Iris.ai

Your CTO worries about hallucinations. Your CFO worries about ROI. Same AI initiative, two different languages, one shared problem underneath: the data the model is reading. When the data going into a model is fragmented, unstructured, or inaccessible, the results are unreliable. A better model will not fix a broken foundation. A unified data layer resolves both technical and financial concerns. The structural path to success includes:  ↳ Assigning a single owner with authority over the data layer. ↳ Aligning technical accuracy with financial payback metrics. ↳ Fixing the data foundation before deploying the application. In one regulated telecom deployment, this approach took knowledge work accuracy from 80% to 97% on the same model class. The variable was not the model. It was the context underneath. #EnterpriseAI #DataStrategy #AILeadership #IrisAI

It’s always better when we’re together 💙 And even better when our interns join us in Sofia! This Wednesday, we got together at our Sofia office for a special team visit from our CSO & Co-founder, Anita Schjøll Abildgaard, and our CMO, Liana Hakobyan. Joining them was Katerina Dimitrova, our Research Intern, who came all the way from Penn State University in the US to spend the day with us in Sofia. And yes, she brought us a Penn State magnet to mark the occasion. Great conversations, quality time together, and another reminder that even when our team is spread across the globe, it’s always good to be in the same room. #TeamBuilding #DeepTech #SofiaTech #TeamCulture

Some teams bond over coffee. We apparently prefer mountains, waterfalls… and unexpected wildlife encounters. 🏔️🐻🐎 This weekend, we traded our screens for fresh mountain air and headed into the Bulgarian mountains for a well-deserved team getaway. Some of our more adventurous colleagues took on the hike to Rayskoto Praskalo - the highest permanent waterfall in Bulgaria. And as if the views weren’t memorable enough, the trail came with a few surprises: an encounter with a bear (thankfully, a story everyone came back to tell 😅) and a meeting with wild horses along the way. Because sometimes the best team building doesn’t need an agenda - just good people, a mountain, and a little adventure. 💚 Back to work with recharged batteries, stronger connections, and definitely a few stories we’ll be telling for a while. #TeamGetaway #TeamBuilding #MountainAdventure #RayskotoPraskalo #Bulgaria #TeamSpirit #LifeAtWork

You cannot build a stable enterprise AI strategy on a crumbling data foundation. Look at how most regulated organizations attempt to deploy AI. They spend millions design-thinking the top floors of the skyscraper: the overarching AI Strategy, the flashiest GenAI Use Cases, and the executive Dashboards & Insights. But look beneath the surface. If your core data Definitions are broken, your Data Quality is unmanaged, your Metadata is completely siloed, and your data Lineage is untraceable, the entire corporate structure will tilt and collapse under the weight of the first regulatory audit. Mainstream AI architecture forces teams to build backwards. They treat data governance as a post-launch patch rather than the structural concrete holding up the model. When your underlying foundation is failing, picking a better LLM or adding complex orchestration loops won't save the project. It just accelerates the collapse. We investigated the specific architectural failures that cause enterprise AI initiatives to stall out completely before reaching full production. The structural answers, the implementation sequence, and the hard compliance data are all laid out in our latest playbook. Read the full blog post here ➔ https://lnkd.in/enhVvB-R #EnterpriseAI #DataGovernance #AIArchitecture #RegulatedAI #Irisai

Your AI model just went dark. Now what? Earlier this month, a government export directive forced a state-of-the-art LLM offline globally within hours. It wasn't a cloud outage, and it wasn't a product failure. It was a live demonstration of a critical architectural vulnerability: AI model availability is fundamentally exogenous.  Tomorrow, the disruption could be a deprecation notice, an unexpected price hike, or a border dispute. None of it is yours to control. For enterprises that hardwired their RAG pipelines, chunking logic, and prompts to that single model, the disruption meant a massive re-engineering effort.  Treating a hosted model as a hard dependency is a single point of failure and in regulated industries, that is an unacceptable risk. At @Iris.ai, we build enterprise AI differently.  By decoupling your knowledge and context layer from the underlying LLM, the model becomes a swappable component rather than a load-bearing wall. If a model is restricted, our vendor-neutral architecture combined with continuous evaluation allows you to replace the model layer in less than two weeks, without sacrificing contextual accuracy. You cannot control whether your AI model stays available. Your architecture decides what that costs you. Read our latest technical briefing on navigating AI model continuity risk and how to build a resilient foundation for your enterprise. Link to the blog: https://hubs.ly/Q04schLT0 #EnterpriseAI #AISovereignty #TechLeadership #DataGovernance #AIArchitecture #IrisAI

Stop building your AI architecture backwards. If your engineering team is picking an LLM first and treating data ingestion as an afterthought, your pipeline is already compromised. Violently chunking documents into arbitrary token lengths destroys semantic context.  And building complex, multi-agent reasoning loops on top of context-stripped data just means your autonomous agents will coordinate confidently over unreliable inputs. If you want to hit 95%+ accuracy in a regulated industry, you have to flip the sequence.  Build the semantic layer before the model ever touches the data. Stop optimizing prompts and start fixing your data foundation.  Read the full architectural in the comments.  #EnterpriseAI #RAG #AIArchitecture #DataEngineering #AgenticAI #Irisai

Victor shared a metric recently: 10/10 proofs of concepts (POC) converted to production this year! 🚀 I want to unpack how Iris.ai got there. The approach comes down to three main moves.  First is rigorous scoping before anyone signs a contract. Iris.ai invests significant time upfront to understand the exact problem the enterprise needs to solve. This means setting clear expectations and defining success metrics early, so the technology aligns with strategic business goals rather than isolated experiments. Second is the discipline to say no. Companies often approach Iris.ai with a dozen different ideas for implementation. Iris.ai points them to the single, measurable first use case that proves value no one else can deliver. Turning away distractions requires focus, yet it ensures the initial project establishes a solid foundation for future scaling. Third is the reality of the data itself. Most enterprise pilots fail because they are built on clean, simple datasets and then they meet messy real-world data. Industrial knowledge is full of contradictions, legacy files, and undocumented rules. Iris.ai makes messy data its core business. Because the infrastructure is engineered to handle complex, unstructured knowledge from day one, projects move from a limited proof of concept direct to full production without falling off a technical cliff. Scoping heavily and saying no requires immense operational discipline. The effort pays off when every single project lands and delivers measurable results. What's the main reason you see technology pilots fail inside large organizations? #EnterpriseAI #TechPilots #DataStrategy #StartupGrowth #DeepTech

I’ve spent over 20 years in the data, AI, and working with enterprise software world. I’ve lived through the cloud cycle, the big data cycle, and the transformation in machine learning.  But what I’m seeing on the ground in enterprise AI right now is a completely different beast 🔥 A few weeks ago, I was sitting in a room with an enterprise buyer who had evaluated over 20 different AI vendors. Their whiteboard looked like a graveyard of promising ideas, disconnected proof-of-concepts, and endless model evaluations. But when I asked how many workloads they had running in production? Barely anything had crossed the finish line to the quality they expected.! When I joined Iris.ai to reignite this 10-year-old deep-tech company, we faced a hard truth. We had 10 years of world-class research under the hood. But commercially? People, product, process were working independently. We lacked a coherent growth engine. Turning around an existing deep-tech company is WAY harder than building from scratch 💪 You have to make hard calls on what to stop doing, find your defensible position, and build the plane while flying it. For us, that meant shifting our commercial motion away from "selling AI promise" to solving the 20-year-old data bottleneck preventing enterprise AI from reaching production. When you get that engine right, results aren't a 10% incremental improvement, they are exponential 🚀 Take ArcelorMittal. Tracking 120+ complex competitor patents monthly across 68 languages used to take their R&D team up to 4 hours per document, nearly 480 hours a month of manual grind! We didn't hand them a generic chatbot. We implemented Axion™ to automate domain-specific entity linking and data extraction, turning fragmented documents into structured intelligence. The real-world commercial impact? ↳ 90% reduction in overall patent analysis time. ↳ 60x faster extraction turning a 4-hour manual task into 4 minutes per document. ↳ 94% precision accuracy. ↳ 30x more patents processed per month, freeing their researchers to actually drive strategy and instead of manual data entry. Now a reusable data knowledge foundation is ready for AI & scale output by 30x while cutting costs 🤝 That is what real AI commercialization looks like: Not another pilot. Not a disconnected model evaluation. We’re building this commercial engine with that same builder, customer-obsessed, Day 1 mentality. Exciting momentum ahead as we scale across Europe and globally! Let's GO! 🔥 Now, time for me to log off and prep for tomorrow's calls (and try to run off some of this weekend's food, my triathlon training is definitely calling my name 🏃♂️😅)! Huge thanks to our incredible team and partners building alongside us! Victor Botev Anita Schjøll Abildgaard Ivan Tsenov #EnterpriseAI #DeepTech #AICommercialization #ScaleUp #Day1 #BusinessGrowth #TechLeadership

Raw data is a liability that leads to a foundation of hallucinations. Unorganized documentation is the primary bottleneck for enterprise AI deployment.  After 10 years of R&D on scientific text, we recognize that durable capability requires more than just "more data." It requires unified, normalized data. Feeding raw technical files into a general-purpose LLM often results in: ➔ Significant Accuracy Gaps: General models are good for general tasks but struggle with domain-specific deep knowledge terminology. For example, a "train" in everyday English is transportation, but in nuclear engineering, it is a parallel safety channel. Because models rely on general statistical priors, they struggle to distinguish this technical truth from generative "fluency." ➔ High Operational Friction: Teams waste time on manual data labeling and complex taxonomies. ➔ Contextual Noise: Without a clean foundation, models fail to handle complex layouts like chemical tables and patent graphs. We bridge the AI readiness gap by turning complex documentation into a verified knowledge foundation. At Iris.ai, we normalize data before any model interaction begins: → Unifying Ontologies & Metrics: We map unstructured text, tables, and graphs into standardized schemas, connecting fragmented data points and automatically reconciling conflicting units (like Fahrenheit and Celsius). → Resolving Domain Terminology: We map internal company jargon, project abbreviations, and industry vocabulary to standard definitions upstream, preventing downstream misinterpretation. → Enforcing Source Authority: We embed source hierarchy and conflict rules directly into the data layer, ensuring authoritative policies automatically take precedence over drafts or contradictory files. Durable AI isn't built on the volume of your data, it’s built on the readiness of it. Is your organization viewing unorganized data as an asset or your primary bottleneck to ROI? #AIReadiness #EnterpriseAI #DataStrategy #DeepTech #IrisAI

📰 We're in TechBullion. TechBullion covered the Iris.ai and Amazon Web Services (AWS) strategic collaboration, this time through the lens that matters most to us: regulated industries. In financial services, manufacturing, healthcare and public sector, AI output has to be deterministic and auditable, not just plausible. AWS provides the enterprise cloud and model stack. Iris.ai provides the trusted knowledge layer those models and agents reason on, running natively on Amazon OpenSearch, Bedrock, EC2 GPU and SageMaker, and now available through AWS Marketplace. As our co-founder and CEO/CTO Victor Botev put it: "Most enterprise AI conversations revolve around model choice. That focus isn't wrong. But the model is only as good as the knowledge it has access to. If the knowledge layer is incomplete, outdated or poorly structured, the output looks convincing right up until it becomes a production issue. Or a trust issue." Our Axion engine has now processed and contextualised 330M+ documents across 68 languages and 50+ data source types at 96%+ precision, so teams build the knowledge layer once and deploy it across departments, use cases and workflows. Article link in the comments. #AgenticAI #EnterpriseAI #AWS #KnowledgeLayer