The Different Forms of AI
AI takes many forms, depending on your project's needs.
AI Chatbots (LLM)
Great for answering questions and engaging users.
AI Experts (LLM + RAG)
Provide expert-level insights and knowledge from your data.
AI Agents (LLM + RAG + Tools)
Perform tasks autonomously by integrating external tools and systems.
AI Innovators (LLM + RAG + Tools + Time)
Improve over time and perform long-running tasks for research and development.
AI-first Organizations (LLM + RAG + Tools + Time + Teams)
Fully integrated teams of humans and AI working seamlessly together for maximum efficiency.
AI Use Cases for Your Organization
Explore some ways AI can revolutionize your workflows. Choose below to customize your own scenario to discuss your ideas.
Best Practices Building with AI
In the past, programmers wrote low-level instructions directly by hand. Today, higher-level programming languages allow computers to automatically generate these detailed instructions. Eventually, we may see AI-generated code evolve similarly — producing entire languages optimized for machine efficiency rather than human readability. However, we are currently in a transitional stage where human oversight remains essential.
Recently, a software development practice called "Vibe Coding" has emerged, in which developers let AI generate unmonitored code (often without even understanding the results), relying entirely on intuition rather than careful engineering. Throughout late 2024, we extensively experimented with Vibe Coding and arrived at the following conclusions:
AI "Vibe Coded" Apps
- Built quickly by unmonitored AI
- Overly complex with no solid foundation
- Difficult to debug and maintain
- High risk of unintended side effects
Human-Guided AI Apps
- Built by humans using AI power tools
- Solid foundation and well-architected
- Human-readable for better maintainability
- Validated from real-world experience
Flexible Engagement Model
At CodeDrifters, innovation is part of our DNA. To build faster and smarter, we built KanBot, our open-source AI coding agent, to help work alongside our team to deliver high-quality work, faster.
Unlike SaaS companies, with CodeDrifters:
- You own everything:No hidden licenses or subscriptions. You fully own the code, data, and IP we deliver.
- Flexible Pricing & Engagement:Month-to-month fractional hiring — only pay for what you need, scaling easily up or down.
- Transparent Development:Our use of open AI coding tools makes your project faster, more transparent, and affordable.
Our AI Capabilities
We help enterprises turn AI's promise into real-world impact through thoughtful solutions tailored to your goals.
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AI-powered App Development
From concept to launch, we integrate advanced AI tooling directly into your software projects, delivering intelligent functionality, efficiency, and speed.
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Custom AI Agents & Chatbots
We create tailored AI solutions that amplify productivity, automate routine tasks, and improve customer experiences.
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AI Integration Services
We offer integration services for Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), and APIs to streamline workflows.
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AI-focused Design & Prototyping
Our human-centered design services prototype AI-driven experiences, ensuring interactions are intuitive and aligned with your users' needs.
AI Gives Us “Power Tools” to Do More Than Ever Before
Traditional Software Development
- Limited speed & productivity
- Fewer projects delivered
- Creativity limited by feasibility
AI-powered Software Development
- Rapid productivity & efficiency
- Increased scale with high quality
- Greater creative freedom
Understanding AI Application Tech Stacks
Below, we break down various AI solutions — everything from simple chatbots to sophisticated AI-first organizations — by distinct technology layers, helping you clearly understand the components involved and precisely how, for example, an AI agent differs from a chatbot.
| AI Chatbot | AI Expert | AI Agent | AI Innovator | AI-first Org | |
|---|---|---|---|---|---|
| User Layer | Single User | Single User | Single User | Single User | Team Users |
| Interface Layer | Chat | Search | Widget | App | Platform |
| Time Layer | < 1 Second | < 3 Seconds | < 5 Minutes | 30-60 Minutes | Continuous |
| Tool Layer | ✗ | ✗ | MCP | MCP | MCP |
| Context Layer | ✗ | RAG | RAG | RAG | RAG |
| Model Layer | Single LLM | Single LLM | Multi-LLM | Multi-modal | Multi-modal |
From Ideation to Launch - AI Development Options
Choosing the right AI development approach sets the foundation for your project's long-term success. Here are a few options we recommend:
Click-thru Prototypes
(no functional code)Ideal for proofs of concept, to design ideas and illustrate user experiences but no actual functionality.
Human-AI Collaboration
(production-ready code)Clear, concise, code that is well-architected and built for reliability, efficiency, and performance.
AI-coded Prototypes
(prototype code)Rapidly generated and fully functional code, useful to show proofs of concept beyond initial click-thru prototypes.
Understanding Security in AI Applications
AI applications offer powerful capabilities, but they also introduce unique security challenges. Below we highlight eight common risks and practical strategies for mitigating them.
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Prompt Injection Attacks
Risk: Malicious inputs trick AI into unintended actions or data leaks.
Mitigation: Implement strict input validation, sanitization, and context management.
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Tool Poisoning
Risk: Hidden malicious instructions within AI tool metadata leading to data theft.
Mitigation: Regularly audit and sanitize tool metadata; verify tool integrity.
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Context Leakage
Risk: Unintentional exposure of sensitive information through AI inputs.
Mitigation: Use data masking, encryption, and strict context control measures.
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Token Theft
Risk: Unauthorized access to AI tokens, API access keys and secrets.
Mitigation: Enforce secure authentication practices and regular key rotations.
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Trojan Horse Injection
Risk: Malicious code embedded in legitimate AI applications causing unauthorized access.
Mitigation: Implement continuous code reviews, integrity checks, and trusted software sources.
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Dependency Exploits
Risk: Vulnerabilities in third-party libraries and dependencies used in AI systems.
Mitigation: Regular dependency scanning, updates, and security patch management.
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Broad Permission Abuse
Risk: AI agents with excessive permissions facilitate unauthorized actions.
Mitigation: Apply the principle of least privilege, granting minimal necessary access.
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Cross-Server Attacks
Risk: Malicious servers intercept or manipulate legitimate communications.
Mitigation: Employ strong server authentication, session management, and network segmentation.
Large Language Models (LLM) are the core models that power text-based AI applications.
Retrieval Augmented Generation (RAG) connects LLMs to external data sources to improve accuracy.
Model Context Protocol (MCP) standardizes interactions between AI models and enterprise tools
AI-first organizations enhance workers by freeing up time to focus on things that people are good at (and love doing) and let AI focus on things that AI is good at (and people hate doing).