Who we are

About AFR LOGIC

We turn data chaos into structured knowledge for AI.
You send us the data remotely, we process it on our own private servers. No on‑site visits, no external APIs, and complete confidentiality.

🔐 Remote processing 🧠 Custom software ⚙️ LLMOps & RAG 🗑️ We don't keep data
Learn about our service Request processing
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Our Mission

Why we do what we do

To help companies and organizations prepare their data for artificial intelligence from a distance. We believe that the potential of AI lies not in the models, but in the quality of the data that feeds them, and in the trust that everything is processed remotely and securely on private servers.

"We don't visit your premises. You send us the information and we do the heavy lifting. The result comes to you clean, organized, and ready for your AI."

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Our Vision

The future we are building

To be the go‑to technology partner for companies that want to master their data without having to expose their local infrastructure. We don't just process information; we design custom software architectures that enable businesses to operate with AI efficiently, remotely, securely, and scalably.

Scalable Remote AES-256 encrypted
Our ethical foundation

Values that define us

We don't just do the job. We define how we do it, based on solid engineering and data ethics principles.

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Remote Processing

We never go to your premises. You send us your files (PST/MBOX, documents, URLs) via secure links. All work is done on our private servers.

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Total Confidentiality

We work under strict NDAs. We process data exclusively on our private servers and delete it completely after delivery. We keep no copies.

Key
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Data‑Focused

We are not a generalist AI consultancy. We are specialists in preparing clean, structured, and duplicate‑free data so your AI performs correctly.

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No External APIs

Data is never sent to third‑party clouds. Processing happens 100% within our controlled environment, guaranteeing maximum security for your intellectual property.

“We are not magicians. We just process data.”

That phrase sums up our philosophy. We don't make impossible promises or work on‑site. We extract, clean, organize, and deliver data from a distance, in the format your AI needs. No noise, no signatures, no duplicates.

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Scientific Rigor

We apply data engineering methods based on information science. Every cleaning and classification algorithm is tested before implementation.

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Iteration & Continuous Improvement

We develop custom software tools that evolve with your business. We don't deliver a static product; we build a living knowledge base.

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No Retention

Once the client receives their processed .md files, we delete all original and temporary data from our servers. That is our security promise.

How we work in 3 simple remote phases

📥 Phase 1

The client shares their files (PST, MBOX, Docs, Web) via a secure transfer link.

⚙️ Phase 2

We process the data on our private servers, apply SHA‑256, remove noise, and convert to .md.

📤 Phase 3

We deliver the cleaned, organized files. We permanently delete the data from our systems.

Our work

What we do exactly

From remote data extraction to token‑cost optimization for your AI.

Pillar 1

Remote email processing (PST/MBOX)

We extract and clean the content of your PST (Outlook) and MBOX (Thunderbird, Gmail) files. We apply SHA‑256 deduplication to remove duplicate emails, strip out automatic signatures, eliminate "Re: Re: Re:" chains, and leave only the useful text. The result is a .md file that weighs a fraction of the original.

  • ✅ Extraction of email bodies and metadata.
  • ✅ Removal of HTML noise and tracking pixels.
  • ✅ Conversion to standard Markdown (.md).
🔻 Token savings: Removing signatures reduces up to 80% of unnecessary text per email.
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Pillar 2

Documents and websites

We extract and structure content from Word, Excel, PDF, PowerPoint, and websites. We remove redundant navigation from web pages and extract the real text that your AI needs to learn. This prevents your RAG system from wasting tokens on menus, footers, and broken links.

  • ✅ Text extraction from digitally formatted PDFs.
  • ✅ Cleaning of complex Excel tables.
  • ✅ Structuring of web content into .md.
Pillar 3

Organization and Classification

We segment content by functional area (Customer Support, Finance, HR, Operations, etc.) so that your AI doesn't mix contexts. This is the foundation of an efficient "tokenomics": if the AI searches only in the right department, it consumes far fewer tokens and delivers more accurate answers.

  • ✅ Classification by department and topic.
  • ✅ Creation of automatic executive summaries.
  • ✅ Content hierarchy for better data retrieval.
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Pillar 4

Tokenomics Optimization

We don't build agents, but we drastically reduce the operational cost of your AI. By cleaning noise, removing duplicates, and organizing by area, we ensure that your RAG system or LLM has to query far less unnecessary text. Fewer tokens queried = lower AI bills.

  • ✅ Token reduction per email (up to 80% in some cases).
  • ✅ Prevent the model from "hallucinating" by reading signatures and footers.
  • ✅ Optimized chunking to avoid duplicating contexts.
💡 The key: It's not about having the best AI, but about giving it the cheapest and most precise data to process.
Competitive advantage

Why us and not someone else?

Our value proposition lies not in what we sell, but in how we build it, how we process it, and how we protect your data.

Key aspect Traditional approach Our approach (AFR LOGIC)
Data access Require physical access to client servers or on‑premise software installation. 100% remote. Client sends files via secure links (WeTransfer, Drive, etc.).
Security Data travels to third‑party clouds or stays on uncontrolled infrastructure. Processing on our own private servers. Nothing leaves our environment. We don't keep data.
Scope Process a single URL or document. We process complete email accounts (PST/MBOX), documents, and websites.
Deliverable Raw, unorganized data with noise. Clean, area‑organized data in standard .md.
Development Generic solutions and fixed templates. Custom software and autonomous agents tailored to your business.
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We don't keep data

At the end of the project, we perform a complete purge of data from our systems. No hidden copies or forgotten backups.

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Encryption & NDA

All files are transported and processed under AES‑256 encryption. We sign strict confidentiality agreements before starting.

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Remote advisory

We help you gather and prepare your data from your office, remotely, via video calls and guided assistance.

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Cost optimization

Our data cleaning drastically reduces token consumption, resulting in direct savings on your AI bills.

The method

Process and processing philosophy

How we turn chaos into code and actionable data, applying pure data engineering principles.

1

Reception

Remote reception of PST/MBOX files.

2

Extraction

Extraction of text and metadata.

3

Cleaning

SHA‑256, removal of signatures and HTML.

4

Conversion

Conversion to .md and classification.

5

Delivery

Delivery and data deletion.

Our engineering principles

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Efficiency over complexity

We don't use tools for the sake of trend. We use lightweight, robust scripts to extract and clean data, focusing on the final result.

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Isolated processing

Each project runs in ephemeral processing containers. When it finishes, the container and the data are destroyed.

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Hash‑based validation

We use mathematical algorithms (SHA‑256) to verify that each email and document is unique before converting to .md.

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Context optimization

Our ultimate goal is not just to "clean data". It is to reduce the amount of text the AI has to read to give you a correct answer.

Research & Development

Advanced thinking in data optimization

We don't just execute tasks. We research how to drastically reduce token consumption and improve the efficiency of RAG systems.

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Tokenomics Research

We study model behavior with different chunking granularities and contexts. Our cleaning algorithms are designed to maximize value per token. A document with noise can cost 5 times more in tokens than a clean one.

Research example (Costs):
Original file (with signatures): 4,500 tokens.
Processed file (only useful text): 500 tokens.
Savings per file: 89% of tokens.
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LLMOps and Cost Control

We don't build AI agents, but we design the "context call" rules so that your RAG systems (or any LLM) query exactly the necessary information. By reducing noise and duplicates, the AI needs to make fewer queries to find the answer.

Operating principle:
# Less context = Fewer tokens = Lower cost.
## Goal: Reduce the client's AI bill without losing answer quality.

Data Optimization Lab

1

Smart Hash Deduplication

We research the exact threshold where SHA‑256 can remove duplicates without losing important metadata variations.

2

Semantic Chunking Strategies

We study how to split long documents into complete "thought blocks" so that RAG doesn't have to read 3,000 tokens to find a 50‑token answer.

Trust

What our clients say

Real results from companies that trusted us. (All testimonials are anonymous due to NDAs.)

★★★★★

"We had 15 years of accumulated emails in our customer service mailbox. They converted everything into .md files organized by area and built us an internal assistant that finds answers in seconds. All remotely, without anyone setting foot in our office."

Operations Director

Logistics sector

NDA protected
★★★★★

"What worried us most was security and customization. We needed a custom RAG pipeline. They not only cleaned our data, they developed a custom API to connect to our platform, all without accessing our local servers."

CTO

Fintech sector

NDA protected
★★★★★

"We thought it was expensive because it was our first time and we didn't know the market. But they were patient with us, explained everything, and in the end the service saved us a huge amount of time. We no longer have to spend hours looking for old emails. It was totally worth it."

Purchasing Manager

Industrial sector

NDA protected
★★★★★

"We are a large company and we were afraid that someone would see our information. They work under NDA and with remote local processing, which is what convinced us. The final result was better than we expected."

CEO

Educational technology

NDA protected

Explore our service and software ecosystem

Prepare data for RAG Convert to Markdown Organize company emails Extract text from emails Prepare data for ChatGPT Knowledge base for AI SHA-256 deduplication Prepare data for NotebookLM Clean signatures and HTML noise Convert PST or MBOX to Markdown Data consulting for SMBs
Start today

Ready to prepare your data for AI?

You have years of information accumulated in a department's mailbox, documents, and your website. We deliver everything clean, organized, and in .md. Your AI does the rest.

No impossible promises. Just well-done work. SHA-256 Deduplication · Private server processing · .md ready for RAG

Request Processing Service Data Consulting

📬 Do you have PST or MBOX? We convert to Markdown with signature cleaning, HTML noise removal, and SHA-256 deduplication.