Budget: 1000 USD Deadline: 5 days
Good day, ready to approach this task quickly and professionally. There are reviews on another platform, and more than 17 completed works, write to discuss everything in more detail)
Project: Implementation of an AI-based system for automating requests, logistics, and analytics in a pharmacy system of a medical center
Goal: Creating an intelligent system for forecasting needs, inventory management, logistics, monitoring, and integration with Medical Information Systems (MIS)
Goal: Assessing needs for each department based on historical and current data.
Functions:
Collecting data on departments: surgery, cardiology, etc.
Analyzing diagnoses, treatment pathways, average hospitalization duration.
Classifying consumption types by profiles.
Forming recommendations for necessary medications.
AI Module:
Clustering departments based on consumption structure (ML).
Using NLP to extract data from discharge summaries and treatment protocols.
Technical Implementation:
Connecting to the MIS database.
Building an ETL process (Extract → Transform → Load) with regular updates.
Goal: Reducing shortages and surpluses of medicines and medical supplies.
Functions:
Forecasting based on:
Historical data (last 3–5 years).
Epidemiological trends.
Seasonality and receipt statistics.
AI Module:
Time series model (Prophet / ARIMA).
Adjustable accuracy (MAE ≤ 10%).
Output Data:
Monthly needs table.
Signals for forming requests.
Goal: Accounting for standard and individual material sets.
Functions:
Creating templates for sets based on operation types.
Integration with operational plans.
Ability to adapt to individual patient features.
Integration:
API from MIS/operational subsystem.
Auto-request to warehouse.
Goal: Prevent shortages of key medications.
Functions:
Setting minimum stock levels.
Automatic notifications when threshold is reached.
Manual adjustment options.
Technical Implementation:
REST API for synchronization with internal database.
Ability to connect mobile notifications.
Goal: Improve placement and utilization efficiency.
Functions:
Automatic distribution of medicines and supplies within the warehouse.
Considering dimensions, expiration dates, and usage frequency.
AI Module:
Combination of greedy algorithms + linear programming.
Goal: Minimize losses from expiration.
Functions:
Considering expiration dates.
Automatic recommendations for relocating drugs between departments.
"Critical stock" flag.
Goal: Ensure timely delivery of medicines and supplies.
Functions:
Creating delivery schedules.
Calculating delivery cost and time.
Prioritizing requests (urgent/planned).
Integration:
External logistics APIs (if available).
Internal clinic logistics.
Goal: Transparent control over the movement of medicines and supplies.
Functions:
Accounting for movement between warehouses/departments.
Movement log with timestamps.
QR/RFID scanning during movement (optional).
Goal: Prompt informing of responsible persons.
Functions:
Notifications for delivery delays.
Signals about shortages of specific medicines.
Channels: Telegram bot, email, SMS.
Goal: Creating a database for decision-making.
Functions:
Collecting data on:
Remaining stock.
Expiration dates.
Number of operations.
Discharges.
Goal: Supporting management decisions.
Functions:
Dashboards (stock, consumption, anomalies).
Forecast for 1–3 months.
Export to Excel, CSV, PDF.
Goal: Increase expense control.
Functions:
Detecting abnormal consumption.
Algorithms for over-expenditure control.
Reports on deviations from norms (Z-score, IQR, etc.).
Functions:
Obtaining data on patients, diagnoses, prescriptions, discharges.
Matching actual consumption with prescriptions.
Interface:
API on HL7/FHIR.
Logging all transactions.
Goal: Improving request accuracy.
Functions:
Comparing "prescribed" vs "discharged" vs "used".
AI analysis at department and individual doctor levels.
Budget: 1000 USD Deadline: 5 days
Good day, ready to approach this task quickly and professionally. There are reviews on another platform, and more than 17 completed works, write to discuss everything in more detail)
Budget: 1000 USD Deadline: 2 days
Hello, I worked on an AI system for medical data processing ✅, where I analyzed patients and predicted needs with up to 10% accuracy. Are you considering using NLP for protocol analysis?
I suggest we get in touch, I will provide free technical consultation and we will develop a development plan + I will tell you about my team!
Budget: 12000 USD Deadline: 30 days
Good afternoon, I suggest you consider a comprehensive ERP solution Odoo that already has all the necessary functions for you:
- Application formation
- Logistics
- Inventory management
- Analytics and monitoring
- Feedback from clients and order points
The system has a built-in API that allows integration with any other system and transmits certain data based on triggers.
The capabilities of using artificial intelligence are already built into the system and will be modified based on your processes:
1. Warehouse inventory
2. Applications based on artificial intelligence
3. Stock levels based on forecasting
A demonstration of the working system can be shown. Since the systems of artificial intelligence and agents were formed for similar projects, the demonstration will only cover ERP and all business processes based on your requirements; artificial intelligence will be prepared separately.
Budget: 25 USD Deadline: 1 day
Hello!
A very interesting and large-scale project — glad to see a systematic approach to automating pharmacy logistics processes using AI.
📌 Why I am suitable for you:
I have experience developing complex ETL processes, integrating with HIS using HL7/FHIR.
I have implemented ML modules for forecasting (Prophet, ARIMA, XGBoost) and consumption clustering based on medical profiles.
I have worked on logistics automation, stock tracking, dashboard creation, and notification systems (Telegram, SMS, email).
I know how to design scalable architecture considering medical specifics, medication rotation, urgency of requests, and seasonality.
I am ready to discuss details, suggest architectural solutions, and point out risks.
Budget: 25 USD Deadline: 1 day
Hello.
I have read your project with interest. I am confident that I can do effective and high-quality work that meets your requirements and expectations. Over 8 years of experience.
Budget: 3200 USD Deadline: 77 days
Hello!
My name is Mikhail Petrikey, I am a developer of intelligent systems with experience in automation, predictive analytics, NLP, and integration with medical systems. Your project is a systematic and well-thought-out initiative, and I am ready to join its implementation as a technical developer and solution architect.
In this project, I can offer:
- Development of an ETL process connected to the HIS and regular data updates;
- Creation of demand forecasting modules based on time series (Prophet, ARIMA);
- NLP processing of discharge summaries and treatment routes (using spaCy, transformers);
- Development of AI interfaces for automating applications, transaction accounting, and logistics;
- Integration with HIS via FHIR/HL7 API, transaction logging;
- Building dashboards and analytics (Plotly, Power BI, Streamlit, Dash);
- Implementation of optimization algorithms (greedy, LP) for warehouses and logistics;
- Setting up alert systems: e-mail, Telegram, SMS via API.
I have experience in medical-technical projects, participated in international AI competitions, worked with neural network models, databases, and automation of clinical processes.
I am ready to discuss architecture, prepare technical specifications, and proceed with phased implementation with MVP demonstrations at each key block.
Budget: 3500 USD Deadline: 50 days
Hello!
This is a powerful and promising project, and I will be happy to help you implement a full automation system for the pharmacy based on artificial intelligence. I can create an intelligent platform that will analyze department profiles and patient data to forecast medication needs, optimize inventory levels, automate warehouse distribution, and detect deviations in real time. Each module will be closely integrated with your Management Information System (MIS) via HL7 or FHIR, and the system will support ETL pipelines, predictive models, and intelligent notification flows through Telegram, email, or SMS.
In the field of logistics and warehouse management, I will apply intelligent algorithms to optimize placement, track expiration dates, and ensure cost-effective routing. The monitoring dashboard will include clear exportable reports and forecasts based on real-time data. You will be able to easily configure all parameters through a user-friendly admin panel, and the system will evolve along with you.
Thank you!
Budget: 1500 USD Deadline: 7 days
Hello
I am a developer in the field of AI/ML. I can complete your project. Write to me, let's discuss.
Budget: 1496 USD Deadline: 20 days
I want to take on the implementation of a project for an AI system for automating applications, logistics, and analytics in a medical center's pharmacy network. I have experience in AI, ML, integration with MIS, and analytics. I am ready to be responsible for the entire cycle — from architecture to implementation. Please assign the project to me.
Budget: 1800 USD Deadline: 30 days
Good day.
I have 21 years of experience in Python.
I have worked on commercial medical projects as a Team Lead for 8 years.
I know how everything should be built.
I can do everything properly, without rework, without violating medical laws.
Write in private - we will discuss the details.
Budget: 3000 USD Deadline: 100 days
You have a lot there but "they eat the elephant in parts" (Budget is conditional and deadlines)
In particular, there is a ready-made inventory management solution (with a series of expiration dates)
Inventory - Critical balances, integration with trading equipment (barcode scanners, data collection terminals, scales, POS printers, and label printers)
The overall view looks something like this
https://youtu.be/vMrE4KfbjzA
Their own report generator
Budget: 12345 USD Deadline: 15 days
Hello!
Your AI integration project looks very interesting. We have extensive experience in machine learning and NLP.
We offer:
- Machine Learning solutions
- NLP text processing
- Computer vision
- Integration with existing systems
- Cloud solutions for AI
We can conduct a workshop with our AI specialists. I offer a free consultation. When is convenient for you?
Currently, we are working on a mobile application using AI. We are not disclosing project details at the moment due to NDA, but we will gladly share our approaches and results.
I would be happy to discuss the details of your project, timelines, budget, and next steps in private messages.
Budget: 1000 USD Deadline: 20 days
Hello!
Recently, I have been happily working with AI and am ready to implement an intelligent system for you that automates applications, manages supplies and logistics of medical records and medical information with maximum accuracy and safety.
My approach will be based on:
📊 Data-driven analytics — demand forecasting based on real diagnoses, discharge summaries, surgeries, and history,
🧠 AI modules — time series, NLP, anomaly detection, department clustering,
🔗 Integration with HIS — HL7/FHIR, REST API, logging of all transactions,
🧾 Transparency — visualization of stock levels, reporting, signals for deadlines and errors,
⚙️ Flexible architecture — you can start with one department and scale without completely stopping processes.
Такие (очень похожие) системы существуют готовые, можно выбрать готовую и доработать ее.
Looking for an experienced specialist in Chatterfly.ai to set up a full-fledged automated sales funnel in Telegram in the trading niche. What needs to be done: Set up Chatterfly.ai from scratch. Connect the Telegram bot. Create an AI assistant that will automatically communicate with users, answer questions, and guide them to registration. Set up the sales funnel with user segmentation by stages. Integrate the system with the broker Pocket Option. Set up the transfer and verification of user ID, postbacks, and registration/deposit statuses (if API or other integration methods are available). Configure automatic messages, triggers, tags, and communication scenarios. If necessary, assist with the integration of CRM and other services. Important: Real experience with Chatterfly.ai is mandatory. Experience with integration with Telegram and Pocket Option is preferred. It is necessary not just to set up the service, but to help build a working system that will automatically lead clients and increase conversion. Work result: A fully configured and tested funnel, where the user goes from the first message to registration with the broker, while the AI automatically supports them at all stages. A brief instruction for further use of the system is also required.
We are looking for a specialist to create one realistic AI model / AI character and prepare a content package for social media. The task is to develop a visually high-quality and consistent image that can be used in photos and short video formats. What needs to be done: create one AI model with a recognizable appearance and a unified style; prepare a small package of photos and short videos; adapt materials for publication on social media; ensure realism and stability of the image in different scenes. In your application, please indicate: whether you have experience in creating AI models / AI characters; whether you can show examples of similar work; estimated cost and completion time; A detailed technical task will be discussed with suitable candidates in private messages.
Good day! Two tasks need to be completed: 1. Develop a product parser from an external website (10–40 thousand items, marketplace) with structured data saved in MySQL for subsequent output in WordPress. 2. Install and configure n8n on VPS, as well as organize AI content processing: prompt setup, text rewriting, image processing, SEO optimization, and text checking for AI detection. You can estimate the cost of completing both the entire project and each task separately. .
Task: one dashboard with all business metrics — advertising, funnel, payments, manager performance, revenue planning. Data is pulled automatically via API. Scope: only the YCL direction (employment in Europe). Kommo has other directions — only YCL funnel deals will be included in the repository (filter by funnel/tag to be agreed upon).1. Data Sources (Integrations) Kommo CRM — leads, deals, funnel stages, responsible persons, sources, dates of transitions between stages (must keep history), reasons for refusals, custom deal fields (see point 2). Stripe — payments, amounts, statuses (success/failure/refund), linked to deals. Meta Ads — expenses, impressions, clicks, CPL, leads by campaigns (currently operational). Google Ads, Reddit Ads, LinkedIn Ads — planned; architecture — extensible connectors without core rework. SEO/organic— Google Search Console + GA4. Cross-link: traffic source → lead in Kommo → payment in Stripe (UTM, deal ID in Stripe metadata — propose the mechanism). 2. Mandatory Cuts (Deal Fields in Kommo) Each metric must be filtered/grouped by: Client Citizenship (Kenya, Nigeria, India, etc.). Residence Status: lives in their country / expat (already in Europe). These are two different segments with different cycles, conversion rates, and checks. Country of Placement / Service: Poland, Serbia, Slovakia, Germany (ZAV). Manager, team, traffic channel, period. If any fields are missing in Kommo — the executor indicates which fields need to be added, the client adds them.3. Funnel and Leading Indicators Data by funnel, for each stage — summary and leading metrics: Traffic → lead: leads, CPL by channels + day-to-day expense/click dynamics. Lead → qualification: conversion + first response speed, touches/calls to the manager per day, unanswered leads. Qualification → contract/invoice: conversion + sent offers, stalled deals (days in stage above norm). Invoice → payment: payments, average check + unpaid invoices, failed payments. Summary: revenue, ROMI by channels, run rate to monthly plan. 4. Deal Cycle Average and median lead → payment cycle (business benchmark ~4 weeks), cycle trend over time. Breakdown of cycle by stages (how many days a deal sits at each stage) — to see which stage is dragging. List of deals that have stalled at a stage longer than normal. Cycle breakdown by segments: citizenship, residence status, country of placement, manager. 5. Early Warning of Decline (Key Block) Since the cycle is ~4 weeks, today's leads = payments in a month. The system must: Compare leads/qualifications of the current week with the moving average (4 weeks) and issue an alert if there is a downward deviation: “leads -X%, with a 4-week cycle expect a payment decline in the week [date].” Build payment forecast for 4 weeks ahead from the current pipeline: deals at each stage × historical conversion of the stage × remaining cycle. Highlight in red weeks where the forecast is below plan — with time to react. 6. Additional Payments and Sales Planning In the Kommo deal card, the date and amount of the planned additional payment are stored. The system must: Collect a calendar of upcoming additional payments: total expected, by weeks/months. Highlight overdue additional payments (date passed, no payments in Stripe) — a separate list for follow-up. Calculate the monthly plan as: plan − already paid − scheduled additional payments = how many new sales are needed (in money and in deal units at average check). Weekly schedule: additional payments + forecast of new payments against the weekly plan. 7. Manager Performance Daily snapshot for each manager: touches/calls, conversations, sent offers, payments — for each day separately, with a chart over the period. Progress on personal plan compared to monthly pace (ahead / on pace / behind). Benchmarking with colleagues. 8. Visualization and Roles “Traffic lights” (green/yellow/red) for key metrics relative to norms/plans; progress scales; trend graphs; mobile adaptive. Roles: CEO — everything; COO — entire funnel and managers; team lead — their team; manager — their metrics and position relative to colleagues. 9. Reports and AI Automated reports on schedule (daily summary, weekly report) in the dashboard and/or messenger. Free-form queries (“how has CPL from Meta changed over 2 weeks?”) — LLM over the repository. Alerts in the red zone and according to the rules from points 5–6. 10. Technical Expectations and Staging Repository (PostgreSQL/BigQuery or equivalent) + ETL: Kommo webhooks + periodic synchronization (15–60 min). Frontend: custom or BI tool — propose with justification; requirements for roles, traffic lights, forecasts, and AI queries must be implementable. Stages: (1) audit and metrics map → (2) MVP: Kommo + Stripe + Meta, funnel, traffic lights, roles → (3) deal cycle, early warning, additional payments and plan → (4) SEO, AI reports, alerts → (5) new advertising channels. Payment is staged, with a demo for each stage. In the response, indicate: similar projects (end-to-end analytics), stack with justification, timeline and cost estimates by stages, monthly ownership cost (hosting, tokens, licenses).
Task: deploy an LLM service that knows all the company's documentation and answers questions from the sales department managers. Current situation: the client has independently assembled a prototype (a separate project with uploaded company information, hosted on a server), but the information from the database is not transmitted to the model — likely, there is an issue with the API. We will provide the code and access. The first step is an audit: fix the existing setup or justifiably rebuild from scratch. Required functionality: Upload all company documentation: description of each service, regulations, FAQ, pricing (all materials will be provided). Answers strictly based on the uploaded documents (RAG). The model does not invent facts; if the answer is not in the database — it honestly informs about it. Access for managers via a link (web interface), with authorization. Scenarios: the manager asks any question about the company's work; inserts the client's question "as is" and receives a ready answer for sending; finds the necessary regulation/report by request. Knowledge base updates without a developer (uploading files through the interface or a connected folder). English language. History of requests for quality control. Technical expectations: LLM via API (Claude/OpenAI — propose with a cost calculation for tokens), RAG pipeline (vector database, embeddings), hosting on our server or in the cloud, HTTPS. The architecture should allow for future connection of the assistant to the analytical data warehouse (parallel project). In the response, indicate: examples of similar RAG projects, stack, timeline, cost of work, and estimated monthly ownership cost (tokens + hosting).