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  • Claude for the Editor: The Power of Neural Networks in Document Management and Beyond

    Claude for the Editor: The Power of Neural Networks in Document Management and Beyond

    Claude for Editors: The Power of AI in Document Work and Beyond

    The Claude AI, developed by Anthropic, is rapidly gaining momentum, offering unique capabilities for working with text data. From processing long documents to generating creative content, this AI, according to its developers, can change the approach to editorial tasks. But is this really true? We tested its functionality on real tasks to understand where Claude truly shines and where it still requires human oversight.

    This platform, which has evolved from a simple chatbot to a powerful ecosystem, can now accept PDFs up to 1000 pages, work with Excel spreadsheets, and efficiently search for information. AI content production with Claude is becoming a reality, especially for those looking for AI production by subscription or striving to scale the impossible.

    Claude’s Evolution: From Chatbot to Ecosystem

    Anthropic, founded in 2021 by former OpenAI employees, introduced the first publicly available version of Claude in 2023. Since then, the AI has gone through several generations, reaching version Claude 5.5. The lineup includes models:

    • Claude Sonnet 5.5: for everyday tasks, document analysis, programming, information work, and mathematical calculations.
    • Claude Opus 5.5: for more complex and lengthy tasks.
    • Claude Fable 5.1: the most powerful for complex reasoning and autonomous assistant work.
    • Claude Haiku 5.5: the fastest and most affordable for mass tasks.

    All current models support a context window of up to 1 million tokens and a response of up to 128 thousand tokens. However, it’s worth remembering that files, conversation history, and service data also take up space, reducing the actual limit.

    Tariffs and Availability: How much does one AI creative cost?

    The free version of Claude allows you to use chat, upload documents, and work on projects, but has a floating limit. Paid tariffs offer significantly more possibilities:

    • Pro: 15 euros per month (180 euros per year) plus VAT. Provides a larger volume of work, additional models, and tools.
    • Max: from 90 euros per month for intensive users.

    For companies, Team and Enterprise tariffs are available with employee management functions, corporate search, and a single invoice. Russia is not yet on the list of officially supported countries, but Claude perfectly understands and generates texts in Russian, which makes it attractive for AI UGC for a crypto project and other Russian-language tasks.

    How to work with Claude: interface and functionality

    Registering with Claude.ai is simple: via Google or a one-time link to your email. The interface is intuitive, but to achieve the best results, it’s important to formulate requests as detailed as possible.

    “Shorten the material to 3,000 characters. Preserve surnames, dates, amounts, and the author’s conclusion. Do not add facts not present in the original. After the text, list what had to be removed.”

    This approach allows for more accurate and useful results than simply “shorten the text.”

    Working with files and projects: scaling was impossible — now it’s possible

    Claude supports uploading files in PDF, DOCX, CSV, TXT, HTML, ODT, RTF, EPUB, JSON formats, as well as JPEG, PNG, GIF, WebP images. Up to 20 files, each up to 500 MB, can be added to one chat. PDF documents up to 100 pages are analyzed considering text and images; longer ones are analyzed by text only. This is critical if you need to generate 100 videos or even an AI conveyor of 500 videos per day, as graphic processing in large documents is limited.

    The “Projects” feature allows you to create a knowledge base from documents, text, and code, which Claude will refer to when performing tasks. For example, you can set general instructions:

    Claude for editor: the power of neural networks in working with documents and beyond — illustration 2
    • Respond in Russian.
    • Do not change the spelling of names.
    • For each output, indicate the document and page.
    • If data is not available, state this directly.

    This significantly increases the efficiency of AI against real UGC, as the neural network becomes more predictable and manageable.

    Claude’s skills: automating routine

    Skills in Claude are folders with instructions and scripts that the neural network applies when it deems them suitable for a task. This allows for automating repetitive processes and significantly speeding up work, for example, when mass generating videos with AI or creating AI commercial videos.

    Testing Claude: what the neural network can do in practice

    We tested Claude on several tasks typical for an editor:

    1. Analysis of a voluminous PDF: Claude successfully compared economic scenarios from a 229-page Bank of Russia report, but clarification was needed for precise matching of indicators. This highlights the need to double-check answers, especially when dealing with financial data.
    2. Navigation through applications: The neural network created a brief navigator for ten applications, but additional commands were required for accurate formatting.
    3. Text editing: Claude shortened the text, preserving numbers, but lost an important caveat about the temporary nature of the economic slowdown. When working with an AI avatar with subtitles or AI voiceovers, such nuances can be critical.
  • Working with tables: The model correctly identified revenue drops and calculated order costs, pointing out missing data. It was important to request formulas and a list of omissions in advance.
  • Image recognition: Claude recognized pizza and created a recipe, and also recognized text on a cake with a resignation letter. However, for small inscriptions and curved surfaces, manual verification is recommended.
  • Argumentation: The neural network successfully objected to the author’s reasoning about screen adaptations but made a factual error, calling the 1968 film “The Brothers Karamazov” a TV series. This proves that deepfake-ethically only verified facts should be used.

    • API Cost: How much does 1 minute of AI video cost?

    Separate tariffs are used for working with the Claude API. For example, Claude Sonnet 5.5 costs $2 per million input tokens and $10 per million output tokens. Fable 5.1 costs $10 and $50 respectively. Haiku 5.5 costs from $0.10 and $0.50. These costs are not included in the Max subscription and are paid separately. This is important to consider when planning video generation from text or using the mass generation API for AI video for advertising.

    Frequently Asked Questions

    What is Claude and how does it differ from ChatGPT?

    Claude is a neural network from Anthropic, focused on deep analysis of long documents and complex reasoning. Unlike ChatGPT, Claude often demonstrates higher accuracy in working with large texts and has a more flexible system of projects and skills.

    Can Claude be used to create AI commercials?

    Yes, Claude can be useful at the stage of script creation, processing text information for animated AI characters and AI clips with face replacement. However, for full-fledged AI video for advertising, integration with other tools will be required.

    How much does one AI creative or minute of AI video cost?

    The cost depends on the Claude model used, the volume of tokens, and the complexity of the task. For accurate calculations, it is necessary to study the API tariffs. For example, the cost of 1 minute of AI video can vary depending on how much text needs to be processed and generated.

    Where to order AI UGC for crypto?

    Claude can be an excellent tool for creating AI UGC for a crypto project, helping to generate texts, analyze data, and even create scripts for UGC advertising. To order services for creating AI content for crypto projects, it is recommended to contact specialized agencies or freelancers working with neural networks.

    Is Claude an analog of Synthesia?

    Claude and Synthesia solve different problems. Claude focuses on text analysis and generation, while Synthesia specializes in creating videos with realistic AI avatars. Claude can be used to write scripts that will then be implemented in Synthesia.

    Conclusion

    Claude is a powerful tool for those who regularly work with large volumes of text information and strive to automate routine tasks. Its strengths include document analysis, project work, and the ability to engage in complex reasoning. However, like any neural network, Claude requires careful verification of results, especially where high accuracy is important. For editors aiming for AI efficiency in their work, Claude offers unique opportunities for subscription-based neuro-production. Contact us to discuss how Claude can transform your workflow and help scale the impossible.

  • Artificial Intelligence in Business: How to Build a Team of AI Employees

    Artificial Intelligence in Business: How to Build a Team of AI Employees

    In today’s business environment, many leaders face the same headache: how to optimize workflows and free up team time for strategic tasks. Instead of just using AI for one-off requests, imagine having a whole staff of AI employees who perform routine operations, following your standards and schedule. This article will explain how to turn AI from a simple tool into a full-fledged member of your team, capable of taking on some of the work and increasing business efficiency.

    Implementing AI: From One-Off Requests to a Systemic Approach

    Many companies claim to actively use AI. However, as Callan Faulkner, founder of The Uncommon Business, notes, this often boils down to one-off requests. For example, a sales manager might spend an hour creating a commercial proposal using Claude or ChatGPT, but the next time a request comes in, the process starts from scratch. This is inefficient and does not allow for the full potential of the technology to be utilized.

    • Lack of reusable instructions.
    • Failure to create a knowledge base (successful proposals, objection handling methods).
    • AI does not become a repeatable system that supports the process.

    Creating an AI employee is a completely different skill. Callan defines it as “a trained, reusable AI system that performs a specific business function as well as or better than a human.”

    Benefits of a Systemic Approach to AI

    Systemic AI implementation brings significant benefits. Callan Faulkner’s company, The Uncommon Business, projects $40 million in revenue with a staff of about 50 people. A few years ago, such a result would have required a much larger team. The goal is not to reduce staff, but to unlock human potential.

    “Callan has never fired a person because of AI. Instead, AI allows employees to focus on strategic and creative work that truly inspires them.”

    However, a gap is already noticeable between employees trained in AI and those who are not. A marketer without AI skills can spend weeks creating landing pages and images, while a trained specialist does 10 times more in a day. The problem is not replacing humans with AI, but the need for humans who can effectively manage AI.

    Three Fundamental Concepts for Creating AI Employees

    Before you start creating an AI employee, you need to master two key concepts and one important asset.

    Concept 1: Finding the shortest path to an “A+” result

    Many professionals, by habit, strive to do everything themselves, proving their worth. Today, this becomes a burden. Callan’s philosophy: the fast track to an outstanding result is a new competitive advantage. This does not mean “cutting corners,” but rather optimizing the process.

    For example, Callan’s team used to spend days developing Instagram carousels. Now, after training Claude Design on examples of their best work, they create a polished carousel in eight minutes, and ten carousels in half an hour. The quality remained the same, but time and energy costs were reduced.

    Concept 2: Training equals quality of result

    Without proper training, AI produces generic and impersonal content. To get a unique and high-quality result, you need to document standards:

    • Publication goal.
    • Brand tone and voice.
    • Examples of successful content.

    If these details are not documented and passed on to the AI, it will have nothing to base itself on. Callan gives an example from her podcast: every step of the process must be described in detail so that AI employees can work at the proper level.

    Asset: Your “business brain”

    A “business brain” is a centralized repository of all important company documentation: prices, processes, standards, brand voice, ideal customer profiles, past successes, etc. If the documentation is insufficient for a new employee, it will be insufficient for AI. AI employees are only as effective as the data they have access to. The time of chaotic files on Google Drive is over.

    How to build an AI employee from scratch

    The process begins with an audit of current tasks. Callan recommends identifying all daily, weekly, or monthly tasks that generate revenue but do not bring satisfaction or are valued at less than $50 per hour. This could be:

    • Content design and editing.
    • Creating presentations.
    • Generating commercial proposals.
    • Writing email newsletters.

    For example, Callan’s copywriter, Nick, spent most of his time writing sales pages. He documented his approach, analyzed the most successful pages, and began creating AI employees to produce content. Now Nick manages a team of AI copywriters and QA specialists, focusing on strategy and creative direction.

    Professional tip: Even tasks in which you feel like an expert are worth analyzing. Callan found that after training AI in processes in which she considered herself the best, the AI’s results sometimes surpassed her own. Transitioning from creator to editor is a more efficient use of time.

    AI Interview Method

    Once you’ve defined the task, open a new chat in Claude and describe the situation in detail:

    • Who you are.
    • What the company does.
    • What specific task the AI should perform.
    • What the ideal outcome looks like.

    Then, ask Claude to conduct an interview by asking five high-impact questions to extract your exact process. Callan prefers voice input as it provides richer context. Tools like Wispr Flow allow you to speak rather than type, which facilitates a more complete and nuanced description.

    “Board of Directors” Technique

    For complex decisions, Callan uses Claude to assemble a team of 3-5 experts in the relevant field to discuss the best approach. For example, when creating executive compensation packages, she asked Claude to draw on frameworks from Mark Cuban and Sara Blakely. This resulted in recommendations based on proven business thinking, rather than generic search results.

    Training and Testing AI Employees for Outstanding Results

    Once you’ve achieved an excellent result in a chat with Claude, the next step is to save it as a “skill.” Within Callan’s framework, a skill is a saved, reusable set of instructions that is available at any time and connected to Claude. It’s like a “prompt on steroids”: one command triggers a complex, trained process. “A skill is an AI employee,” says Callan.

    Managing Skill and Memory Changes

    After creating a skill, Callan asks Claude: “What do I need to do to turn this into a reusable skill?” The answers usually point to creating a Claude Project — a workspace where knowledge files, instructions, and skills are stored. For example, for a sales proposal skill, the project might include past successful and unsuccessful proposals, all pricing information, and 15 key objections with detailed descriptions of how to handle them.

    Artificial intelligence in business: how to create a team of AI employees — illustration 2

    The final layer is connectors. If a workflow involves external tools (e.g., saving a proposal to a CRM or sending it via Salesforce), Claude connectors allow the skill to interact directly with these platforms. A fully built AI employee works as a skill, based on a knowledge base, with activated connectors, and the entire team is trained to use it.

    Testing and Refinement

    After creating a skill, the real work begins. Callan advises: test each skill to exhaustion. Most people are content with mediocre results from AI. Callan recommends persistently demanding better. If Claude produces something mediocre, she explicitly states that it can do better and asks it to try again. Often, it is after such a “push” that the desired result is achieved.

    Her skill as a voice copywriter, one of the most valuable AI employees, required fifteen hours of work. This investment is justified when compared to human training. It took three months of weekly work for her copywriter to start writing in her style. Many, however, give up on AI training after 45 minutes, believing it cannot grasp their style.

    The testing process follows a specific pattern:

    1. Launch the skill.
    2. Check the result.
    3. Manually correct parts that do not meet the standard.
    4. Return corrections to Claude with a specific request: “I made the following changes to your output to meet my standards. Please update your skill to incorporate these changes and use them in future outputs.”

    A useful trick: during the conversation, ask Claude: “What have you learned from our interactions?” and ask it to update the skill so that these lessons are reinforced. An important technical detail: skills and memory in Claude Projects can become out of sync. Claude can update its memory based on the conversation but not update the underlying skill. Callan recommends explicitly telling Claude to update the skill itself, and then manually clicking the save button.

    Projects also have their own invisible project-level skills that can conflict with workspace-level skills. When creating a skill that should work across all projects, specify “workspace skill” or “core skill” so that it is available everywhere, not just in one project.

    Pro Tip: For teams building skills at scale, Callan’s company copies each skill into a Notion database, tracking who created it, when, the version, and its purpose. They even have a skill that checks their skill database for duplicates. Each department manages its skill repository, and skill management is a standard part of every quarterly review.

    Scheduling AI Employees for Autonomous Execution

    Once a skill has been thoroughly tested and refined to a high standard, it can be scheduled for autonomous work without human intervention.

    Current limitation: the computer running Claude must be turned on for scheduled tasks to execute. Callan uses a dedicated office computer logged into the corporate Claude account. Any employee wishing to schedule a skill logs into this account on that machine.

    Callan uses Claude’s scheduled task feature via the Claude Cowork desktop application. There are options to select the day, time, and frequency of skill execution. There is no limit to the number of active scheduled tasks.

    One of Callan’s scheduled skills is the “Instagram Researcher.” Every Monday morning at 6:00 AM, it visits competitor Instagram accounts, identifies viral content in the AI education sphere, analyzes the approaches and “hooks” used, extracts content transcripts from the previous week, and adds content ideas directly to the Notion database. Her social team logs in on Monday morning and manually approves the ideas they want to implement. What used to be a routine task for the social team on Monday morning is now performed autonomously.

    Another scheduled skill runs hourly, extracting meeting transcripts from Granola and archiving them into a Notion database that powers her “second brain.”

    Conclusion: Invest in AI Employees for Business Scaling

    Creating AI employees is not just automation; it’s an investment in the strategic scaling of your business. Shifting from routine operations to managing efficient AI systems allows your team to focus on creativity and innovation, addressing “customer pain points” related to lack of time and resources. If you are looking for full-cycle short video production or comprehensive promotion through short videos, consider how AI employees can take on some of the work. This allows you to order everything in one place, effectively replacing part of the team with a service. Are you ready to rethink your business processes and build a team where AI and people work in harmony? Start with a task audit and document your standards today. Learn more about how to choose a contractor for social media content outsourcing, and what mass posting quality criteria are truly important to avoid wasting your budget.

    Frequently Asked Questions

    What is an AI employee and how does it differ from regular AI use?

    An AI employee is a trained, reusable AI system that performs a specific business function as well as or better than a human. Unlike one-time AI requests, an AI employee is part of a systematic approach that automates repetitive tasks, following defined standards and schedules.

    How do I start creating my first AI employee?

    Start with a self-audit: identify tasks that are time-consuming, revenue-generating, but unfulfilling or undervalued. Then document each step of performing that task, its purpose, quality standards, and examples of successful outcomes. Use the AI interview method in Claude to describe the process in detail.

    Do I need to be a technical specialist to create AI employees?

    For 90% of business operations, creating AI employees does not require technical skills. The key is communication (the ability to clearly articulate needs) and creativity (resourcefulness to bring AI to an outstanding result).

    How to ensure high-quality results from an AI employee?

    The key to quality is thorough training and testing. Document all standards, tone of voice, examples of ideal results. After creating a skill in Claude, test it exhaustively, demanding improvements from the AI until the result meets your “A+” standards. Don’t forget to regularly update the AI’s skills and memory.

    Can AI employee work be automated on a schedule?

    Yes, once a skill is debugged, it can be scheduled for autonomous execution. Use scheduling features in tools like Claude Cowork to set the day, time, and frequency for the skill to run. This allows for the automation of routine tasks such as data collection, competitor analysis, or content generation.