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Bulkbeat TV was built around a simple but hard problem: financial data is everywhere, but actionable signal is rare.
As Project Manager at Sitekraft.dev, I delivered a Telegram-first product that continuously watches market disclosures and related news sources, then decides which events deserve to become alerts. The result was a production system that behaves less like a generic news bot and more like a selective intelligence layer for traders.
Fetching updates from NSE and market websites is not the interesting part. Many bots can scrape headlines and forward them. The real engineering problem starts after ingestion:
That framing changed the whole architecture. Instead of optimizing for message count, I optimized for signal quality.
The system follows a multi-stage flow:
Each stage exists to remove noise before the user sees anything.
This bot needed to do many small tasks repeatedly: poll sources, download documents, parse content, score events, check payments, run scheduled jobs, and respond inside Telegram. A blocking design would have wasted time and memory.
Using a fully async Python architecture made the runtime practical for a small VPS deployment. It allowed the bot to keep moving across concurrent network-heavy tasks — polling, downloading, parsing, scoring, and responding — without turning the system into a heavyweight backend.
In this kind of workflow, OCR is not just a nice feature. It is a reliability feature.
Some filings are image-heavy or difficult to extract cleanly. If the enrichment layer fails, the AI layer receives weak context, and weak context leads to weak alerts. Adding OCR fallback made the pipeline much more resilient because it improved the quality of text available for scoring and formatting.
One of the strongest lessons from this project was that a premium alerting product should usually send fewer messages, not more.
To enforce that, I leaned on:
That choice shaped the whole product. It made the bot more trustworthy and reduced the chance of users tuning it out.
User features alone were not enough. A live product also needs operational visibility.
That is why I built separate admin-side controls for tasks like:
This prevented the system from becoming a black box after deployment.
Bulkbeat TV reinforced a few ideas I care about deeply:
This project represents the kind of engineering I want to keep doing: product-focused backend systems that combine automation, AI, document processing, user delivery, and operational thinking in one deployable workflow.
It also reflects something important about my approach. I enjoy building systems where the hard part is not just making the code run, but deciding how the product should behave under real constraints.