AI Bot for Outlook

Budget: 1700 UAH Deadline: 4 days

Freelancehunt
Hello!

I am interested in your project for developing an AI bot for automating email processing and parsing commercial offers.

The task is clear and understandable:

Set up a connection to Outlook mailboxes (via Microsoft Graph API / IMAP, with a provision for future connections to other services like Gmail).

Implement analysis of incoming emails and attachments using AI (for accurate recognition of price offers, specifications, and details).

Set up automatic structuring and recording of extracted data into an Excel file (.xlsx) or Google Sheets.

I am ready to discuss the architecture of the solution, the format of the output Excel file, and clarify details regarding the formats of incoming emails (PDF, Excel, text in the body of the email).

When would it be convenient for you to communicate? Expand

Budget: 700 UAH Deadline: 7 days

Hello! I have practical experience in developing Python automation scripts and AI integrations for processing documents, emails, and spreadsheets.

🛠 Here's how I'll do it:
1. I will set up a connection to Outlook (via Microsoft Graph API or IMAP) to monitor incoming emails.
2. I will connect an AI model (OpenAI GPT-4o / Claude API) to analyze the content of the emails and accurately extract pricing information into a structured format.
3. I will automate saving the collected data into an Excel file with the necessary columns.
4. I will establish a modular architecture for easy integration of other email services in the future.

🎥 Portfolio: Freelancehunt

I would be happy to discuss the details in chat and get started! Expand

Budget: 5700 UAH Deadline: 7 days

Good day, I have over 5 years of experience in development, often working with AI bots and data processing in email services.

At Throngwatch, I wrote both a Discord bot and the backend, as well as the dashboard: there were also integrations with third-party platforms.

I will create a bot for Outlook on Node.js with OpenAI API integration and data export to Excel, allowing for expansion to other email services.

I aim to have the first working version ready within a week. I will provide the exact cost after a few clarifications or in private messages.

Is a specific Excel format required for the export, such as a table or template? I have attached a sample of my work, please take a look.

Feel free to reach out! Expand

Budget: 5000 UAH Deadline: 4 days

Hello, Magomed! I will do it in Python: connecting to Outlook via Microsoft Graph API, monitoring incoming messages, extracting data from commercial offers through LLM (I will automatically normalize prices in different currencies/formats, more reliable than regex), writing to Excel via openpyxl.

I also analyze attachments (PDF/Word/Excel) — prices are often hidden there, not in the body of the email. The architecture is modular — connecting other email services later will not require reworking the entire logic.

Deadline — 4 days, price — 5500 UAH. I would like to clarify: how many Outlook accounts at the start and what is the approximate flow of emails per day — real-time processing or a one-time pass through the incoming?

Budget: 18000 UAH Deadline: 7 days

Hello, I recently created a similar bot for processing emails: connecting mailboxes via API, searching for the necessary emails based on key criteria, extracting data from the body of the email using LLM, and writing it into a structured Excel. Reading commercial proposals with prices and parsing them into a table was part of the same project.

If the prices in the emails come in different formats and currencies, it’s worth adding normalization through LLM right away; this eliminates manual reconciliation and saves about 5-7 hours a week.

How many Outlook mailboxes are there at the start, and what is the approximate flow of emails per day? Should the parsing be in real-time or a one-time pass through the incoming ones?

I suggest we get in touch; I will also sketch a solution scheme for your mailboxes and the structure of fields for Excel.

Budget: 7000 UAH Deadline: 14 days

Hello! The task is clear: I need a bot that monitors Outlook for incoming emails with commercial proposals, parses them, and creates a structured Excel table. This type of task is exactly what I'm interested in.

I have 3.5 years of commercial experience in TypeScript and NestJS. In a similar project, I developed integrations for a B2B platform where it was necessary to automatically process incoming data from suppliers and consolidate it into a single registry. Essentially, the logic is similar, only the source here is an email inbox. For Outlook, I will connect via Microsoft Graph API, and I will extract data from emails using LLM (GPT or Gemini, depending on the budget). Writing to Excel will be done through exceljs or an equivalent. I will design the architecture so that connecting other email services in the future can be added without rewriting the core.

I am ready to discuss the details: the format of incoming emails, the structure of Excel, the frequency of checking the inbox, etc. Write to me, and we can quickly synchronize.

Budget: 2500 UAH Deadline: 1 day

I see that the bot needs to independently track incoming commercial proposals in Outlook, extract prices, and record them in Excel. This is a working scheme, but there is one aspect that determines the entire architecture: access to mailboxes via Microsoft Graph with OAuth, or through IMAP? I have already worked with such a setup: an AI bot on Claude for a store, plus order automation in Telegram. I understand where there can be pitfalls with tokens and email parsing. Let's clarify which specific fields need to be extracted from the emails, and I will prepare an estimate.

Budget: 3000 UAH Deadline: 4 days

I will create an AI bot for Outlook that can find incoming emails with commercial offers and prices among the connected Outlook mailboxes, extract the necessary data from them, and record it in Excel. Would you like me to complete this work within 3–5 days?

Budget: 1000 UAH Deadline: 1 day

Hello. I will develop a scalable microservices architecture using Microsoft Graph API for integration with Outlook and applying LLM for accurate analysis of commercial proposals and extraction of key data. The system will be built with a focus on data security, reliability in processing large volumes of correspondence, and flexibility in configuration for further expansion to other email platforms. I have successful experience in creating similar systems for automating the processing of electronic correspondence and integrating with corporate dashboards, which will allow for effective use of ready-made modules and optimize the development process. I suggest discussing all implementation details, final budget, and timelines in private messages.

Budget: 6000 UAH Deadline: 7 days

Good day! I can develop a bot for Outlook that will automatically analyze incoming emails, extract necessary data from commercial proposals, and record it in Excel. I will foresee the possibility of further connecting other email services. I am ready to discuss the data format and start after agreeing on the details.

Budget: 22000 UAH Deadline: 12 days

Magomed, hello. The task is clear: a bot that automatically checks incoming emails in Outlook, finds commercial offers with prices among them, extracts the necessary fields, and compiles them into Excel.

Here’s how I do it:
1. Connecting email via Microsoft Graph API (official OAuth access to Outlook/365). The architecture immediately includes provider abstraction, so later Gmail or IMAP can be added without rewriting.
2. Filtering incoming emails: first, a quick sift by subject, sender, keywords, and presence of attachments, to avoid processing the entire stream through AI and burning the budget on tokens.
3. Data extraction through LLM with a strict response schema: supplier, nomenclature, price, currency, quantity, payment and delivery terms, offer validity period, email date. The model returns structured JSON, not text, so data doesn’t flow from email to email. Commercial offers in attachments (PDF, Excel, scans) are also parsed.
4. Validation: if a field is missing in the email or is ambiguous, the row is marked as needing verification, rather than filled with a fictitious value. This is critical, as errors in pricing are costly.
5. Writing to Excel (or Google Sheets, if more convenient) with deduplication, so repeated emails and forwards do not create duplicates.
6. Scheduled operation plus notifications in Telegram or email about new found offers. A panel with a log: what has been processed, what has been skipped and why.

Experience: I build systems of this class, with integrations of external APIs, queues, webhooks, and LLM as a structured data extractor. Stack: Node/TypeScript, PostgreSQL, OpenAI API, working with Microsoft Graph.

Questions to provide an accurate estimate:
- How many mailboxes are we connecting and what is the approximate volume of emails per day?
- Which specific fields should be included in Excel and is there already a ready-made table template?
- How often do prices come in attachments (PDF or Excel), or are they mostly in text within the email?
- Is Excel local, on SharePoint, or OneDrive?

I am ready to start with a small pilot on real emails, so you can immediately see the quality of extraction, and then scale it to all mailboxes. Expand

Budget: 7500 UAH Deadline: 3 days

Hello! I have been developing similar bots for over 3 years. I am ready to develop one quickly, on time, and with quality!

There are some points I would like to clarify during the discussion.

Budget: 5000 UAH Deadline: 3 days

Hello! I will complete your task quickly and efficiently. I have done something similar before.

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My portfolio: https://freelancehunt.com/ua/freelancer/romas6ka.html#portfolio
Message me, I will start working today. I look forward to collaborating with you! Expand

Budget: 11000 UAH Deadline: 5 days

Hello. I have practical experience in developing automation systems and AI scripts for processing emails and documents. I am implementing a modular architecture (Adapter pattern) so that in the future, adding Gmail or IMAP can be done without rewriting the main logic of the bot.

How it will be implemented:

- Email connection: Outlook via Microsoft Graph API (OAuth2). Processing not only the text of the email but also attached files (PDF, DOCX, XLSX), where the KP usually lies.
- Parsing through AI: using Structured Outputs (strict JSON), which guarantees the exact data format (price as a numeric value, currency separately, nomenclature, and contacts) without column disruptions.
- Saving and efficiency: automatic recording in Excel + saving the Message-ID of processed emails to protect against duplicates and unnecessary API costs.

I am ready to clarify the desired structure of the final Excel file and start working.

Budget: 6000 UAH Deadline: 3 days

I understand the task: manually collecting prices from incoming quotes is a waste of time and prone to errors. I will create a bot in Python: connecting to Outlook via Microsoft Graph API (standard, your Azure application will be required), the AI module extracts the supplier, items, and prices from the email text and attachments (PDF/Word), and writes the result to Excel. A separate layer for email connection will be added later for Gmail without reworking the core. I have previously created a parser with automatic recording to a table for Allegro (https://freelancehunt.com/showcase/work/razrabotka-parsera-sistemyi-avtomatizatsii-allegro/2039438.html). How many Outlook accounts will we connect and what is the flow of quotes per day? Are prices more often in the text or in attachments?

Price: 6000UAH
Term: 3 days

Budget: 6000 UAH Deadline: 10 days

Hello! I can create a bot for Outlook that will analyze incoming emails in 2 seconds and extract the necessary data for further processing in Excel. I will use the Python programming language and libraries such as `pywin32` for working with Outlook and `pandas` for creating Excel files. I can send a sample code structure and bot architecture in private messages.

Budget: 6800 UAH Deadline: 5 days

Hello! I am Serhiy, I have 8 years of experience in IT across various technologies: web/mobile, backend, API, integrations, automation, and data handling.
I see the main task as follows: An AI bot for Outlook. We need an AI bot for Outlook (with the possibility of connecting to other email services in the future). The bot's task: to find among...

Skills for the task:
- backend/API and integrations
- AI/ML, RAG, and automation
- bots, parsing, and data handling
- reporting, analytics, and business automation

Portfolio: https://software.campstudio.net/#showcases

Budget: 5000 UAH Deadline: 5 days

I have significant experience in developing bots for routine automation using Python / AI / API. Your task is a classic use case for LLM, for example, using the OpenAI API to structure unstructured data from emails.

Budget: 700 UAH Deadline: 4 days

Hello, I am in the TOP 10 freelancers on the platform, I have completed such projects before, I will do it quickly, efficiently, and at a low cost.

Budget: 5000 UAH Deadline: 5 days

Hello!
I can create an AI bot for Outlook that:
• connects to mailboxes
• finds emails with commercial offers and prices
• extracts the necessary data using AI
• records the result in Excel
Stack: Python + Microsoft Graph API + LLM (OpenAI/Claude).
I will design the architecture so that other mail services can be added later.

Budget: 12000 UAH Deadline: 7 days

Good day,

I will set up a scenario on Make with Microsoft Outlook, AI processing through OpenAI or Claude, and recording results in Excel. The system will regularly check connected mailboxes, identify emails with proposals, extract the necessary fields — supplier, product or service, price, currency, terms, and date — and add them to a structured table. I will establish the logic so that other email services can be connected via API in the future without rebuilding the entire process.

I have implemented an AI auto-responder for emails: incoming messages are classified by importance, and the system prepares draft responses. I will apply the same approach for selecting and structuring commercial proposals.

Estimated cost: 8,000–15,000 UAH, depending on the number of mailboxes and fields to extract. Deadline — up to 1 week.

How many commercial emails do you receive approximately each month, and what data is critically important for you to see in Excel for comparing proposals?

Budget: 1500 UAH Deadline: 3 days

Good day!

For such a task, it makes sense to build the solution as a backend service with email source adapters: currently for Outlook via Microsoft Graph, and in the future — with the ability to connect other email services without rewriting the core. Within the system, it is better to separate the retrieval of emails, classification of commercial offers, AI extraction of structured fields, and the module for writing to Excel.

From a technical standpoint, there are several risks to consider. Emails from different providers will differ in structure, some data may come in PDF attachments or scans, so without separate handling of attachments, accuracy will be limited. It is also important to immediately incorporate protection against duplicates, reprocessing errors, and storing email statuses; otherwise, automation will quickly become unstable.

For an MVP, usually one email channel, a fixed set of fields, and export to XLSX are sufficient. If later multiple mailboxes, different email, or processing of PDFs/scans with OCR is required, it is better to implement that as a separate extension. In terms of timelines, this volume typically fits within 3–5 weeks after agreeing on the data schema and access to Outlook.

To accurately assess the scope, it is necessary to understand which specific fields should go into Excel, which email formats are actually processed — only text or also PDF/scans/attachments — and whether one Outlook mailbox or several with Microsoft 365 OAuth access are needed.

Budget: 4000 UAH Deadline: 4 days

Вітаю, готова виконати бота ШІ

Дизайн сайтів: Figma / Photoshop
Laravel - React Native JS - Vue
Верстка: HTML, CSS, PHP, C#, JavaScript, SQL, Sass/SCSS, LESS.

Телеграм боти - NodeJS
WordPress - Elementor - WooCommerce
OpenCart
Shopify

Budget: 6500 UAH Deadline: 7 days

Good day, Magomed!

Two things will determine whether this bot will actually work for you — and neither of them is about AI.

First and foremost: access to email. For the program to read corporate Outlook mailboxes, administrator permission in your Microsoft 365 is required — this is a separate action in the admin panel that you need to perform, not the contractor. If this is not clarified at the start, the project will stall halfway: the code is ready, but there’s nothing to read. Let me know if you have admin access — if not, there is a workaround through forwarding to a separate mailbox, which can also be done.

Second: duplicates. The supplier sends a price list, then clarifications in the same chain, then a colleague forwards it to you. Without separate logic in Excel, three lines will end up under one proposal, and the table will quickly become unusable — the manager will stop trusting it and revert to manual sorting. I will establish the definition of duplicates by supplier and item: a new price updates the old line, while the history of changes is stored separately — showing how the price has changed.

What I will do: connect to Outlook, filter only commercial emails among the rest, extract data from the body of the email and attachments, consolidate currencies and units into a single format, and record it in your Excel format.

And one more thing from practice: I will create a separate sheet for "verification" — emails where the bot is unsure about the recognized content. This is more important than it seems: a bot that quietly records an incorrect price is worse than having no bot at all.

I work in Python. I will show the result using your actual emails before finalizing everything — send me a dozen typical commercial proposals, and you will see the accuracy of recognition on your data, not on promises. Expand

Budget: 7500 UAH Deadline: 7 days

Good day. Here, I wouldn't build a solution on managing Outlook through the browser — it's more reliable to connect mailboxes via the Microsoft Graph API and process new emails directly through official access.

The bot will identify commercial offers among incoming emails, extract the supplier, items, prices, currency, date, and other agreed fields, and then add them to a structured Excel file. I will foresee protection against reprocessing and a log of emails that could not be recognized automatically.

The architecture can be laid out modularly from the start, so that later Gmail or other email services can be connected without rewriting the main logic.

Could you please let me know if the prices come mainly in the text of the emails or also in PDF, Excel, and Word attachments? And what exact columns should be in the final file?

Budget: 27000 UAH Deadline: 12 days

Hello!

First, the main question, as it affects half of the assessment: do commercial proposals come in the body of the email or as attachments? In my experience, most price lists are PDFs or Excel files attached, which is a different scope of work than analyzing the text of the email.

Now, to the essence.

Outlook. For several mailboxes under one tenant, it's Microsoft Graph, not IMAP — only Graph provides proper access to other mailboxes and allows connecting other services through the same layer. However, Graph requires your IT administrator to grant application permissions. This is the only part that is not in my hands, so I mention it before we start, not during the process.

Data extraction. Commercial proposals are written in free text — each supplier has its own format. Therefore, this is LLM with a structured response: supplier, position, price, currency, validity period, terms. No one will honestly tell you the percentage of accuracy. So I do it differently: each row in Excel has a confidence column and a link to the source email — you see what to check manually, rather than trusting the number blindly.

The key to the LLM API is billed separately based on volume — this is the service's bill, not my margin. I will provide an approximate monthly amount when it is known how many emails per day.

On a similar note: Lumina — a desktop application, four families of LLM provider protocols under one interface, native tool-calling. Plus a pipeline of aviation navigation data: 72 raw columns normalized across 19,000 airports. github.com/kamedashe

How many mailboxes and how many emails per day approximately? I will respond with the cost and timeline immediately, without "we'll clarify during the process." Expand

Budget: 7000 UAH Deadline: 5 days

Magomed, to allow the bot access to Outlook, it will be necessary to register the application in Azure AD and grant permission to read emails (Mail.Read) for the required mailboxes. This is a standard step in the Microsoft Graph API, and I will guide you through it. The bot will search for emails with quotes based on key features, the AI module will extract titles, prices, and other fields from the text, and if prices are more often found in PDFs or images, I will add recognition from there. The result will be written in Excel row by row; a similar mechanism for automatically recording structured data in a table is already working in my application bot (https://freelancehunt.com/showcase/work/zayavki-bot-telegram-mini-app/2060201.html). I will connect the mailboxes as a separate layer so that later I can add Gmail or another service without rewriting the core.

Before naming the final price, I will show the work on a few of your actual emails with quotes, as this is more accurate than guessing the format. Approximately from 7000 UAH and 5 days for one mailbox without attachments, I will confirm after your response. How many Outlook mailboxes and emails with quotes do you currently have per day, and are the prices more often in the text of the email or in attachments?

Budget: 27000 UAH Deadline: 5 days

Magomed, hello. The task is clear: connecting to Outlook, filtering incoming emails for commercial offers with prices, extracting data, and writing to Excel, with an eye on other email services later.

I will connect via Microsoft Graph API (official access to Outlook, without breaking protocols), as a separate layer of abstraction, so that later I can add Gmail or another service without rewriting the logic. For recognizing commercial emails and extracting prices/products/conditions, I will use LLM with structured output, rather than simple regex text parsing, as it is more stable across different email formats. I will write the data to Excel using a library for working with xlsx, with the option of either appending to one file or creating new rows for each email, depending on what is more convenient for you.

Using a similar multi-agent logic for processing content through LLM pipelines, I have already created AI Content Studio, where several agents also process incoming data and form structured results.

Implementation will take 5 days.

Question: how many Outlook mailboxes need to be connected at once and is there administrator access to them (OAuth permission to read emails is needed)?

Budget: 10000 UAH Deadline: 7 days

The system will scan Outlook mailboxes via the Microsoft Graph API, analyze incoming emails using LLM (Claude or GPT-4), and automatically extract commercial offers and prices into a structured Excel file.

I have built similar pipelines: the LLM agent analyzes the email text, determines if it is a commercial offer, extracts fields (product, price, supplier, date) — and writes them into a table. I will make the architecture modular: the mail provider connects as a separate adapter, so adding Gmail or an IMAP service in the future will be just one new module without rewriting the core.

Approximately — about 25-30 hours of work, but the final scope will be agreed upon after clarifying the details: the number of mailboxes, the list of fields to extract, and whether processing attachments from PDF or DOCX is needed.

Write to me — we will discuss the details and get started.

Budget: 3200 UAH Deadline: 3 days

The bot should distinguish commercial offers from regular letters based on prices and price lists, am I understanding the task correctly?

I will connect through the Microsoft Graph API, with an expectation for further email services. The AI will extract items and prices, and I will record the results in Excel.

Approximately 3 days for implementation.

Budget: 3600 UAH Deadline: 3 days

The task is clear: connecting to Outlook via Microsoft Graph API, monitoring incoming emails, filtering emails with commercial offers (by keywords and text structure or through GPT for content analysis), extracting prices and data, and recording in Excel.

Design the architecture so that the email parser is a separate module: this way, connecting other email services later will not require reworking the entire logic. For recognizing prices and product positions, I will use LLM, which is more reliable than regex on arbitrary emails.

Question: how many mailboxes and emails per day approximately, to assess the load? I am ready to deliver the first working version approximately in 3 days.

Budget: 6000 UAH Deadline: 2 days

Hello, I can create an AI bot for Outlook that will find emails with commercial offers, extract the necessary data, and record it in Excel. I have experience with Node.js bots, n8n, and Hermes; for this task, I consider Hermes to be the best AI layer. The advantage of Hermes is that the logic, instructions, and behavior of the bot can later be edited through a chat in Telegram without constant code modifications. Implementation: connecting to Outlook via API, processing emails with the AI module, normalizing data, and writing to Excel; the architecture can be prepared for future connections to other email services. If a separate backend/API around the bot is needed, I can create it using Node.js or FastAPI. git: github.com/onyx144

Budget: 3000 UAH Deadline: 5 days

Hello, Mahomed! To collect KP from Outlook, it's important to configure the AI so that it doesn't miss attachments (PDFs, photos), where prices are often hidden. I have created similar parsers in Node.js/Python for data collection in tables.

Which specific AI model do you plan to use — OpenAI API or something local for confidentiality?

I am ready to outline the structure of the modules for parsing emails.