Essential
Required for the site to work. Always on.
Born for the AI era
Thinkia operates as an Artificial Intelligence Engine within your business, activating, orchestrating, and scaling intelligence to turn knowledge into real-time business action.
The Mesh. Layers of modular intelligence.
Transformation doesn't happen in silos. It begins with an idea, takes shape through experience, grows with technology, is powered by data, accelerates through automation, and evolves with intelligence.
At the core of everything we do, there's a simple belief: AI is more than a feature — it's the Engine. That's why our solutions move as one, even when they start in different places.
Imagine what's next.
Experiment, innovate, and build with generative intelligence at the core.
Turn data into direction.
Make smart decisions with advanced analytics and a real understanding.
Experience as a driver.
Design digital products that connect, emotionally and functionally.
Build what scales.
Create modular platforms and software that grow with your vision.
Automate what slows you down.
Streamline work with intelligent processes and AI-powered decisioning.
Built for production.
The systems companies actually put into production—agents, enterprise knowledge, and automation platforms designed to operate inside real organisations, not pilots that stall after the demo.
Strategic alliances that power our solutions
What we're thinking about right now
- Innovation Insights New
Open-Source AI Regulation: Mapping the Scenarios for Enterprise Leaders
The debate over open-source AI regulation is heating up, with major tech players lobbying against restrictions. We explore the potential scenarios for future AI policy and what enterprise leaders must watch to navigate the shifting landscape of open-weight models and AI governance.
- Platforms & Engineering New
Prompt Engineering: The Key to Performance in Local LLMs
New research confirms that advanced prompt engineering is critical for unlocking the value of smaller, locally-run LLMs. For enterprises seeking cost and privacy benefits, optimizing prompts is no longer optional—it's a core competency for AI performance and ROI.
- Platforms & Engineering New
Mixture-of-Experts Models: The Enterprise Fix for AI Latency and Cost
New research on Mixture-of-Experts models offers a breakthrough in AI inference optimization. By prefetching model components, techniques like SpecPrefetch reduce model latency and operational costs, making frontier AI practical for real-time enterprise applications.
- Automation New
Agentic Workflows: Why Smart Process Beats Bigger Models in Enterprise AI
The race for AI dominance isn't about model size. New research shows that agentic workflows with small language models can outperform frontier models for specialized tasks. This shift in AI strategy allows enterprises to build more cost-effective and accurate solutions. Learn why workflow intelligence is the new competitive advantage.
- Platforms & Engineering New
AI Safety Verification: Why Ad-Hoc Testing Is No Longer Enough
Recent AI failures show that current safety practices are falling short. Learn why formal AI safety verification, a discipline from safety-critical engineering, is now essential for enterprise adoption. We explore how to improve AI agent reliability and manage risk with structured model validation.
- Platforms & Engineering New
Deterministic AI: The Key to Enterprise-Grade Agent Reliability
Probabilistic LLMs are a barrier to enterprise adoption. We explore how deterministic AI architectures, like the one proposed in the new Phionyx paper, provide the reliability and pre-response governance needed for high-stakes applications. Learn how to build auditable AI agent safety by design.
- Platforms & Engineering
Open-Source AI Models: A CIO's Guide to the Jagged Frontier
The latest open-source AI models like Kimi K3 are closing the capability gap, but their 'jagged' or uneven performance creates new risks. This brief outlines an enterprise AI strategy for navigating the choice between open-source control and closed-source consistency, emphasizing the need for rigorous, use-case-specific evaluation.
- Platforms & Engineering
AI for Code Generation: The Hidden 90% Performance Gap
New research reveals a critical flaw in AI for code generation: while models produce syntactically correct code, it often runs 90% slower than optimized human code. This analysis explores the performance gap in high-performance computing and outlines a hybrid AI co-pilot strategy for enterprises to avoid hidden technical debt and maximize hardware ROI.