Fullstack Developer (Telegram Mini App / AI Integration)

Hello!

I have experience in developing Telegram Mini Apps and full-stack solutions (React + Node.js / FastAPI). I have worked on projects with API integration, data caching, and connecting LLM (including generating structured responses).

I am ready to design the architecture, implement analytics, and build a stable TMA by your deadline (2–3 weeks).

We can discuss the details and get started right away.

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Good day👋
Roman, I see here not just TMA, but a product with data, paywall, and AI logic - these are exactly the tasks I can handle.
I will create the architecture from scratch: a fast front end for Telegram, a backend with caching of sports feeds, integration of LLM for structured predictions, and triggers through Bot API.
I am also considering the verification of actions and database scaling separately.
I have experience in Telegram bots and Mini Apps, so I can quickly assemble a working MVP in 2-3 weeks and bring it to a quality release.
Shall we discuss the details?

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Hello!

Hello!

I looked at the responses — many offer a standard stack, but in your project, the main difficulty is not in the technologies, but in the combination of real-time data + LLM + Telegram UX within tight deadlines.

If we go "head-on," there are risks:
— overload of API providers (limits + delays)
— unstable responses from LLM (break JSON / UI)
… — lags in the Telegram Mini App with live data

How I would implement the MVP in 2–3 weeks:

Data and caching
Separate the streams:
— prematch (cache for hours)
— live (update every 10–15 seconds via Redis)
This will significantly reduce the load and speed up TMA.
AI analytics
Do not query LLM "on the fly" for each user.
Generate forecasts in advance (cron/queue) + store as structured JSON.
This will provide stability and instant feedback.
Telegram Mini App
Focus on speed:
— minimal payload
— aggressive caching on the front end
— preloading key data
Paywall
I will immediately incorporate server-side validation + postback logic to prevent access from being bypassed.

Regarding timelines:
👉 It is realistically possible to meet the 3-week deadline if we clearly define the MVP (without overloading with functionality)

A little about me:
— experience with Telegram Mini Apps and bot API
— worked with FastAPI / Node.js + LLM (JSON mode, prompt engineering)
— completed projects with real-time data and caching

I can offer:
— a quick start (breaking it down into phases over 3 weeks)
— a clear architecture without "over-engineering"
— a focus on making it work reliably, rather than just "getting it done"

Let's discuss: I can quickly draft an MVP plan and specify what will definitely fit within the timeline and what is better to postpone.

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Hello, my last similar project: https://step.app/ - An interactive platform with real-time data, user dashboards, and high-load backend systems.

Also:

https://triend.io/

An app with API integration, data processing, and content generation streams based on artificial intelligence.
https://imps.me/

… An interactive app with user progress, event logic, and state managed by the backend.

Experience in creating Telegram Mini Apps, API integration, and working with real-time data + LLM outputs.

I can create a complete TMA: data pipeline, AI predictions, paid access logic, and a fast mobile interface.

Ready for implementation within 2-3 weeks with a focused MVP approach.

Let's quickly get in touch with you.

Best regards,
Vyacheslav

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Hello! I am interested in the task of creating a platform for sports analytics with predictive AI in the format of a Telegram Mini App. My experience includes developing complex backend systems with ML/AI integrations and creating interactive web applications, perfectly suited for TMA. I am ready to offer architectural solutions that will ensure the quality and scalability of the project. Could you clarify the key metrics for predictive analysis that are planned to be implemented in the first phase? Let's discuss your budget and realistic deadlines for completion.

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Hello, Roman!

We consider your project PiarHunt a priority. We work in tandem: I (Nina) am responsible for management, technical specifications, and meeting deadlines, while the technical part "turnkey" (Frontend + Backend + AI) is handled by a Fullstack developer.

We have real experience in developing Telegram Mini Apps and a deep understanding of how to integrate external data with language models.

Here’s how we will implement your TMA in 3 weeks:

Architecture and Speed: We use the combination of FastAPI + React. This will ensure an instant response from the interface, which is critical for live sports events.
…
AI Analytics (Gemini/GPT): We do not just send requests. We use JSON mode to obtain structured forecasts and advanced Prompt Engineering so that the AI provides analytics based on real H2H and lineups, rather than "hallucinating."

Data Pipeline: We will set up aggregation of sports feeds with aggressive caching in Redis/PostgreSQL. The user will see statistics instantly, without waiting for a response from the data provider.

Paywall and Logic: We will implement a system of access levels and verification through postbacks. We will integrate trigger notifications about the start of matches or score changes.

UI/UX: We will design a responsive "technological" interface that will feel like a native app within Telegram.

We understand that a 3-week deadline is a challenge. Therefore, we use internal AI pipelines to speed up coding and testing, allowing us to deliver results twice as fast as traditional teams.

We are ready to discuss the layouts and start working.

The cost (70,000 UAH) is an estimate for the full implementation of the MVP in 3 weeks: from architecture and layout of the TMA to setting up AI prompts and caching feeds. We understand that your budget may be flexible, so we are ready to discuss the final amount after reviewing the layouts and specifications of the sports APIs.

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Hello! I am a Fullstack developer, stack: React + Python (FastAPI), PostgreSQL, integrations with Telegram Bot API, LLM (OpenAI/Claude — JSON mode, prompt engineering), REST API of sports providers. I have experience building AI agents with real-time data processing, caching, and notification systems. What I can cover from your tasks: Client-server architecture TMA from scratch, integration of sports feeds (fixtures, H2H, live stats), AI analytics through LLM with prompt optimization, paywall logic with postbacks, trigger notifications through Bot API, adaptive mobile layout. I am ready to discuss the details, show examples of work, and agree on payment. When is it convenient to call?

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Hello! Your project is a perfect match for my stack. I have experience building complex systems (like integraljobs.cz), so I understand how to work with large data sets and architecture under load.

Briefly about what I offer for your TMA:

Backend: I will use FastAPI for the fastest processing of sports feeds and asynchronous work with LLM.

AI logic: I will set up prompts in JSON mode for stable generation of predictions that do not "break" the frontend.

Data: I will implement proper caching (PostgreSQL/Redis) so that the statistics pipeline works without delays.
…
TMA & UX: I will create a technological interface in React that works perfectly within Telegram.

Deadline: We will meet in 3-4 weeks. I use AI tools to speed up development without losing quality.

I am ready to discuss the details of integrating sports APIs and the paywall logic in the chat. Stay in touch!

I have indicated the price for developing the basic MVP (the application framework and integration of the main sports feeds). We will discuss the full estimate after a detailed review of the layouts.

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Hello.

I have reviewed the description — the project is clear, the tasks are essentially classic, but with normal complexity due to real-time data and LLM.

I have experience in developing web applications and integrations with APIs, including with Telegram (bots/mini apps). I have worked with Node.js and Python (FastAPI), and on the front end — React. I understand how to build architecture for such tasks: collecting and caching sports data, processing, generating analytics through LLM, and presenting it to the user in a convenient format.

Based on what is described:
— I can build TMA from scratch (frontend + backend)
— set up integration with sports API + a proper caching layer
… — implement prediction generation through GPT/Gemini (with an adequate response structure)
— create an access system (paywall, levels, user binding)
— set up notifications through Telegram
— build a fast and adaptive interface for mobile devices

Regarding timelines: 2–3 weeks in total is realistic, but it depends on the volume of the MVP and the readiness of the layouts/technical specifications. It’s better to have a quick call or discuss the details to avoid discrepancies in expectations.

We can discuss:
— what specific functionality should be in the first version
— what data sources you plan to use
— how the prediction logic should look
— and after that, I will be able to provide exact timelines and costs

If convenient — let’s move on to the details.

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Good day! My colleague and I have been professionally engaged in development within the Telegram ecosystem and the implementation of neural network solutions for over 4 years, so we will help you launch a premium TMA service for sports analysis, combining backend stability with the flexibility of Gemini/GPT integrations. We will implement the architecture from scratch, paying special attention to the security of endpoints and the proper functioning of the subscription system. We will set up automatic collection of injury statistics and lineups in real time and ensure high application performance on mobile devices. Our 4 years of experience is confirmed by projects drkukharevich.rivne.ua, crave-agency.com.ua, jk-solution.com.ua, where we have already successfully worked with data arrays and Telegram Web Apps.

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Hello! My partner (designer + full-stack) and I have been specializing in the development of high-tech Telegram Mini Apps and AI services for over 4 years, so we will implement your sports analytics platform "turnkey" in a short period of 3 weeks, ensuring instant interface response and accuracy of predictive models. We will develop the architecture based on FastAPI and React, set up a processing pipeline for sports feeds (fixtures, H2H) with caching in Redis to minimize delays, and implement LLM analytics using JSON mode for generating structured forecasts. Our experience in development is 4 years; take a look at our work in terms of speed and complex integrations: hyperfi.tech, espressolab.com.ua, hudi.com.ua. We will integrate an access level system (paywall) with verification through postbacks, set up trigger notifications for live events via Bot API, and ensure perfect layout according to your designs. We work autonomously and have experience with sports odds, which will allow us to quickly set up the logic for processing data from providers. We will discuss the project cost after reviewing the designs, and we fully confirm the deadline of 21 days.

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Hello!

We have experience in developing Telegram Mini Apps + AI integrations — we work with the stack React + FastAPI + PostgreSQL + LLM API. We have developed TMA with paywall logic and trigger notifications.

For your project, we envision the following architecture:
- TMA frontend on React + Telegram WebApp SDK
- FastAPI backend with caching of sports feeds (Redis) — to avoid hitting the provider on every request
- LLM predictions: structured JSON mode (Gemini/GPT) with optimized prompts for fixtures + H2H + lineups
- Paywall: access levels through Telegram Bot API + postback verification
… - Retention: event-driven notifications through the bot (live events, deadlines)

We will create an MVP in 14 days, including a responsive UI based on the designs.

Clarification: which sports data provider do you plan to use (API-Football, SportRadar, other)? This affects the caching structure.

Team FlipFactory

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Good day! 👋

I have experience in developing Telegram Mini Apps + AI integrations, including building client-server architecture, working with APIs, and generating data through LLM. I understand well how to quickly assemble an MVP without losing quality — especially under tight deadlines.

Experience in similar projects

I have worked on:

Telegram bots and Mini Apps (WebApp)
…
AI integrations (GPT, Gemini, structured output)

parsing and processing data (including real-time)

access systems (paywall, roles, restrictions)

push/trigger notifications via Telegram

How I will implement your project

Architecture
backend in Python (FastAPI) — fast, stable, ideal for API and AI
frontend — React (for Telegram WebApp)
PostgreSQL + cache (Redis if needed)

Data integration
connecting sports API
data normalization (fixtures, stats, H2H)
caching for fast operation

AI analytics
generating forecasts through LLM
optimizing prompts
controlling response structure (JSON)

Access logic
paywall / access levels
user verification via Telegram

Retention
trigger messages via the bot
reacting to events (matches, updates, activity)

UI/UX
adaptive interface (mobile-first)
fast and “live” UI

How we fit into 2–3 weeks

I create an MVP focusing on key functionality

clear breakdown into stages

parallel work (backend + frontend)

daily progress without “hang-ups”

Work format

clear communication

quick start

staged delivery

possible support after launch

I am ready to discuss the details and immediately get involved in the development.

Volodymyr.

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Вітаю! 🚀

Маю досвід з Telegram (боти + web apps), real-time системами, API інтеграціями та AI-аналітикою

Використовуваний стек :

Backend: Python (FastAPI), PostgreSQL, Redis

Integrations: Sports Data API, OpenAI / Gemini (LLM), Telegram Bot API, webhook/postback логіка
…
Frontend: React + Telegram WebApp SDK (TMA), mobile-first UI

Infrastructure: Docker, Nginx, VPS/cloud, caching layer, background jobs



Готовий підключитися до реалізаціїї Вашого проєкту.

З повагою,
Андрій 🚀

Hello.
I have experience in developing Telegram Mini Apps, backend logic, API integrations, and working with AI. We have done projects involving generation through LLM, data caching, paywall logic, and notifications via Telegram.
Write to me in private messages, we will discuss the details, and I will immediately tell you about the timelines and costs.

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Hello.

I am developing bots for Telegram using NodeJS. I am ready to take on the project. Write to me, and we will discuss.

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Hello. I have extensive experience in developing Telegram Mini Apps on Next.js/Nest.js. I am ready to collaborate.

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Good day

My name is Dmytro, from King Kong Lab. We can implement your Telegram Mini App project for sports analytics with AI in a short timeframe.
We have experience in developing Telegram solutions (bots, Mini Apps), working with LLM API (GPT/Gemini), building backends on Node.js / FastAPI, and integrating with external APIs.
We can cover the entire scope of tasks:
designing the architecture of the TMA (frontend + backend)
developing the interface (React / Vue) optimized for mobile
integrating sports APIs (fixtures, stats, lineups, live data) with caching
connecting LLM for generating predictions + optimizing prompts
… implementing a paywall and access logic
building a system of trigger messages through the Telegram Bot API
designing the database (PostgreSQL / MongoDB) for scalability
We understand the specifics of working with real-time data and generating structured responses (JSON mode), as well as the importance of speed and stability.
We are ready to assemble the MVP within a deadline of 2–3 weeks with further scaling.
We can show relevant cases and discuss implementation details.

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Hello. I am ready to implement a Telegram Mini App for sports analytics using Python/FastAPI in conjunction with modern frontend. I have experience in developing Telegram bots, API integrations, working with PostgreSQL, external sports feeds, caching, as well as building AI logic on top of structured data. I can handle the backend part, integrations with Sports Data providers, LLM API, Telegram Bot API, paywall logic, trigger notifications, and prepare a stable architecture for further scaling.

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