Eigen Technologies’ Post

Breaking Free: How TextQL Plans to Disrupt Enterprise Data Platform Lock-In. VMblog, David Marshall https://bit.ly/4n7ox3M TextQL, #MultiCloud #AI #Agent #MCP #SQL #Automation #DataIntegration #TabularData #ITPT The IT Press Tour 64th Edition in New York

Breaking Free: How TextQL Plans to Disrupt Enterprise Data Platform Lock-In. VMblog, David Marshall https://bit.ly/4n7ox3M TextQL, #MultiCloud #AI #Agent #MCP #SQL #Automation #DataIntegration #TabularData #ITPT The IT Press Tour 64th Edition in New York

Data quality that learns: fewer incidents, faster recovery Most DQ checks crack the moment data shifts, think of a new source or schema tweak.  What if your DQ system learned from each run and got better next time? Meet the SALA‑Driven Data Quality Framework: a multi‑agent system that continuously improves DQ rules in production as an example of how the Symbiotic Agent Learning Architecture (SALA) changes how AI systems operate. Instead of a single model, SALA orchestrates specialized agents; Rule Executor, Anomaly/Drift Monitors, and an LLM Policy Synthesizer, through a Mediator (the coordinator that collects evidence and updates policies) that manages learning and policy updates over time. It fuses empirical evidence (audits, drift, outliers) with semantic reasoning to propose better rules, then updates policies via clear governance (manual approval or auto‑accept above a confidence threshold). Result: faster stabilization, fewer silent failures, and measurable improvements. This framework is proof that SALA shifts AI systems from “model‑first” to a symbiotic oriented, mediator‑driven and self learning systems that adapt continuously. How it works in practice: 👇 Dive deeper: Read the framework (PDF)  #AI Agent #DataQuality #MLOps #DataEngineering #LLM #SALA

𝗬𝗼𝘂𝗿 𝗔𝗜 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗶𝘀 𝗼𝗻𝗹𝘆 𝗮𝘀 𝘀𝘁𝗿𝗼𝗻𝗴 𝗮𝘀 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮. 📊 Data quality issues are forcing data science teams to waste 80% of their time on cleanup. This dramatically slows down model deployment and cuts into ROI. Discover how Zparse leverages AI-powered, no-code ETL to instantly provide the clean, consistent data foundation your machine learning models or RAG systems need for accurate results. Read our latest blog post to see the path from data chaos to competitive advantage: https://lnkd.in/emxGUzUb #AI #DataFoundation #NoCode #DataStrategy

🚨 The hidden cost of enterprise AI isn’t the 𝗺𝗼𝗱𝗲𝗹𝘀, it’s everything that comes 𝗯𝗲𝗳𝗼𝗿𝗲 them. Every enterprise we talk to tells us the same story: - 𝗗𝗮𝘁𝗮 𝗹𝗼𝗰𝗸𝗲𝗱 𝗶𝗻 𝘀𝗶𝗹𝗼𝘀 - 𝗘𝗻𝗱𝗹𝗲𝘀𝘀 𝗺𝗮𝗻𝘂𝗮𝗹 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 - 𝗔𝗜 𝗽𝗶𝗹𝗼𝘁𝘀 𝘀𝘁𝘂𝗰𝗸 𝗶𝗻 𝘁𝗵𝗲 𝗹𝗮𝗯 This is why 𝟴𝟱% 𝗼𝗳 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗳𝗮𝗶𝗹 𝘁𝗼 𝗱𝗲𝗹𝗶𝘃𝗲𝗿 𝘃𝗮𝗹𝘂𝗲. Our Autonomous Graph Database (KGNN) removes the bottleneck: → No manual ETL or schema design → Real-time knowledge graphs from any source → Insights in hours, not months → Enterprise-grade security at any scale Teams cut time-to-insight by 10–100x and escape the burden of repetitive, error-prone data wrangling. Ready to move beyond the lab?

Weekend project update: That synthetic data generator I shared last week (https://datagen.gptlab.ae/) now has a brain🧠 and a few new shiny bits and bobs. New features: https://lnkd.in/dqKY_uxY Intelligent Learning: It now uses a Qdrant vector database to learn from every single successful schema generation. The system literally gets smarter as people use it. The more data generated, the better it gets at handling complex types and refining outputs. Smarter, Context-Aware Suggestions: Leveraging that learned knowledge, the AI provides much more relevant field suggestions, using semantic search from thousands of past successful schemas. SQL Generation & Export: Beyond just data, it can now generate SQL CREATE TABLE statements directly from your schema and export generated data to various file formats (e.g., CSV, JSON) – super handy for different project needs! 3x Faster Generation: Performance got a serious boost! Average generation time is down from 30 seconds to just 10 seconds. Under the Hood (for the curious): This RAG pipeline is humming along with Ollama + Qdrant + N8N workflows. Every successful run feeds back into that learning loop. Building this setup has been a huge learning experience – exploring vector embeddings, optimizing semantic search, and creating effective AI feedback loops. Pretty cool to see it working in a live system! Give it a spin if you need some smart dummy data for your projects: 🔗 Try it here: https://datagen.gptlab.ae/ 📁 There is a feedback button now at the top so you can tell me how bad it actually is :) Would love to hear your experiences! #RAG #VectorDatabase #MachineLearning #AIImplementation #SyntheticData #DataScience #GenAI #ProductionAI #LearningSystems #OpenSource #TechInnovation #WeekendProject #SQL #DataExport

Transforming natural language into SQL is more than just query automation — it’s about building a secure, collaborative, and extensible system for data access. 🔹 Unified Approach: Customizable integrations, collaboration features, and strict security measures. 🔹 Process Funnel: From question interpretation → SQL generation → execution → visualization — every step optimized for clarity and accuracy. 🔹 Data Protection: Requests parsed, validated, and filtered to ensure only safe, read-only operations are executed. With this NL2SQL framework, we’re making data interaction intuitive, compliant, and enterprise-ready — turning everyday language into actionable insights. #DataEngineering #AI #NL2SQL #DataSecurity #Innovation #MachineLearning #DataAnalytics

AI is only as good as your data 📊. See how Omni and Coalesce work together to deliver #AI-ready analytics you can trust. Omni adds context with natural language and a semantic layer, while Coalesce ensures pipelines are governed, tested, and documented. Learn how to: 🔹 Build trusted, governed data foundations 🔹 Connect data models to business context 🔹 Automate testing and metadata management 🎥 Watch on demand: https://lnkd.in/gvahQVRS

AI multi-agents helped us solve data modeling tasks 3x faster 📊 We’ve been running AI multi-agents in real production pipelines since 2024. Along the way, we automated a lot of data modeling workflows: schema and field mapping, DDL and SQL generation, relationship inference, validation with test-case synthesis, and even self-healing for schema drift. Here’s what that looked like in numbers: 🔹 Self-healing pipelines reduced MTTR by roughly 45%. 🔹 Hybrid matchers auto-approved ±70% of routine mappings at >0.9 confidence in our pilots. 🔹 Query latency improved up to 40-50% on the 95th percentile. 🔹 Analyst support load went ±55% fewer ad-hoc analyst tickets. Now, we turned these findings and our best practices into a PDF guide, where we show you: agentic AI use cases, how we measured the data, and how to run a low-risk pilot in a few steps. 👉 Read the full PDF:https://bit.ly/4n0WcMt #AI #DataScience #MultiAgentSystems, #SchemaManagement #AWS #AWSPartner #AIAgents #BigData #Pipeline #DataOps #ML #data

Data quality check? More like data quality guesswork. You know that moment when you clean data and think: “Did I just delete a goldmine… or junk?” Even with AI in the mix, validation sometimes feels like roulette. Was that weird outlier a hidden gem or a sneaky error trying to ruin your day? AI flags the funky stuff fast. But judgment? That’s still the human game: 💡 Set smarter rules 💡 Double-check what AI flags 💡 And NEVER ignore that gut feeling, it’s saved more datasets than dashboards ever did. Let’s be honest, every researcher, PM, or QA has a ‘data oops’ story (or 5, or 10 - raise your 🖐 so we can see you!). Because behind every clean dataset… is a data handler quietly hoping they didn’t delete the good stuff. — The Quality Guy (playing 52-card pickup, data quality edition) #MarketResearch #DataQuality #AIinResearch #OpsLife #DataCleaning