# Predli

> Predli is your partner for organisational AI adoption - from use-case discovery to deployed enterprise AI solutions.

Predli helps organisations turn the latest AI technologies into a real competitive advantage, adapted to their business, data and goals. We deliver enterprise AI agents and the Predli Company Brain platform, along with the team and expertise to make adoption succeed.

The complete text of every page and blog post is available at [/llms-full.txt](https://predli.com/llms-full.txt) for AI ingestion in a single fetch.
Individual blog posts are also available as markdown at https://predli.com/blog/{slug}.md.

## Pages

- [Home](https://predli.com/): Overview of Predli and our approach to AI adoption.
- [Solutions](https://predli.com/solutions): Enterprise AI agents and the Company Brain platform.
- [Customer Stories](https://predli.com/customer-stories): How Tyréns, Vectr Fintech Partners and Spotlight Group use Predli.
- [Portfolio](https://predli.com/portfolio): Selected AI adoption and transformation projects.
- [Blog](https://predli.com/blog): News, research and insights on enterprise AI.
- [About](https://predli.com/about): The team and mission behind Predli.
- [Careers](https://predli.com/careers): Open roles at Predli.
- [Contact](https://predli.com/contact): Get in touch or book a call.

## Blog

- [A new approach to event-driven forecasting](https://predli.com/blog/a-new-approach-to-event-driven-forecasting.md): A practical look at where large language models add value to time series forecasting - and where they do not. We share findings from a Lund University × Predli master’s thesis on event-driven prediction with agentic LLM orchestration.
- [Claude Mythos Preview](https://predli.com/blog/claude-mythos-preview-what-it-actually-signals.md): Anthropic recently released a new model and decided not to make it publicly available - a voluntary call that goes beyond what their own safety policy requires. The model can autonomously find and exploit security vulnerabilities that have survived decades of human review. We break down what it actually does and what it signals.
- [WebMCP Doesn’t Look Revolutionary. That’s Why It Might Be.](https://predli.com/blog/webmcp-doesnt-look-revolutionary-thats-why-it-might-be.md): Google quietly introduced an early preview of WebMCP in Chrome Canary - a capability that lets websites expose structured actions to AI agents. After testing it, it becomes clear that the interface isn’t the story. The shift is happening underneath, in how capabilities are exposed and executed.
- [Clawdbots in the Enterprise: Opportunity, Risk, and the Shift from Answers to Action](https://predli.com/blog/clawdbots-in-the-enterprise-opportunity-risk-and-the-shift-from-answers-to-action.md): As autonomous, tool-using AI agents gain traction in enterprise environments, organizations are shifting from asking AI for insights to delegating real work. This article explores where agentic systems create real enterprise value, and what teams must design for before letting them interact with production data.
- [LLM Deep Dive: Kimi K2.5](https://predli.com/blog/llm-deep-dive-kimi-k2-5.md): As large language models continue to evolve, the focus is shifting from making individual systems smarter to making them work better together. We explore Moonshot AI’s Kimi K2.5 and its approach to large-scale coordination through native agent swarms.
- [From Adoption to Impact: What Anthropic’s 2026 Economic Index Really Signals](https://predli.com/blog/from-adoption-to-impact-what-anthropics-2026-economic-index-really-signals.md): Anthropic’s latest Economic Index shows that AI’s real impact is no longer about adoption, but about execution. This analysis examines what the data reveals about productivity, reliability, and why organizational design now matters more than access to models.
- [Introducing Our New Intelligent Database Agent](https://predli.com/blog/introducing-our-new-intelligent-database-agent.md): Modern organizations don’t struggle with a lack of data - they struggle with turning it into fast, reliable insights. Our new Database Agent replaces simple text-to-SQL with automated analytical reasoning, reducing manual workflows while preserving transparency and control.
- [Predli’s AI Outlook for 2026](https://predli.com/blog/predlis-ai-outlook-for-2026.md): What will define AI in 2026? AI has moved from experimentation to operations. In this outlook, we examine the signals already shaping what comes next - from agents entering real workflows to open-weight models and AI-driven advances in science.
- [Looking Back at 2025: How the AI Year Unfolded](https://predli.com/blog/looking-back-at-2025-how-the-ai-year-unfolded.md): As 2025 comes to an end, we revisited the predictions we made a year ago. Some shifts accelerated faster than expected, like the rise of practical agents and deeper OS-level AI, while others surfaced new challenges around regulation, energy use and security.
- [Beyond Scale: Why Asynchronous Reasoning Signals a New Era of AI Architecture](https://predli.com/blog/beyond-scale-why-asynchronous-reasoning-signals-a-new-era-of-ai-architecture.md): Microsoft recently published new research on asynchronous reasoning, introducing a model-level structure that moves beyond traditional linear chains of thought. This article breaks down what the shift means and why it aligns with the emerging agentic architectures.
- [Inside H-MAC: Building a Hierarchical Multi-Agent Reasoning Architecture](https://predli.com/blog/inside-h-mac-building-a-hierarchical-multi-agent-reasoning-architecture.md): Most AI systems react to prompts, but few can reason in a structured and transparent way. H-MAC coordinates multiple specialized agents through planned, adaptive workflows - transforming AI from reactive problem solving to scalable, explainable reasoning inside Predli Studio.
- [AI Cost Optimization: Token Tariffs and the Case for Custom Tokenizers](https://predli.com/blog/cost-optimization-token-tariffs-and-the-case-for-custom-tokenizers.md): Every word processed by an LLM comes with a measurable cost - the token. This article examines how token-based pricing creates hidden inefficiencies across languages, and how organisations can reduce costs through smarter prompt design, model routing, and custom tokenization.
- [A Self-Generated Overview of AI and MCP Capabilities](https://predli.com/blog/a-self-generated-overview-of-ai-and-mcp-capabilities.md): What happens when you ask an AI platform to describe itself? This article was entirely generated by Predli Studio, exploring how it uses Model Context Protocols (MCPs) to power human-centered AI for enterprise knowledge work.
- [One Year of Agentic AI: Lessons from Predli & McKinsey](https://predli.com/blog/one-year-of-agentic-ai-lessons-from-predli-mckinsey.md): What began as experimental demos has become a new frontier in enterprise AI. Together with McKinsey’s findings, we reflect on the hard-earned lessons from building agents that move beyond promise to real, measurable performance.
- [What OpenAI and Anthropic’s Usage Reports Really Tell Us](https://predli.com/blog/what-openai-and-anthropics-usage-reports-really-tell-us.md): AI is spreading faster than any technology in history - yet trust remains fragile. Wealthy nations lead today, but growth is steepest in emerging markets. The future of AI won’t just depend on what it can do, but on how we choose to use and govern it.
- [Why 95% of GenAI Pilots Fail - And How to Beat the Odds](https://predli.com/blog/why-95-of-genai-pilots-fail.md): Billions are being invested in generative AI pilots, but most never escape “AI purgatory.” The real struggle isn’t the technology itself - it’s scaling, adoption, and trust. We look at why so many initiatives stall and what it takes to turn AI into a real business advantage.
- [SEO for Generative AI: Why llms.txt is the New robots.txt](https://predli.com/blog/seo-for-generative-ai-why-llms-txt-is-the-new-robots-txt.md): As search moves from links to AI-generated answers, websites face a new challenge: being readable by large language models. llms.txt introduces a lightweight, markdown-based standard to ensure content is efficiently parsed and surfaced in generative search.
- [MCP: The Next Leap in AI Integration](https://predli.com/blog/mcp-the-next-leap-in-ai-integration.md): For the past decade, APIs have powered digital transformation. With Model Context Protocol (MCP), AI agents are no longer just consumers of data - they’re active participants in business workflows. For companies, this is a strategic opportunity and a risk for those who fall behind.
- [Agent Evaluation: Strategic AI Advantage](https://predli.com/blog/agent-evaluation-strategic-ai-advantage.md): As companies integrate AI-powered agents into customer service, agent evaluation becomes crucial. It’s more than technical - it’s about protecting revenue, brand trust, and ensuring security at scale.
- [Predli Studio: Secure, Tailored AI for Your Business](https://predli.com/blog/predli-studio-secure-tailored-ai-for-your-business.md): Everyone’s talking about generative AI, but how do you actually make it work in a real organization, with real data, real workflows, and real constraints? This post introduces Predli Studio and how it stacks up against Microsoft Copilot.
- [RAG Series: Making Sense of Internal Data With GraphRAG](https://predli.com/blog/rag-series-making-sense-of-internal-data-with-graphrag.md): GraphRAG takes RAG systems to the next level by structuring internal data as a knowledge graph. Instead of retrieving isolated text snippets, it builds context - and uncovers insights that traditional RAG approaches often miss.
- [RAG Series: GraphRAG](https://predli.com/blog/rag-series-graphrag.md): Discover how GraphRAG reimagines Retrieval-Augmented Generation by combining LLMs with structured knowledge graphs - offering contextually rich and insightful solutions for complex data relationships.
- [Fine-Tuning Series: On-Device LLMs - How Google Leads and Why Apple Should Follow](https://predli.com/blog/fine-tuning-series-on-device-llms---how-google-leads-and-why-apple-should-follow.md): AI is moving from the cloud to smartphones, with on-device LLMs unlocking faster, more private, and cost-efficient applications. With LoRA fine-tuning, developers can customize AI without massive compute costs.
- [Fine-tuning series: Intro](https://predli.com/blog/fine-tuning-series-intro.md): Fine-tuning allows businesses to adapt LLMs to specific needs, improving accuracy, efficiency, and consistency without full-scale training. Techniques like LoRA have made this process more accessible and cost-effective.
- [Agentic Workflows and Prompt Optimization](https://predli.com/blog/agentic-workflows-and-prompt-optimization.md): Agentic workflows enhance AI capabilities by integrating reasoning, decision-making, and tool usage. LangGraph enables structured multi-agent interactions - and well-designed prompts significantly impact accuracy and workflow execution.
- [DeepSeek R1: o1’s Open-Source Rival](https://predli.com/blog/deepseek-r1-o1s-open-source-rival.md): The spotlight in AI is shifting from generative models to reasoning models with human-like thinking and greater accountability. Enter DeepSeek R1 - a bold, open-source rival with a 128K context length that’s redefining accessibility in advanced AI.
- [The Future of AI: Predictions for 2025 and Beyond](https://predli.com/blog/the-future-of-ai-predictions-for-2025-and-beyond.md): What will define AI in 2025? From specialized agents transforming workflows to nuclear power driving sustainable growth, we see exciting opportunities - but also risks like misinformation, security threats, and debates over data ownership.
- [Apple Intelligence: First Look at New Features](https://predli.com/blog/apple-intelligence-first-look-at-new-features.md): Apple’s latest AI initiative introduces tools aimed at boosting creativity and productivity, including Writing Tools and a more capable Siri. Is this the beginning of a transformative journey, or just an incremental step?
- [AI Commission’s Roadmap for Sweden](https://predli.com/blog/ai-commissions-roadmap-for-sweden.md): The AI Commission’s Roadmap for Sweden aims to elevate Sweden’s AI rank from 25th to the top 10 with initiatives like democratizing AI, fostering collaboration, advancing PETs, and establishing an EU AI Factory.
- [How to choose the right LLM for your use-case](https://predli.com/blog/how-to-choose-the-right-llm-for-your-use-case.md): Choosing between convenient proprietary or customizable open-source LLMs involves balancing rapid prototyping against long-term costs and data security. The optimal approach depends on use case breadth and security needs.
- [Using the Language Powerhouse for Effective Content Generation](https://predli.com/blog/using-the-language-powerhouse-for-effective-content-generation.md): Our team explored using LLMs like GPT-3.5 for controlled content generation from seed data, designing prompts and evaluation methods to quantify quality. LLMs possess great potential but need guidance.
- [Future of Manufacturing](https://predli.com/blog/future-of-manufacturing.md): While we see manufacturers fiddling with AI and machine learning, Industry 4.0 is still a moonshot for many. Too many companies are stuck in the “pilot purgatory” phase - and we explore why.
- [Beyond Traditional Automation: The Rise of Agentic AI Workflows](https://predli.com/blog/the-rise-of-agentic-ai-workflows.md): Agentic AI enables autonomous workflows that adapt in real time, transforming business processes by reducing human intervention in routine tasks - underscoring AI’s potential in driving efficiency and real-time adaptability.
- [LLM Deep-dive: Solar 10.7B](https://predli.com/blog/llm-deep-dive-solar-10-7b.md): SOLAR 10.7B blends Llama 2’s architecture with Mistral 7B’s weights for unparalleled performance - marking a new industry benchmark and South Korea’s rising prominence in AI.
- [Predli and the AI for Good Foundation Partner to Advance UN’s 2030 Agenda](https://predli.com/blog/predli-and-ai-for-good-partnership.md): Predli announced a collaboration with the AI for Good Foundation to accelerate work on the UN Sustainable Development Goals and address the most pressing challenges faced by our communities.
- [LLM Deep-dive: Llama 3.1](https://predli.com/blog/llm-deep-dive-llama-3-1.md): Meta’s Llama 3.1 release, including the powerful 405B model, sets a new standard for open-source LLMs, rivaling proprietary models like GPT-4o and Claude 3.5 Sonnet - highlighting the growing impact of open-source AI.
- [RAG series: ARAGOG](https://predli.com/blog/rag-series-aragog.md): Summary of our paper ARAGOG: Advanced RAG Output Grading.
- [RAG series: Two types of chunks](https://predli.com/blog/rag-series-two-types-of-chunks.md): By decoupling retrieval and synthesis, and introducing Sentence-window Retriever, Auto-merging Retrieval, and Document Summary, we significantly improve the LLM’s ability to generate precise, contextually rich responses.
- [RAG series: Query Expansion](https://predli.com/blog/rag-series-query-expansion.md): Query expansion techniques such as Hypothetical Answer and Multi-Query enhance LLM performance by facilitating more relevant and accurate information retrieval - pushing the boundaries of what’s possible with LLMs.
- [RAG series: Intro](https://predli.com/blog/rag-series-intro.md): The true value of RAG lies in its ability to grant LLMs access to previously unseen internal datasets, pivotal for organizations that need to utilize their proprietary data for enhanced decision-making.
- [LLM Deep-dive: Gemini](https://predli.com/blog/llm-deep-dive-gemini.md): Google’s Gemini model, with its advanced multimodal capabilities, is a noteworthy event in the LLM landscape. Its true standing, particularly compared to GPT-4, will hinge on unbiased, independent validation.
- [Working with Sensitive Data and LLMs](https://predli.com/blog/working-with-sensitive-data-and-llms.md): The synergy between sensitive data and LLMs marks a significant step forward in healthcare and finance. We explore three approaches to combine LLMs with sensitive data, while protecting data integrity.
- [LLM Deep-dive: Phi-2](https://predli.com/blog/llm-deep-dive-phi-2.md): Microsoft’s Phi-2 challenges the notion that bigger models always equate to better performance. With 2.7B parameters, it rivals much larger models - underscoring the power of meticulously curated training data.
- [LLM Deep-dive: Mixtral 8x7b](https://predli.com/blog/llm-deep-dive-mixtral-8x7b.md): Mixtral 8x7b’s unique Mixture of Experts architecture offers a blend of efficiency and capability that challenges even the best open-source LLMs - hinting at a future where powerful AI tools are more accessible.
- [Supercharge how you interact with proprietary documents with LiQA](https://predli.com/blog/supercharge-how-you-interact-with-proprietary-documents-with-liqa.md): LiQA leverages AI to transform enterprise document search. Proprietary files are ingested, converted to vectors, and indexed for personalized QA - unlocking the potential of your organization’s documents.