Tatras Data

2023 was about proofs of concept. 2024 was about plugging GenAI into workflows. 2025 is about building the infrastructure that will last. 𝗧𝗵𝗲 𝘀𝗺𝗮𝗿𝘁 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗿𝗲 𝗮𝘀𝗸𝗶𝗻𝗴:  • How do we capture clean data streams across functions?  • Where do we need vector-native infrastructure to bridge structured + unstructured sources?  • What’s the right balance between agentic AI orchestration and traditional ML systems that quietly run the backbone?  • How do we design for governance? AI maturity will look less like adding copilots and more like designing an operating layer for decisions. The companies that invest in infra today will own the capabilities everyone else rents tomorrow. At Tatras, we help teams architect systems that scale with them. 𝗟𝗲𝘁’𝘀 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝘄𝗵𝗮𝘁 𝘁𝗵𝗮𝘁 𝗰𝗼𝘂𝗹𝗱 𝗹𝗼𝗼𝗸 𝗹𝗶𝗸𝗲 𝗳𝗼𝗿 𝘆𝗼𝘂 → https://lnkd.in/gukd4KsH

𝗖𝗹𝗮𝘂𝗱𝗲 𝟰.𝟱 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗧𝗿𝘂𝘀𝘁𝘄𝗼𝗿𝘁𝗵𝘆 𝗔𝗜 Anthropic has been running a different race than the other AI titans with a greater focus on reasoning and safety. 𝗪𝗶𝘁𝗵 𝟰.𝟱, 𝗲𝗮𝗿𝗹𝘆 𝗿𝗲𝗽𝗼𝗿𝘁𝘀 𝗽𝗼𝗶𝗻𝘁 𝘁𝗼:  • Sharper long-context handling   • More consistent reasoning   • A step closer to agents that can hold state and execute reliably Why does this matter? Because AI isn’t enterprise-ready until you can trust an LLM to complete a workflow without breaking or hallucinating mid-process. Every release like this pushes the line a little further from “assistant” to “co-worker.” If you’re betting on LLMs, the question isn’t which model is best for you today. The question is which organization’s roadmap aligns with the systems you’ll need 18 months from now? At Tatras, we help teams cut through the noise and build systems that last. 𝗟𝗲𝘁’𝘀 𝘁𝗮𝗹𝗸 —> 𝗵𝘁𝘁𝗽𝘀://𝘁𝗮𝘁𝗿𝗮𝘀𝗱𝗮𝘁𝗮.𝗰𝗼𝗺/𝗯𝗲𝗴𝗶𝗻-𝘆𝗼𝘂𝗿-𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻/

𝗠𝗟 𝗗𝗲𝘃𝗢𝗽𝘀 𝗶𝘀 𝘄𝗵𝗲𝗿𝗲 𝗺𝗼𝘀𝘁 𝗔𝗜 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘀𝘁𝗮𝗹𝗹. Everyone talks about model benchmarks and intelligence, but far fewer teams think deeply about the pipeline. Sometimes the fastest way to scale is by improving the infrastructure around the the models you already have in production. Think about what changes after the proof-of-concept:  • Your data starts drifting  • A vendor changes their document format and your claims model fails.  • Your forecasting pipeline gets messy because you can’t trace which model version used last quarter’s commodity data This is where ML DevOps matters. Companies that get the infra right early ship faster and build credibility with their clients. If you’re hitting a wall in moving from pilot to production, maybe it's not your model but your pipeline. We’re curious, what are some DevOps issues your teams is facing right now?

𝗪𝗵𝘆 𝗛𝘂𝗺𝗮𝗻-𝗶𝗻-𝘁𝗵𝗲-𝗟𝗼𝗼𝗽 𝗦𝘁𝗶𝗹𝗹 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 𝗶𝗻 𝗔𝗜 AI can scale decision-making. But only if humans remain part of the feedback loop, guiding, and improving the system over time. That’s the essence of 𝗵𝘂𝗺𝗮𝗻-𝗶𝗻-𝘁𝗵𝗲-𝗹𝗼𝗼𝗽 (𝗛𝗜𝗧𝗟) 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻. The cycle is simple but powerful. At Tatras, we use this approach across every production deployment. It’s how we make sure AI stays reliable under pressure whether that’s processing compliance documents, running financial reconciliations, or powering internal copilots. 𝗧𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗔𝗜 𝗶𝘀 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻, 𝘄𝗶𝘁𝗵 𝗵𝘂𝗺𝗮𝗻𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗹𝗼𝗼𝗽. Speak to AI experts today → https://lnkd.in/gukd4KsH

𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗙𝗮𝗶𝗹 𝟳𝟬% 𝗼𝗳 𝘁𝗵𝗲 𝗧𝗶𝗺𝗲. 𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝗙𝗶𝘅 AI agents were supposed to be the leap from conversation to action. They would book flights, reconcile invoices, and automate the routine while you focused on strategy. The reality is less impressive. Most agents collapse the moment they leave the demo stage. A recent benchmark ran popular frameworks through simple, well-defined tasks. They failed nearly 70 percent of the time. This gap is the real frontier. Building better agents isn’t about stacking more GPUs or adding another GPT, it’s about engineering discipline. At Tatras, we focus on the parts that make agents production-ready. Reliable agents come from the kind of engineering that rarely gets a spotlight: 𝗟𝗼𝗻𝗴-𝗹𝗶𝘃𝗲𝗱 𝘀𝘁𝗮𝘁𝗲 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Persist workflow state across steps and API calls instead of relying on stateless prompts. 𝗘𝗿𝗿𝗼𝗿 𝗵𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗮𝗻𝗱 𝗿𝗲𝗰𝗼𝘃𝗲𝗿𝘆: Use structured retry policies, backoff strategies, and fallback plans so transient failures don’t terminate runs. 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝘁𝘆𝗽𝗲 𝘀𝗮𝗳𝗲𝘁𝘆: Enforce schemas (e.g. Pydantic, JSON schema) to guarantee well-formed outputs and prevent integration failures. At Tatras, we build agents you can trust in production. Finance, compliance, operations; wherever reliability matters, our systems are designed to hold up under pressure. If you’re ready to move past fragile experiments, let’s talk. 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗲 𝗮 𝗰𝗼𝗻𝘀𝘂𝗹𝘁 𝘁𝗼𝗱𝗮𝘆 → https://lnkd.in/gukd4KsH

𝗖𝗹𝗮𝘂𝗱𝗲 𝗦𝗼𝗻𝗻𝗲𝘁 𝟰: 𝗧𝗵𝗲 𝗠𝗶𝗹𝗹𝗶𝗼𝗻-𝘁𝗼𝗸𝗲𝗻 𝗦𝗵𝗶𝗳𝘁 Claude Sonnet 4 now supports one million tokens of context. That’s a 5x jump, enough to load 75,000+ lines of code, dozens of research papers, or entire legal archives in a single request. This changes what developers can do.  • 𝗖𝗼𝗱𝗲𝗯𝗮𝘀𝗲𝘀 𝗮𝘀 𝗰𝗼𝗻𝘁𝗲𝘅𝘁: Instead of working file by file, Claude can now analyze an entire project at once. So the entire architecture, cross-file dependencies, and documentation are all in scope.  • 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝘀𝘆𝗻𝘁𝗵𝗲𝘀𝗶𝘀: Hundreds of contracts, specifications, or research papers can be processed together, with relationships mapped across the whole set.  • 𝗖𝗼𝗻𝘁𝗲𝘅𝘁-𝗮𝘄𝗮𝗿𝗲 𝗮𝗴𝗲𝗻𝘁𝘀: Agents can maintain state across hundreds of tool calls and multi-step workflows, with full documentation and histories included. Pricing scales with size, but Anthropic has added prompt caching and batch modes to offset cost and latency. Early adopters like Bolt.new and iGent AI are already using the expanded context for production-grade code generation and agentic software engineering. At Tatras, we see long context as more than a feature upgrade. It’s a shift in what becomes possible. The bottleneck has always been how much you can fit into the model’s head at once. A million tokens moves that line from “one document” to “an entire system.” If you want to explore how to use long context windows for production workflows — from compliance to code intelligence — talk to us. Schedule a consult today → https://lnkd.in/gukd4KsH

When LLMs Replace Meetings, Who Replaces Context? Everyone’s excited about cutting meetings with AI. Fair enough, who doesn’t want fewer calls? But most “meeting replacers” miss the point. They generate summaries, bullet points, action items. As if that’s all a meeting ever was. The real substance of a meeting isn’t what’s said. It’s what’s understood.   By the right people, in the right way, at the right time. That’s where trust lives. And that’s what Tatras helps preserve. We work with clients to build systems that shape context differently for a lead engineer than for an exec. They maintain memory across conversations. They align outputs to the rhythms of how your team operates. GenAI can reduce meetings. But only if it replaces what made them valuable in the first place. Want systems that communicate how your org works? Book a 1:1 with a Tatras lead → https://lnkd.in/gukd4KsH

𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲'𝘀 𝘁𝗮𝗹𝗸𝗶𝗻𝗴 𝗮𝗯𝗼𝘂𝘁 𝗚𝗿𝗼𝗸 4. Elon Musk’s latest model from xAI that claims to be the 𝘴𝘮𝘢𝘳𝘵𝘦𝘴𝘵 𝘈𝘐 𝘪𝘯 𝘵𝘩𝘦 𝘸𝘰𝘳𝘭𝘥. Multimodal, agentic, math-savvy, black hole-visualizing... the works. But here’s the question no one’s asking: 𝗖𝗮𝗻 𝘆𝗼𝘂𝗿 𝗼𝗿𝗴 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘂𝘀𝗲 𝗶𝘁? Most enterprise AI roadmaps don’t fail because of weak models. They fail in the 10 feet between the model and your workflow. Let’s say you plug in Grok 4. Now ask yourself this: • Where’s the feedback loop? • Who trusts the outputs? • What happens when the org changes, or Grok 5 drops next quarter? 𝗔𝘁 𝗧𝗮𝘁𝗿𝗮𝘀, 𝘄𝗲 𝗯𝘂𝗶𝗹𝗱 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗹𝗮𝘀𝘁 𝗺𝗶𝗹𝗲: • Human-in-the-loop where it matters. • Continuous evals • Embedded into actual workflows You don’t need the smartest model. You need the one that 𝘴𝘶𝘳𝘷𝘪𝘷𝘦𝘴 𝘵𝘩𝘦 𝘳𝘦𝘢𝘭 𝘸𝘰𝘳𝘭𝘥. Curious how Grok 4 fits into your workflows? Let’s map it out: h̲t̲t̲ps̲:̲/̲/̲t̲a̲t̲r̲a̲s̲d̲a̲t̲a̲.̲c̲o̲m̲/̲b̲e̲gi̲n̲-̲yo̲u̲r̲-̲t̲r̲a̲n̲s̲f̲o̲r̲m̲a̲t̲i̲o̲n̲/̲

𝗚𝗲𝗻𝗔𝗜 𝗜𝘀𝗻’𝘁 𝗮𝗻 𝗔𝗽𝗽. 𝗜𝘁’𝘀 𝘁𝗵𝗲 𝗡𝗲𝘄 𝗦𝘁𝗮𝗰𝗸. Most companies still think of GenAI as a UI. As another clever interface on top of old systems. That’s a mistake. The real shift happens when you stop seeing it as a feature and start seeing it as a substrate. Something to build with, not just build on. At Tatras, we help clients make that shift. It’s a cognitive shift. • From writing prompts → to designing around intent • From chatbots → to modular reasoning • From tools → to toolchains Once you stop treating GenAI like a layer, you start using it like infrastructure. The kind platforms are made of. Does your team need AI that understands your team’s inner tempo? Let’s explore that together: Book a discovery session →

𝗪𝗵𝘆 𝗘𝘃𝗲𝗿𝘆 𝗚𝗿𝗲𝗮𝘁 𝗔𝗜 𝗦𝘆𝘀𝘁𝗲𝗺 𝗜𝘀 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗡𝗮𝗿𝗿𝗼𝘄 There’s no such thing as a general AI solution. The best AI systems are narrow by design, but feel broad in use. At Tatras, we’ve worked with clients across insurance, manufacturing, banking, and foodtech. Almost every time, the ask is the same: “We need an AI assistant.” Something that can 𝗮𝗻𝘀𝘄𝗲𝗿 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀, 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝘁𝗮𝘀𝗸𝘀, 𝗮𝗻𝗱 𝘀𝗰𝗮𝗹𝗲 across the org. Sounds great in theory, but that kind of breadth rarely delivers real impact. What works is focused intelligence aligned with the outcomes that move the needle. Sure, models that can debate Plato are impressive. But they’re not much help when you’re reconciling invoices or resolving a claim. Behind the illusion of generality is a stack that’s anything but generic:  • 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗳𝗹𝗼𝘄𝘀 grounded in domain-specific logic  • 𝗦𝗺𝗮𝗿𝘁 𝗰𝗵𝘂𝗻𝗸𝗶𝗻𝗴 tailored to real-world documents and databases  • 𝗠𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 that knows when to defer, escalate, or remain silent  • 𝗘𝗺𝗯𝗲𝗱𝗱𝗲𝗱 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗹𝗼𝗼𝗽𝘀 that adapt to people, not just prompts Real impact comes from architecture that understands the domain, the user, and the edge cases. That’s what we build. If you’re building something for the messy real world, let’s talk. Speak with AI experts today → https://lnkd.in/g-PPdniJ