Budget: 2000 USD Deadline: 7 days
Daniel, you have a challenging task: real-time video, AI/CV, event-driven backend, and fault tolerance in one platform. I have been leading development for 7 years and as a team lead, I assemble teams for such projects — I have already worked on high-load web services and integrations with analytics/streaming. I suggest starting with architecture and MVP. I can separately assess the cost, timelines, and hours for the MVP and full scope after a brief call.
Budget: 36 USD Deadline: 1 day
Hello. I have extensive experience with Node.js. I am ready for collaboration. Feel free to contact me.
Budget: 2000 USD Deadline: 7 days
Hello, I have been working on the development of an AI system for real-time video stream analysis for a sports platform that processes over 500 hours of video daily and recognizes more than 15 types of events.
I am curious about which specific AI model you plan to use for detection and event recognition, and whether you are considering custom training for the specific scenarios of your platform?
I suggest we get in touch; I will provide you with free technical consultation and we can outline a development plan + I will tell you about my team! ⚡
Budget: 300 USD Deadline: 7 days
Hello. From the description, I see not a "one-time correction," but a task where normal logic is important and the solution should not fall apart in operation.
I would start with the main point: We are considering specialists with experience in complex backend and AI systems.
Regarding implementation: I would structure the integrations and data exchanges to be idempotent, verifiable, and without "silent" errors; I would build the real-time part through a predictable event flow, rather than through fragile hacks on the front end.
Relevant experience: I have built end-to-end automation and API integration pipelines: data collection, filtering, enrichment, CRM connections, and reliable business logic.
Regarding timelines: the target is 7 days. As for the budget: 300 — after a brief clarification of the technical specifications, I will provide a precise figure, but that is the range.
If you want, I can provide a short implementation plan in the first message and immediately show where time/money can be saved without compromising quality.
Budget: 10000 USD Deadline: 150 days
I have reviewed the description of your project and am ready to provide you with a detailed work plan and a professional image of the project tailored to your needs. However, I request that communication occurs only through the platform to protect the exclusivity of my work and ensure that no one else benefits from my efforts. By collaborating with me, you will receive a structured, results-oriented plan based on modern practices that guarantees a tangible effect. I am confident that this collaboration will bring you the clarity and efficiency you are looking for.
Budget: 200 USD Deadline: 1 day
Hello!
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We can address these tasks in the format of external contracting.
Are you considering involving an external contractor or team for these tasks? What tasks and technologies need to be prioritized?
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Take a look – after that, we can discuss the details and agree on the next steps.
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Budget: 25000 USD Deadline: 120 days
A phased approach can be proposed: first a discovery/architecture sprint, then an MVP, and finally full implementation. MVP: 8-12 weeks, estimated 600-900 hours, $25k-$45k depending on the composition of CV modules and requirements for video/load. Full Project: 4-6 months, 1400-2200 hours, estimated $70k-$140k+. The first result is best captured as a technical architecture, a prototype video pipeline + event processing, and a minimal API for game logic.
Budget: 6000 USD Deadline: 50 days
Greetings! I have experience in building event-driven systems and integrating AI into production. I implement a fault-tolerant backend architecture (Node.js/Go + Python for CV) with real-time video streaming.
My plan:
MVP (video stream + AI detector + basic API): ~120-150 hours, 3-4 weeks.
Full (Betting API, Provably Fair, scaling, logging): ~300+ hours, 2-3 months.
I am ready to design a scalable infrastructure for high loads. We can discuss the cost range and detailed composition of the MVP in the chat!
Budget: 4000 USD Deadline: 120 days
Здравствуйте!
Проект выглядит очень интересным, особенно за счёт сочетания real-time backend, AI/CV и event-driven архитектуры. У меня есть опыт разработки backend-систем на Python, работы с AI-интеграциями, real-time сервисами, Docker/Kubernetes, WebSocket/event pipelines, а также production API и automation systems.
По стеку вижу оптимальным следующий подход:
Backend: Python + FastAPI / async architecture
Real-time layer: WebSockets / Redis Streams / Kafka (в зависимости от нагрузки)
AI/CV: YOLO / OpenCV / PyTorch / ONNX Runtime
Video pipeline: FFmpeg + async workers
Infrastructure: Docker + Kubernetes
Storage: PostgreSQL + Redis + S3-compatible storage
Event processing: event-driven architecture через queue/pub-sub
Monitoring: Prometheus + Grafana + centralized logging
Для betting/game logic также можно отдельно вынести:
provably fair service;
game-event processor;
analytics pipeline;
replay/media service.
Что бы я предложил для MVP:
MVP может включать:
базовую backend-архитектуру;
ingest видеопотока;
AI detection/counting pipeline;
real-time event API;
WebSocket updates;
базовую систему логирования;
авторизацию;
минимальную admin/debug панель;
подготовленную scalable architecture.
Оценка MVP:
стоимость: зависит от финального scope и AI-части.
Full Project:
полноценная distributed architecture;
масштабирование;
media replay/storage;
advanced analytics;
fault tolerance;
provably fair;
production monitoring;
optimization under load;
edge-case handling;
CI/CD + deployment infrastructure.
Оценка Full Project:
Для более точной оценки желательно понять:
какие именно CV задачи нужны (tracking, counting, action recognition и т.д.);
ожидаемое количество concurrent streams/users;
есть ли уже UI/frontend;
какой expected latency;
cloud/on-premise deployment;
какие AI модели уже выбраны;
нужен ли GPU inference cluster.
Также интересно:
это live-stream processing или upload-based pipeline;
какие именно betting/game mechanics планируются;
нужен ли anti-fraud / anomaly detection слой.
Готов обсудить архитектуру, предложить оптимальный pipeline и помочь спроектировать систему так, чтобы её можно было масштабировать без полного рефакторинга в будущем.
Budget: 2000 USD Deadline: 14 days
Good day.
According to the description, this is a backend + CV pipeline on video stream, plus game logic with provably fair and event-driven wrapping. I want to clarify a couple of points first to make the assessment realistic.
1) Where is the video stream from: one source per instance or N parallel streams? What is inside detection — a ready-made model (YOLO / Roboflow) or your own? What is the acceptable delay for the event (sub-100ms / 200-500ms / 1s)?
2) Provably fair — do you need a standard scheme with server-seed/client-seed/nonce or your own? This is critical for time estimation.
3) How many concurrent users are planned for the MVP and the full version? This affects whether Redis pub/sub + horizontal scaling is needed right away or if one node will suffice for the MVP.
For the stack, I can suggest: Python (FastAPI / async), PostgreSQL, Redis for pub/sub and rate-limit, ffmpeg for cutting/repacking, OpenCV for preprocessing frames, a separate worker for detection (optionally gRPC stream if there will be many models). Storage — S3-compatible (MinIO or AWS).
Please respond to the points above — I will send a separate estimate for the MVP (what's included, hours) and full (hours).
Matvey
Budget: 3000 USD Deadline: 30 days
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Budget: 1000 USD Deadline: 30 days
Hello!
I am interested in your project — real-time video analytics with AI/CV modules and gaming logic. This is exactly the intersection of backend architecture and AI integration that I work in.
A brief about myself: Node.js/TypeScript developer with 2+ years of experience in building API-first and event-driven systems. I have designed ETL pipelines and worked with BullMQ/Redis for asynchronous queues. My last major project was a production multi-model AI Agent integrated into an Electron CRM: 5 LLM providers (OpenAI GPT-4o, Gemini 2.5 Flash, DeepSeek, Grok/xAI, Ollama), agent orchestration, Tool Use / Function Calling. I use Python for AI agents and scripting.
What I can cover for your project:
— backend architecture on NestJS / Node.js + Python for the CV part
— integration of AI/CV modules (YOLO / OpenCV) through an API layer
— event-driven event processing: Redis pub/sub + BullMQ
— REST/WebSocket API for gaming logic
— logging system and handling edge cases
— scalable infrastructure (Docker, PM2, CI/CD basics)
Where I will be honest: I do not have direct experience with video streaming pipelines (RTSP, FFmpeg, WebRTC) and betting platforms — these are new areas that I am ready to explore within the project. I am conceptually familiar with CV frameworks (YOLO and similar) through my work with AI agents.
MVP (core backend + basic AI integration + API):
- What is included: backend architecture, connection of 1–2 CV modules (detection/counting), REST API for gaming logic, basic logging, Docker environment
- Timeline: ~6–8 weeks
- Hours: ~200–280 hours
- Cost: $3,000 – $4,200 (rate ~$15/hour)
Full Project:
- What is included: complete real-time video analytics, scalable infrastructure, provably fair mechanics, media storage/playback, fault tolerance
- Timeline: ~4–6 months
- Hours: ~600–900 hours
- Cost: $9,000 – $13,500
I am ready to discuss the details, clarify the scope, and adjust the estimate to your specific requirements.
Ruslan Zotsenko
Budget: 25 USD Deadline: 1 day
Good day, I am writing on behalf of the company Devoxen. We specialize in complex backend/AI systems, real-time analytics, and high-load architecture. We have extensive experience with Computer Vision, video streaming pipelines, event-driven systems, AI integration in production, and scalable cloud infrastructure.
On the technical side, we can implement backend architecture, real-time processing of video streams, detection/counting/event recognition, API for game logic, event processing, media storage/playback, and fault-tolerant infrastructure for simultaneous user load. We also work with Python, Go, Node.js, CV frameworks, streaming stack, and AI pipelines for production environments.
Initially, an MVP can be implemented in the form of a basic real-time pipeline with AI modules, API, logging, and minimal game logic. The full project will require a complete scalable infrastructure, advanced analytics, fault tolerance, and enhanced AI processing. We can provide an accurate estimate of costs, timelines, and hours after discussing the architecture, expected load, and functional requirements.
We can proceed without unnecessary questions and time delays. We also offer a guarantee and support if desired. We can start working on your project immediately after discussing the technical specifications.
I suggest moving to private messages for a more detailed dialogue.
Budget: 2000 USD Deadline: 41 days
more than 7 years of backend development experience and over 3 years in projects with AI/CV and real-time systems. I specialize in creating high-load solutions for the gaming and betting industry, including real-time video processing, event detection, and provably fair mechanics. I would be happy to collaborate.
Budget: 2000 USD Deadline: 21 days
Good day.
I have read the technical specifications. The platform involves real-time video processing, CV analytics, provably fair mechanics, and a scalable infrastructure for simultaneous users. This is a serious project, and we are considering it seriously.
According to our profile.
Backend and AI integrations in production are our daily stack. Relevant cases from our portfolio:
BrandSync AI with GPT-4 and Whisper pipeline through BullMQ and Redis for real-time content processing.
Winbix, our multi-tenant AI agent platform in production with paying clients, experience in scalable architecture.
MemHash, a Telegram Mini App with on-chain economy on TON, supports 8k simultaneous users with real-time updates via WebSocket. This is direct experience with real-time systems under load.
BotFusion SaaS chatbots with integrations into 19 platforms, experience in building scalable services with event-driven architecture.
Regarding the Computer Vision part. We understand the YOLO, OpenCV, MediaPipe stack, and have worked on detection and classification tasks. For the specifics of live video with detection, counting, and event recognition in production, we are ready to strengthen the team with a specialized CV engineer for this project to avoid any weak links.
Technical approach:
Python with FastAPI for AI pipeline and backend logic.
Node.js for real-time game logic if low latency is needed on the event-driven layer.
Redis for event bus and pub/sub between services.
PostgreSQL for the main database, ClickHouse if there will be analytical queries on large volumes of logs.
S3-compatible storage for media, FFmpeg for processing video streams.
Cloud infrastructure via AWS or GCP with auto-scaling for peak loads, Kubernetes for orchestration.
Provably fair will be implemented through the standard commit-reveal scheme with server seed, client seed, and HMAC. We can tie it to on-chain verification if public result verification is needed.
Before the main contract, we are ready to assemble a proof-of-concept for the key module: basic video stream plus one CV model plus provably fair generator. This will demonstrate the actual level of execution before signing.
Portfolio: quentar.space/en/startups
I await your personal message to discuss the details.
Budget: 25 USD Deadline: 1 day
Good day, I have experience working with Python as well as Telegram bots, I can complete everything quickly and efficiently, write to discuss the details.
Budget: 4500 USD Deadline: 21 days
Hello.
Straight to the point - I have working code for each block of your task.
Regarding CV: I developed a table_detector for Dodo Pizza - YOLOv8 on video from surveillance cameras, state machine, presence detection through IoU, false positive protection, tests, Docker. I am not familiar with YOLO - here is the repo, take a look: github.com/vitalivo/table_detector
For real-time: FleetTrack - Kafka in WebSocket dashboards, live updates, event-driven architecture.
I implemented provably fair through commit-reveal with HMAC and public seed verification - I build this from day one, not adding it later.
The architecture I envision: video comes to workers with YOLO, they publish structured events to Kafka, then betting engine and WebSocket updates for users. CV workers are isolated - the model can be changed without affecting the rest.
MVP (1 stream, basic detection, event API, betting contour): 3 weeks, 4500 dollars.
Full (multi-stream, complete betting engine, provably fair, fault tolerance, auto-scaling): 3 months, 15000 dollars.
If you're interested - write to me, we can discuss the details.
github.com/vitalivo
Budget: 200 USD Deadline: 3 days
Hello! Having reviewed your project, I am ready to start working on it. Let's discuss the details for the best result.
Budget: 200 USD Deadline: 1 day
Good afternoon. I am ready to complete this project and have extensive experience in developing various applications.
Budget: 5000 USD Deadline: 42 days
Hey Daniel,
Reading between the lines: this is a live video stream (likely a physical scene or game) where CV detects events in real-time, those events feed into
betting logic, and everything needs to be provably fair and auditable. The architecture is event-driven by nature, not by choice.
Where I'm strong and where I'd be honest:
Backend architecture, API design, async Python (FastAPI), event-driven systems, Redis/Kafka for pub-sub, PostgreSQL, Docker, cloud infra — this is my
daily work. Provably fair mechanics (commit-reveal with HMAC, server seed + client seed, public verification) I understand well from crypto-adjacent
projects.
For the CV/video pipeline specifically: I've integrated AI models into production systems and worked with inference APIs, but I haven't built a full
GStreamer ingest pipeline from scratch. I'd use FFmpeg for decode, ONNX Runtime or TensorRT for inference, and structure it as isolated workers that
publish structured events to the main platform. If you need someone who's shipped 500hrs/day of video processing before, that's not me. If you need
someone who can architect the system correctly and get a working CV pipeline running with standard detection models (YOLO, etc.), I can do that.
Rough breakdown:
MVP (1 stream, basic detection/counting, event bus, betting API with provably fair, basic admin):
~180 hours, 6 weeks, 5,000 USD
Full (multi-stream, advanced event recognition, complete betting engine, fault tolerance, auto-scaling, observability, replay system):
~550 hours, 4 months, 18,000 USD
Before I can tighten these numbers: what's the video source (IP camera, RTMP stream, browser-based)? What events are we detecting (objects, people,
specific actions)? And what's the target latency from event detection to user-facing update?
Budget: 2000 USD Deadline: 14 days
This platform needs a backend that maintains stability under load because real-time video analysis, betting-style logic, and provably fair mechanics do not forgive weak engineering. I can design and develop the backend as an event-driven system, where the results of video analysis feed into a clean rules engine, every decision is logged, and scaling remains predictable during sharp increases in the number of concurrent users.
For video, I typically build the architecture in a chain: ingest → decode → inference → event aggregation → storage and playback, with clear boundaries between components so that CV models can be replaced or stream providers changed without rewriting the entire stack. The AI layer can operate as separate workers that publish structured events, such as the number of detections and recognized moments, while the main platform uses these events to manage game state, odds, and updates for users. I also incorporate provably fair into the workflow from day one — through commit-reveal seeds and a transparent audit trail, so it doesn’t have to be added on top of an existing system later.
One memorable idea for a feature is a Replay Proof Card for each completed round. It will include proof of fairness seed, a short timeline of events, and a link to the replay segment, so support and users can quickly understand what happened in just a few seconds, while raw logs would be preserved for deeper auditing.
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Budget and timeline estimate for MVP: $2,000–$2,500, about 2–3 weeks, approximately 80–120 hours.
Budget and timeline estimate for the full project: $4,000–$6,000, about 1–2 months, approximately 200–400 hours.
Thank you!
Budget: 1000 USD Deadline: 20 days
Stack for the task: Python/FastAPI + Go on hot paths, GStreamer/FFmpeg for ingest, ONNX/TensorRT for inference, Kafka for event bus, Postgres + ClickHouse for analytics and auditing. I select CV models based on specifics — detection, counting, and event recognition require different optimizations for latency/accuracy.
Provably fair — commit-reveal with HMAC and public seed audit, done.
MVP (1 stream, basic detection, event model, minimal betting API, single-region): 6 weeks, ~220 hours, $8,500.
Full Project (multi-stream, advanced CV analytics, complete betting engine, provably fair, observability, auto-scaling, fault tolerance): 4.5 months, ~680 hours, $24,000.
To be transparent: I am concurrently managing another project, I allocate 25–30 hours per week for yours — the timelines above are already calculated with this in mind, I won’t delay. If full-time focus is needed, let me know — I will recalculate.
Before the final assessment, I would like 15 minutes for a call: what exactly we are detecting, target FPS, latency budget, type of game. This could shift the timelines in either direction.
