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Deepbloo centralizes French public procurement data and international tenders to help you anticipate projects, monitor competitors, and identify the right opportunities.
DEV.to DeepblooDeepbloo's answer:
Deepbloo's answer:
Deepbloo stands out by focusing on high-quality, structured intelligence rather than simple tender aggregation in Energy and infrastructure markets
Its key differentiators are:
In short, Deepbloo is designed to reduce noise and surface high-value opportunities earlier, enabling more efficient and strategic business development.
Deepbloo's answer:
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.
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.
Deepbloo's answer:
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.
In short, Deepbloo is designed for teams that need both operational visibility on tenders and strategic insight on markets to drive growth.
Deepbloo's answer:
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:
In short, Deepbloo was born from a very practical problem in the field and built to solve it in a scalable, technology-driven way.
Deepbloo's answer:
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 Donnรฉes de Montpellier, bringing cutting-edge academic expertise into the platformโs AI capabilities.
In short, Deepbloo combines industrial-grade data infrastructure with specialized AI to transform complex, unstructured procurement data into actionable intelligence.
As a mini-blog, it is a nice alternative for Medium to publish and share information about programming.
However, the community and the organization are biased toward social justice (and they are open to it). You can read its Code of Conduct, it is so vague and politically leads (I prefer a term of service because it defines fair rules for everybody). So it alienates developers that we don't care about politics in pro of people that want to talk about any other topic such as sexuality, how women are unprivileged, and such. It even mandates to use inclusive language. Good grief.
My main complaint is the quality of the community. It is not StackOverflow (so we don't want to ask for an answer here), and most of the top topics are clickbait, such as "how to become a rockstar developer in ... days", "100 tips to become a better programmer" (and it doesn't even talk about programming).
Technically this "mini blog" site allows us to use markdown, and it is okay. However, the whole experience is really basic. Even the template is ugly.
Based on our record, DEV.to seems to be more popular. It has been mentiond 651 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
The search box on the homepage now runs keyword, semantic, and hybrid search, with a toggle so you can compare and watch them disagree. Type pydub and flip to semantic mode to see it get the answer wrong; flip to hybrid to see it get it right again. The whole thing is a 4 MB lookup table, a tiny document index, and about 300 lines of dependency-free JavaScript, lazy-loaded only when you focus the search box so the... - Source: dev.to / 1 day ago
Start tracking costs from day one with a tool like Tokonomics. Start charging when AI costs exceed 15% of revenue or when you see a clear 10x+ variance between your lightest and heaviest users. Early-stage startups can absorb costs temporarily for growth, but set the expectation early that AI features have usage-based pricing. - Source: dev.to / 1 day ago
Python -m pip install unlimited-search Unlimited-search read https://dev.to --max-content-chars 1500. - Source: dev.to / 8 days ago
While developing Wasp, a JS full-stack framework, we keep researching other ecosystems (Rails, Laravel, Django, etc.) and finding ways how they figured out developer productivity. We kept finding these reusable legos, so we gave them a name: "full-stack modules". Let's define what we mean by that exactly. - Source: dev.to / 16 days ago
If you want to see where your site sits in this distribution, run an audit โ it takes about 12 seconds. - Source: dev.to / 20 days ago
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