Deepbloo
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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.
Deepbloo RedditDeepbloo'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.
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From urllib.parse import urlparse Def normalize_gh(r): return { "title": r["name"], "url": r["url"], "source": "github", "score": r["stars_this_period"], "desc": r.get("description", ""), "date": r["trending_date"], "lang": r.get("language"), } Def normalize_hn(p): return { "title": p["title"].replace("Show HN: ", ""), "url":... - Source: dev.to / 3 months ago
@tool Def search_reddit(keywords: str, max_results: int = 20) -> list[dict]: """Fallback: search Reddit directly via PRAW.""" reddit = praw.Reddit( client_id=os.environ["REDDIT_CLIENT_ID"], client_secret=os.environ["REDDIT_CLIENT_SECRET"], user_agent="doug-agent/1.0", ) candidates = [] for submission in reddit.subreddit("all").search(keywords, sort="new",... - Source: dev.to / 3 months ago
Import requests Import time Def fetch_subreddit_posts(subreddit, sort="hot", limit=25): url = f"https://www.reddit.com/r/{subreddit}/{sort}.json" params = { "limit": limit, "raw_json": 1, # Prevents HTML encoding in responses } headers = { "User-Agent": "PythonScraper/1.0 (research project)" } response = requests.get(url, params=params, headers=headers) if... - Source: dev.to / 4 months ago
From sessionkeeper import SessionKeeper Async with SessionKeeper("reddit") as sk: page = await sk.get_authenticated_page("https://reddit.com") # You're logged in. Do your automation. await page.goto("https://reddit.com/r/blender/submit"). - Source: dev.to / 4 months ago
It's completely free, and takes just moments to set up - you just need to create an account, and set up keywords for the service to track. When your keywords are mentioned on Reddit, Hackernews, or Lobste.rs, you'll get a tidy little email in your inbox. - Source: dev.to / over 1 year ago
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