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AI
65 posts about AI. Every summary links to the original.
The turbine that tried to tell you it was failing
Gas turbines produce millions of daily readings—vibration, temperature, pressure, flow rates, and electrical output—but maintenance teams may learn about warning signals only when an unplanned outage occurs. The post argues that predictive maintenance has struggled less because of model capability than because decision-makers lack fluid access to model findings and operational context. Databricks Genie provides a conversational interface to sensor, maintenance, dispatch, cost, and regulatory data, allowing questions about elevated vibration against maintenance baselines and timing maintenance around outage cycles. It is presented as decision support, not automation, with time-series analysis, maintenance history, integrated costs, and compliance context helping asset managers investigate fleets and act with more confidence.
Caitlin GordonCompanies winning with AI built the data layer first
Trinity Industries’ experience is presented as a case for treating the data layer, rather than models or dashboards, as the foundation of enterprise AI. The railcar manufacturer migrated 95% of its enterprise data to a single Databricks lakehouse, adopted Medallion architecture, moved transformations upstream, and retired legacy dashboards containing nearly 600 measures. That consolidation supports streaming ETA predictions, procurement agents, and Genie conversational analytics: the ETA model is reported as 50% more accurate than industry ETAs, agents helped increase on-time material delivery by 15%, and Genie handles more than 1,000 questions monthly. The migration took close to a year, followed by six to eight months of additional work, but the account argues that trusted, standardized data enables experimentation, automation, and broader employee access to analysis.
Aly McGueBuilt in, not bolted on: What AI-native actually means in cybersecurity
The discussion defines AI-native cybersecurity applications as systems architected with intelligence at their core, rather than traditional products with AI added later. It links tool sprawl to slower threat detection and response and argues that proprietary, context-rich security telemetry is the foundation for adaptive defense. Barracuda uses Databricks to consolidate fragmented data, normalize schemas, support real-time streaming detection, run ML operations through MLflow, and power natural-language log search across billions of security events with strict data isolation. The work began by defining customer outcomes, then progressed through incremental delivery; normalized data enabled models and agents to use cross-domain context. This approach extended across WAF-as-a-service, automated configuration, API security, and bot protection while shared outcomes aligned product, data science, engineering, and business teams.
Aly McGuePowering KPMG UK Audit's AI future with Databricks
KPMG UK is evolving its Audit data platform with Databricks to support AI-enabled analytics while preserving governance, rigour, and professional standards. The program converges structured data, advanced analytics, and AI on a unified cloud-native Lakehouse, with Databricks SQL replacing core SQL Server workloads and Delta underpinning the platform. Lakebridge assessed migration complexity, while Databricks-hosted large language models including Claude Sonnet and Genie Code helped convert T-SQL, refactor stored procedures, modularize queries, and suggest Delta-based optimizations under engineer review. This reduced refactoring time by around 60% and enabled modernization of more than 400 scripts and stored procedures in roughly three months. Databricks SQL Serverless provides elastic compute for spiky workloads, while Genie offers traceable, version-controlled SQL and Delta Sharing supports governed data exchange.
Mark Wallington, Greta NasaiOpenAI GPT-5.5 + Codex, now available and fully governed on Databricks
Databricks announces native support for OpenAI’s GPT-5.5, making it available for coding workflows with Codex, enterprise agents, document pipelines, and data-driven employee workflows. Access is governed through Unity AI Gateway, which provides permissions and rate limits, configurable guardrails for PII, prompt injection, and content safety, MCP tool-call auditing, failover, and request-level observability for model and Codex interactions. The announcement describes GPT-5.5 use with Genie for natural-language analytics, Agent Bricks Custom Agents for multi-step workflows, and Lakeflow Spark Declarative Pipelines for document-focused GenAI ETL involving summarization, extraction, and classification. GPT-5.5 is available on Databricks across AWS, Azure, and GCP, with entry points including AI Playground, governed Codex endpoints, Agent Bricks, and the Foundation Model API.
Patrick Wendell, Hanlin Tang, Ahmed Bilal, Arnav Singhvi, Ivan Zhou, Harish GaurGuide to BI Reporting and Maximizing Intelligence Effectiveness
Business intelligence (BI) reporting turns data from operational systems and analytical repositories into structured reports, dashboards, and visualizations for organizational decision-making. The guide explains managed and ad hoc reporting, then traces a typical workflow through data sources, ETL, deduplication, normalization, validation, data modeling, visualization, and automated distribution. It compares established platforms such as Microsoft Power BI, Tableau, SAP BusinessObjects, Qlik, and Looker with newer AI-native tools, while noting evaluation criteria including connectivity, scale, governance, usability, and cost. It also addresses dashboard overload, stale semantic models, and specialist bottlenecks, and cites reports of 10x faster query creation from AI-assisted natural-language querying alongside recommendations to start with one decision, one primary source, and three to five KPIs.
Databricks StaffAnnouncing the Public Preview of Lakeflow Designer
Databricks announces the Public Preview of Lakeflow Designer, a visual, no-code, AI-native experience for data preparation and analytics built directly into the Databricks platform. It targets analysts, domain experts, and other less technical users with drag-and-drop workflows and natural-language authoring, while representing each transformation as an operator with previews. Genie Code can use Unity Catalog metadata, descriptions, lineage, popularity, and example queries to generate or modify workflows, inspect transformation results, and iterate when needed. Every visual transformation produces production-ready Python code that can be reviewed, versioned in Git, and scheduled through Lakeflow Jobs, while users pay for compute rather than per-user licenses. The preview is available in all workspaces, although an administrator may need to enable Visual data prep in the preview portal.
Jason Messer, Emanuel Zgraggen, V Maharajh, Matt Jones, Tracy YangHow conversational analytics removes the BI bottleneck
The interview presents conversational analytics as a way to move beyond fixed dashboards and turn governed data into decisions and workflows. Databricks’ Genie lets nontechnical users ask open-ended questions in plain language across Lakebase, Lakehouse, and federated sources, while Unity Catalog applies shared definitions and access controls. Lakebase provides transactional storage, instant provisioning, environment forking without copying underlying data, and scale described as billions or trillions of records; examples include real-time matchmaking, routing, and purchase decisions. The discussion stresses that human oversight, refreshed data, business context, and controls against hallucination remain necessary, citing Fox Sports’ chatbot deployment as an example. It concludes that organizations combining trusted semantics with operational data systems can make faster decisions and automate work, while the competitive gap may compound for slower adopters.
Catherine BrownBeyond the spreadsheet: How Databricks is delivering the modern CFO in financial services
Financial-services CFOs are being asked to act as Strategists and Catalysts, but fragmented legacy systems, T+1 batch processing, opaque reporting logic, and mismatched business semantics keep them focused on stewardship and retrospective operations. The proposed answer is Databricks as a unified, governed platform combining real-time streaming, centralized lineage, data and AI, with Unity Catalog, Lakeflow, Genie, and Agent Bricks addressing trust, latency, access, and model reproducibility. Unity Catalog can connect semantic definitions and end-to-end lineage from transactions through regulatory reports and models, while Lakeflow supports continuous ledger and liquidity processing. The post cites a global bank reducing liquidity-reporting processing from 10 hours to 8 minutes and Nationwide Insurance reporting a 5-point combined-ratio improvement and 3-point expense-ratio improvement. It presents a Lakehouse-based CFO stack as a shift from reporting historical results toward real-time capital management and previews AI-driven deposit and PPNR modeling.
Jennifer Miller, Marcela Granados, Andrea DeSosa, Alex Oberlander, Kim Hatton, Pavithra Rao, Naeem Rehman, Pravin Varma, Olga Deriy, Prasanna SelvarajAI App Development: Guide To Building AI-Powered Apps
Production-grade AI app development combines model orchestration, transactional state, governance, security, and live-data integration rather than only interface construction. The guide recommends defining users, outcomes, success metrics, timelines, and AI-relevant journeys, then shipping must-have features before enhancements. It evaluates builders by end-to-end support, technical audience, platform integration, code export, CI/CD, data connections, pricing, and deployment limits. For Databricks teams, it describes Databricks Apps, Declarative Automation Bundles, and Lakebase: serverless app compute, version-controlled deployment, and managed PostgreSQL with synced Unity Catalog Delta tables. It also recommends monitoring outputs, collecting correction feedback, refining prompts with automated evaluations, and governing models through MLflow, concluding that integrated infrastructure can leave teams focused on production AI workflows.
Databricks StaffA Modern AI Risk Management Framework
AI systems introduce probabilistic behavior, model drift, adversarial manipulation, bias, and auditability challenges that traditional IT risk practices may not address. The framework described combines NIST AI RMF, EU AI Act, and ISO/IEC 23894:2023, using Govern, Map, Measure, and Manage as its operational structure while recognizing regulatory and societal context. It recommends cross-functional governance, clear risk ownership, an AI-BOM covering systems, data flows, dependencies, and accountability, plus continuous measurement of fairness, explainability, data quality, security vulnerabilities, and harm likelihood and severity. Across the AI lifecycle, the text identifies risks including data poisoning, model drift, malicious library injection, prompt injection, hallucinations, and platform vulnerabilities, and points to the AI RMF Playbook for checklists, timelines, and governance artifacts.
Databricks StaffFrom Static Policies to Autonomous Insurance: How AI enables Real-Time Coverage
Insurance products often rely on annual pricing, periodic risk assessment, and coverage changes tied to major life events, even though driving behavior, digital fraud exposure, and travel risk can change in seconds. The post presents embedded insurance and usage-based insurance as a shift toward contextual, just-in-time coverage delivered through apps, checkout flows, vehicle purchases, and partner platforms. Its automotive example combines telematics, geospatial data, and real-time weather signals to update risk scores and premiums from trip behavior such as braking, following distance, mileage, location, and environmental conditions. A Databricks Lakehouse architecture registers devices, ingests telemetry through Zerobus, curates data in a governed Medallion architecture, and exposes features through Lakebase and Databricks One, with Unity Catalog supporting access controls, lineage, explainability, and audit trails. The proposed trajectory leads from embedded coverage toward autonomous insurance whose policies continuously adapt to risk.
Amee Vora, Anindita Mahapatra, Marcela GranadosRamp ·
Building a Unified Pipeline for AI Token Spend
AI token spend is volatile, consumption-based, and distributed across teams, making usage and cost difficult to monitor. Ramp's pipeline ingests LiteLLM and OpenRouter events through authenticated, tenant-scoped webhooks, Kafka, and ClickHouse, then aggregates them for REST dashboards and AI-generated forecasts. LiteLLM uses callbacks with token counts and metadata, while OpenRouter sends OTLP traces; ReplacingMergeTree deduplicates replayed events using business_id, source, and event_id. Customers can connect gateways with an API key, configure LiteLLM callbacks or OpenRouter Broadcast, and pass attribution metadata. The resulting visibility supports breakdowns by provider, model, department, user, project, and use case, and exposed phantom Gemini reasoning tokens after a LiteLLM upgrade, geo pricing premiums, runaway loops, and prompt bloat.
Kedar Thakkar, Richard Wang, Veeral PatelDatabricks on Google Cloud: Innovate faster. Smarter. Together.
Databricks and Google Cloud are presenting their partnership at Google Cloud Next ’26 as a route from governed enterprise data to production AI, including autonomous agents and modernized workloads. In 2025, Databricks became a first-party provider of Gemini models, giving users direct API access to build, customize, and deploy agentic AI on governed data across all three Databricks clouds without moving data or managing infrastructure. The post reports more than 55% quarter-over-quarter growth in Gemini adoption, 85% year-over-year growth in Google Cloud consumption, and 4,000-plus Marketplace sign-ups, alongside support for Google Axion processors. Customer examples include Databricks SQL Serverless savings for Digital Turbine, faster Marketplace procurement for Dun & Bradstreet, regional compliance in Saudi Arabia and Brazil, and 30% faster analytics delivery through Lakeflow-powered pipelines. At the event, Databricks is offering demos, talks, expert sessions, and a free $400 trial through Google Cloud Marketplace.
Taylor Hoss, Sarah JackWhat is Generative AI in Marketing?
Generative AI in marketing creates content, insights and recommendations that can personalize experiences, optimize campaigns and improve performance. Unlike traditional analytics, which mainly reports on past results, it produces net-new outputs such as ad copy, audience segments, product recommendations, visual assets and strategic summaries. A typical workflow prepares campaign, customer and brand data, grounds or fine-tunes models, generates outputs, applies targeting and optimization, and uses human review and refinement. It distinguishes pretrained tools such as ChatGPT, Claude and Perplexity from customized models and broader AI transformation, noting trade-offs between speed, investment, relevance and strategic alignment. Successful adoption depends on high-quality, consented data, governance, privacy controls, human oversight, monitoring and cross-functional ownership; the conclusion presents AI as a force multiplier rather than a replacement for human creativity and judgment.
Databricks Staff8 AI and data trends shaping financial services in 2026
The post argues that financial-services AI adoption is widespread, but execution—not model capability or strategy—is determining who captures value in 2026. It attributes stalled pilots to fragmented legacy infrastructure, inconsistent data, weak lineage, and insufficient control for governed, real-time workflows such as fraud detection, pricing, and personalization. Firms advancing further treat data as a managed asset, embed governance in data and model pipelines, and align data, analytics, and AI teams around shared definitions, workflows, and metrics. The proposed remedy is a unified lakehouse environment combining storage, compute, governance, lifecycle management, orchestration, streaming, and AI workflows, with Unity Catalog providing centralized access control, lineage, and auditing. The conclusion is that by the end of 2026, firms that embed AI into operational decisioning at scale will pull ahead of organizations still running pilots.
Kim Hatton, Junta Nakai, Marcela Granados, Antoine Amend, Ashraf Safdar, Jennifer Miller, Andrea DeSosa, Rajaram SureshLovable + Databricks: Build Data-Driven Apps at the Speed of Thought
Databricks users outside data teams often depend on requests to access business data because they may not write SQL or have a Databricks license. The official Lovable connector lets teams connect Lovable to a Databricks environment and build custom internal applications with plain English. Lovable's AI-powered agent examines accessible data and builds and deploys an application, while Databricks remains the source of truth and data stays within its security perimeter. Data is queried from Databricks at runtime, avoiding ETL, data replication, and sync jobs. Examples include live revenue and pipeline dashboards, spreadsheet-replacing operational tools, internal Slack chatbots, and other business applications that teams can build without engineering or data-team support.
Evan PandyaRamp ·
Automating Receipt Collection: Apple Intelligence for On-Device Inference
Ramp built automatic receipt detection for its iOS app to reduce the effort of finding receipt photos while keeping private images on device. An initial design used PhotoKit, Vision OCR, background processing, local storage, and exact checks for merchant, amount, and date, but formatting differences, aliases, and OCR errors produced many false negatives. FoundationModels enabled a local structured-output LLM, yet a single prompt performed poorly in evals because the small 4096-token model hallucinated and took shortcuts. Splitting the merchant, date, and amount questions and requesting rationale improved evaluation results, but the alpha release reached 66% precision and about 18% recall. Version 3.0 combined one LLM merchant-name check with deterministic iOS 26 data detectors for dates and monetary values; it cut processing time, more than doubled recall, and raised precision to 87%.
Kabir OberaiRamp ·
Financial Benchmarks
Ramp describes how it benchmarks large language models (LLMs) used in financial products against day-to-day tasks such as invoice extraction, financial-statement OCR, policy review, accounting autocoding, compliance detection, and fund routing. These benchmarks combine task-specific metrics with cost, latency, reasoning effort, human decisions, historical context, and ground-truth datasets where available. For contextual invoice OCR, perfect extraction requires every field to match the user's final bill, while financial-statement OCR uses a 1% relative-error threshold against transcriptions from over 500 real P&L documents. Results show application-specific trade-offs: Gemini 3 Flash is described as a cost-efficient leader for several visual and financial tasks, while Claude models lead some high-accuracy or low-miss-rate settings, and behavior can differ within one provider. The framework emphasizes Pareto trade-offs and continuous testing, with plans to expand from benchmarking current performance to hill climbing on capabilities.
Kedar Thakkar, Anton Biryukov, Ashwin Kumar, Ryne CarboneRamp ·
Closing the Books Without the Spreadsheet Shuffle: My Fall Internship at Ramp
During a fall internship at Ramp, the author worked on reducing repetitive accounting work around month-end close, focusing on amortization and ERP mapping workflows. One project uses receipt data, merchant category codes, customer history, and embedding similarity to detect likely prepaid expenses and suggest service periods and accounts or templates, with recommendations reviewable before application. A second built Ramp-Native Amortization to create schedules, generate periodic debit and credit journal entries, handle catch-ups and reclassifications, and sync entries to ERPs through a dashboard; transaction and reimbursement support was nearing alpha production release, while bill pay was next. The third generates high-confidence mapping-rule suggestions from customer coding patterns on a nightly schedule. Across the work, backtesting, accuracy metrics, and human workflow research support automation intended to earn trust rather than remove accountant judgment.
Timothy Kim
