public tenders, he repeatedly faced the same issue: missing critical opportunities due to fragmented and incomplete information. Tracking tenders across multiple countries, platforms, and formats was time-consuming, unreliable, and often led to lost deals.

   Frustrated by this inefficiency, he decided to build Deepbloo with a clear objective: ensure that no strategic opportunity is missed.

   To achieve this, he brought together a team combining strong industry expertise in energy and infrastructure with advanced capabilities in data aggregation and artificial intelligence. The goal was not just to collect tenders, but to create a platform capable of structuring, analyzing, and enriching data at scale.

   The result is a solution that reflects both:

   • Deep operational understanding of how tenders drive business
   • High technical standards in AI and data processing

   In short, Deepbloo was born from a very practical problem in the field and built to solve it in a scalable, technology-driven way.

6. Which are the primary technologies used for building Deepbloo?

   Deepbloo is built on a combination of large-scale data engineering and advanced artificial intelligence, designed to handle complex and fragmented procurement data environments.

   • Data collection and aggregation technologies The platform relies on robust data pipelines capable of collecting information from a wide range of sources (public platforms, institutional databases, international portals). These systems are designed to handle millions of data points, continuously ingesting, normalizing, and updating information.

   • Data structuring and deduplication A key layer of the technology focuses on cleaning, deduplicating, and structuring data, as the same opportunity can appear across multiple sources and formats. This ensures that users access consistent, reliable, and non-redundant information.

   • Document processing at scale Deepbloo retrieves and processes large volumes of documents (tender specifications, annexes, technical files), making them searchable and usable for further analysis.

   • Artificial intelligence (AI) and domain-specific models The platform combines state-of-the-art AI models with proprietary models trained specifically on tender data. These models extract key business information, analyze technical criteria, and support advanced use cases such as opportunity qualification or automated summaries.

   • Research partnerships in AI Deepbloo collaborates with leading research institutions such as LaBRI and Institut des Sciences des Données de Montpellier, bringing cutting-edge academic expertise into the platform’s AI capabilities.

   In short, Deepbloo combines industrial-grade data infrastructure with specialized AI to transform complex, unstructured procurement data into actionable intelligence.