business

Building a Platform for Automating Control Processes

Control processes are essential for ensuring consistency, compliance, and reliability in business operations. Automating these procedures can save significant time, reduce errors, and improve overall efficiency. This guide outlines the key elements needed to design a platform that supports the creation, execution, and testing of control processes.

The platform should allow users to log in securely and create new control processes. A straightforward setup makes it easier for users to begin their workflows. Once logged in, users can start by selecting the subject of the control process. Subjects might include physical or digital objects, individuals, documents, data, or even larger business processes. Providing flexibility in subject selection ensures broad applicability across different use cases.

From there, the platform should help users identify relevant rules, laws, and regulations connected to the selected subject. This step is crucial for ensuring compliance. Whether referencing internal rules or external regulations, users need tools to locate these frameworks easily.

Defining control outcomes is an essential step. Outcomes represent the end goals of the control process and ensure the work done has measurable and actionable results. Success might be defined as confirming compliance, identifying gaps, or achieving specific metrics like reduced error rates or improved processing times.

The workflow design, or control flow, forms the core of the control process. It maps out how tasks are executed—from sequential steps to branching pathways that account for conditional outcomes. This structure needs to be intuitive but flexible, allowing users to adapt workflows as requirements evolve. Supporting templates and visual design tools can simplify the creation process further.

Before deployment, control processes should be tested and validated within a simulation environment. Simulation helps identify weaknesses or inaccuracies while ensuring the process handles typical use cases and edge cases effectively. Users can iterate on their workflows based on test results, reducing the risk of issues when processes go live.

Building and refining automation for control processes is an ongoing effort. A well-constructed platform empowers users to create robust workflows while maintaining compliance and improving efficiency. Following these steps lays the groundwork for a system that evolves with organizational needs while consistently delivering value.

Automating Control Processes Using Language Models

Many industries rely on control processes to ensure operational accuracy, maintain quality, and comply with regulations. Common examples of these processes include deviation control, quality control, compliance checks, fraud detection, and documentation control. These checks often happen at different stages, such as pre-controls, post-controls, or through mapping workflows. Traditionally, these processes have been done manually, which can be time-consuming and prone to errors.

Language models offer a new way to automate control processes without needing to specify or code every detail explicitly. Instead of relying on predefined rules, language models work by identifying patterns. This makes them effective at detecting deviations or irregularities on their own. Specialized versions of these models can be fine-tuned to focus on specific tasks, such as fraud detection or anomaly identification, making them powerful tools for modern automation.

To automate control processes using language models, it’s helpful to take a step-by-step approach. First, identify what needs to be controlled, what data is required, and where this data resides in systems and processes. This involves close collaboration with domain experts such as lawyers, engineers, or healthcare professionals, depending on the field. It’s important to focus on areas with high potential for improvement, where automation can have the greatest impact.

Next, determine which control steps and processes are suitable for automation. Processes where there are large data volumes, significant manual effort, or readily available data are often good candidates. Once areas for automation are identified, the next step is to test with a proof of concept. Starting with simple examples in a secure sandbox environment helps validate the model’s capabilities. Testing different language models is essential to finding the best fit for specific needs.

If the proof of concept shows promise, the next step is to run a limited pilot program. A subset of real-world data can be used to experiment with automated controls while comparing different approaches. The results should be carefully analyzed to assess whether automation delivers measurable improvements. Pilots should function as separate processes to avoid disrupting ongoing workflows while testing scalability and reliability.

When automated controls prove valuable in pilot testing, the final step is scaling up for full production. Successful solutions can be integrated into live systems to streamline workflows and handle larger data volumes. Monitoring and refinement are critical during this stage to ensure continued effectiveness and adaptability.

While automating control processes offers significant advantages, practical challenges need to be addressed. Collaboration with subject matter experts ensures that automation captures all critical requirements. Reliable, accurate datasets are key to achieving good results. Additionally, building trust among stakeholders is crucial to gaining buy-in and ensuring that automated controls are accepted. Finally, successful implementation relies on starting small, testing thoroughly, and scaling gradually.

The potential for automating control processes with language models is immense. By reducing manual workload and improving accuracy, organizations can increase efficiency and build smarter workflows. Starting with smaller tests and scaling gradually provides a clear path to unlocking these benefits while maintaining quality and compliance.

Give me a problem

“What’s your biggest problem right now?” This question can be one of the most powerful tools for selling, whether it’s yourself, a technology, or a service. The idea is simple: by directly addressing a real and concrete issue the recipient is struggling with, you immediately demonstrate your value, engage them emotionally, and create a productive dialogue.

The key is to work with an actual, specific problem the recipient cares about. This isn’t about vague concepts or abstract ambitions—it’s about something tangible and relatable. A real problem resonates because it’s something the audience can logically understand and emotionally feel. It should be specific enough that it can be broken down into actionable details, allowing you to dive into solutions that matter.

Here’s where choosing the right level of detail is crucial. For example, saying, “I want to cure cancer” is too broad and lofty to be actionable. However, if you refine it to, “I want to cure cancer, but I’m struggling with researchers spending too much time writing reports to secure funding,” it becomes a problem you can tackle. Similarly, “The company needs to increase revenue” is too generic, whereas “We need to boost revenue, and several consultants currently don’t have ongoing projects” is grounded and solvable. The same applies in tech: “The development team isn’t delivering all the functionality users want” is abstract, but “The team spends most of their time on non-functional tasks and can’t deliver new features quickly enough” identifies specific challenges and barriers.

Starting with the recipient’s biggest problem allows you to demonstrate your skills in problem-solving, analysis, and creativity. By breaking the problem into smaller components, you can sketch alternatives, evaluate solutions “on paper,” and engage them in a focused conversation about possibilities. It’s not just about providing answers—it’s about encouraging clear thinking and collaboration.

This method has wide-ranging applications. In a job interview, you can show your ability and expertise by addressing the company’s key challenges. In sales, you can frame your product or service as the solution to their major pain points. During demonstrations, you can showcase your capabilities by working with relatable, real-world examples.

At its core, this approach builds credibility and trust. It’s a way to prove your ability to navigate and tackle tough challenges while focusing on what truly matters to the recipient. By connecting with someone’s most pressing issue, you encourage collaboration, deepen the dialogue, and position yourself as someone who delivers results.

So next time you’re pitching yourself, a product, or an idea, try starting with this question: “What’s your biggest problem right now?” It’s a way to cut through distractions and focus on creating real value. Sometimes, the simplest approach is the most impactful.

Exploring Model Context Protocol (MCP) and kontekst.cloud

Model Context Protocol (MCP) is an open protocol designed to standardize how applications connect with language models (LMs). Think of MCP as being similar to a USB-C port, not for hardware, but for AI-driven systems. It provides a structured way for applications to interact efficiently with data sources, workflows, and tools. The three main features of MCP are resources, prompts, and tools. Resources consist of context and data that the user or model can utilize. Prompts are templated messages and workflows that guide interactions. Tools are functions that a language model can execute to complete specific tasks. This standardized approach makes MCP useful for integrating applications in a clear and repeatable way.

The concepts in MCP have noticeable similarities with kontekst.cloud, a platform that organizes systems around the central concept of “context.” Most features in MCP align directly to kontekst.cloud’s terms. Resources in MCP correspond to content in kontekst.cloud. Tools translate to actions, and prompts could align with agents or actions. However, prompts are tricky to define in kontekst.cloud since they are used differently. One suggestion is to treat them purely as templated messages and separate workflows as their own distinct concept. Unlike MCP, kontekst.cloud introduces threads that capture logs and process information, extending beyond the limited technical logging seen in MCP. This ability to store execution histories helps define workflows and track processes in greater detail.

Some challenges exist with terms like “resources” and “data,” as they are too broad and often end up encompassing everything. Kontekst.cloud has made efforts to be more precise by splitting features into content, process data, and actions. The platform uses an endpoint called /data to store all information related to features, but alternatively, /resources could be used. However, the generic nature of these terms still poses some risk of overlap between concepts. Despite this, the flexibility built into kontekst.cloud allows substantial customization, which makes implementing MCP on the platform relatively straightforward.

Kontekst.cloud’s design also enables support for alternative protocols like SOLID or other semantic web technologies. By adding a compatible layer, the platform can easily integrate standards like MCP while retaining the ability to work with other options. This adaptability positions kontekst.cloud as a versatile tool for building interoperable systems. Whether working with structured standards like MCP or experimenting with decentralized architectures supported by protocols like SOLID, kontekst.cloud provides the foundation for highly flexible implementations.

An important distinction between MCP and kontekst.cloud lies in the concept of context itself. In kontekst.cloud, context operates as the central organizing principle and can be seen as the “server” that ties together content, actions, workflows, and threads. MCP lacks this central concept and instead ties resources and tools to individual servers. To bridge this gap, kontekst.cloud could represent each context as its own independent server, assigning a root URL to each. This modular approach enhances scalability and allows workflows to be tied directly to user-specific or application-specific contexts, creating a more personalized experience.

Although MCP excels as a standardized integration protocol, kontekst.cloud takes these concepts further by emphasizing context as the foundation for organizing data and processes. This focus enables richer workflows and simplifies the design of reusable systems. With its ability to support MCP and other protocols, kontekst.cloud isn’t limited by any single system but instead embraces interoperability as a core strength. By combining the standardization provided by MCP with the context-driven modularity of kontekst.cloud, developers can build more scalable and flexible applications tailored to diverse needs.

AI-Powered Blog Writing Agent

In today’s fast-paced digital world, consistently creating high-quality blog content is a challenge. Whether you’re a business, a marketer, or an individual looking to publish thought-provoking articles, writing takes time, effort, and creativity.

Our AI-powered blog writing agent is designed to automate, enhance, and optimize content creation—from ideation to publication—so you can focus on what truly matters: sharing valuable insights with your audience.

Key Features

Basic Capabilities

Our AI agent simplifies blog creation with intelligent automation:

Effortless Content Generation – Provide an idea, rough notes, an example post, a concept, or a vision, and the AI will generate a structured, well-written blog post. It can even include relevant images.
Flexible Writing Styles & Formats – Choose from different styles, including short teasers, long-form articles, press releases, visionary thought pieces, or technical breakdowns.
Smart Meta-Tagging – Automatically generates relevant tags, categories, and metadata to optimize searchability and SEO performance.
Seamless Publication – Offers full or semi-automated publishing, so you can review and approve or let the AI handle the entire workflow.

Advanced Capabilities

For those looking for more control, variation, and optimization, our AI offers powerful content evaluation and management tools:

Multi-Proposal Generation & Evaluation – Instead of producing just one draft, the AI creates multiple versions of a post and evaluates them, allowing you to select the best for publication.
Diverse Input Pipelines – Generate completely different blog posts around multiple topics and ideas, providing a variety of content options. This ensures you always have fresh and diverse articles to choose from, making it easier to maintain a dynamic publishing strategy.
Authoring Process Archive – Keep a detailed record of revisions and iterations for each post, enabling easy tracking of changes, content strategy insights, and future repurposing.
Smart Trash-Can Feature – Discarded posts aren’t lost—they go into a special archive where they can be analyzed for trends, revisited for future use, or evaluated for performance improvements over time.

Why Use This AI Agent?

With this AI-powered writing tool, content creators can:
✔ Save time and effort on writing and editing
✔ Generate multiple blog post options across different topics
✔ Improve content quality through AI-driven evaluation and selection
✔ Maintain full control over the publishing process

Whether you’re a business scaling content marketing, a tech writer streamlining production, or a visionary sharing new ideas, this AI agent enhances creativity, productivity, and content quality like never before.

Ready to Revolutionize Your Content Creation?

Try our AI-powered blog writing agent today and experience faster, smarter, and more effective content generation. 🚀

Design Alternatives for Using Path, HTTP Methods, and Actions

When designing an API, choosing how to structure endpoints and model the interaction between client and server is a critical design decision. The three alternatives outlined – data-driven, object-oriented, and action/process-driven – represent different approaches with distinct strengths and weaknesses. The choice of approach should be based on both technical and business needs, as well as user expectations and workflows.

1. Data-Driven Approach

Description

This approach focuses on data as the primary entity in the API. Clients perceive the API as a system for storing and retrieving data, without directly interacting with actions or processes. Business logic and processing happen invisibly on the backend, and clients only see the results through the data produced.

Characteristics

Clear separation between data and processes.
Clients interact only with resources (e.g., submissions) and their lifecycle.
Process statuses are represented as fields in the data.
Resembles a CRUD (Create, Read, Update, Delete) approach.

Advantages

Simple for clients – they only retrieve and store data without needing to understand domain logic.
Fewer endpoints with a consistent URL structure.
Well-aligned with REST principles.

Disadvantages

Business logic can be difficult for clients to understand and discover.
Risk of logic being spread across clients if the API does not provide enough guidance.
Less suitable for complex processes involving multiple steps or data types.

Example

GET /data/submission
GET /data/submission/1199930
POST /data/submission
PATCH /data/submission/1199930

When is this approach suitable?

For simple systems where processes are not highly complex.
When clients primarily work directly with data (e.g., case handlers).
When minimal coupling between clients and domain-specific business logic is desired.

2. Object-Oriented Approach

Description

In this approach, each resource is treated as an object that has both data and associated operations (methods). Clients can not only retrieve and update data but also trigger specific actions on each resource. This makes business logic more explicit in the API.

Characteristics

Each resource has its own set of operations/actions.
Clients must understand domain-specific concepts and processes.
The approach resembles object-oriented systems, where objects have methods.

Advantages

Clearer process support – clients receive explicit signals about available actions.
Easier for clients to navigate business logic.
Well-suited for resources with many specific actions governed by business rules.

Disadvantages

Can lead to an explosion of endpoints when multiple resources have multiple actions.
Maintaining a consistent structure across various object types can be challenging.
Can become cumbersome if many actions are not resource-specific but apply across multiple resources.

Example

POST /data/submission/search
POST /data/submission/submit
POST /data/submission/1199930/selectPractice
POST /data/submission/1199930/cancel

When is this approach suitable?

When resources have specific, business-related operations.
When it is important for clients to understand the processes around resources.
When the API is part of a larger domain application with domain-oriented users.

3. Action/Process-Driven Approach

Description

This approach explicitly separates actions from data. Clients retrieve and manage data in one way, while business processes and operations are modeled as separate process resources or services. This allows actions to involve multiple data types simultaneously and handle more complex workflows.

Characteristics

Clear distinction between data and processes.
Processes have dedicated endpoints that handle multiple resources and complex logic.
Suitable for larger, cross-cutting processes.
Often inspired by Command-Query Responsibility Segregation (CQRS).

Advantages

High flexibility in modeling business logic.
Easier to version or modify process logic without changing data models.
Well-suited for systems with complex, multi-step workflows.

Disadvantages

Can create uncertainty about which data the processes operate on.
Requires more documentation and client adaptation.
May result in an artificial separation of data access and process handling, even when logically connected.

Example

POST /process/submitReimbursementClaim
POST /process/updateReimbursementClaim
POST /search

When is this approach suitable?

When processes involve multiple different data types.
When processes have high complexity and multiple steps.
When processes should function as “black box” operations with clear input and output.
When supporting both manual and automated workflows via the same interface.

Summary Evaluation

Approach	Client Simplicity	Flexibility	Process Support	Suitable for Complex Domains
Data-Driven	✅ Very simple	❌ Limited	❌ Weak	❌ Not well-suited
Object-Oriented	⚠️ Moderate	⚠️ Moderate	✅ Good	⚠️ Partially suitable
Action/Process-Driven	⚠️ Requires learning	✅ High	✅ Very good	✅ Highly suitable

Recommendation

Choosing an approach should be based on:

The complexity of the domain.
How self-sufficient clients need to be.
How clearly processes need to be defined for clients.
Whether the API is primarily a CRUD interface or a process-driven system.

In many cases, a hybrid model may be the best solution, where basic data is managed using a data-driven approach, while more complex workflows are exposed via process-driven endpoints. This provides both simple data handling and flexible process support.