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16 posts about Ontology. Every summary links to the original.
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Asset management runs as a layered control plane: custodian and pricing data feed a governed context layer, agents run the overnight fund work, and a human signs off before anything is published.
AI data assimilation fuses a model's prior state with fresh, uneven observations into one consistent state. See the four-layer architecture and where each layer can break.
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Buy-side asset management finance departments face growing complexity from AI agents influencing valuation, redemption, and reconciliation workflows, while protecting net fee margin and NAV integrity remains their core mission. Databricks Genie One is presented as a data-smart AI coworker grounded in Genie Ontology, which captures business meaning and keeps definitions current as strategies and fee arrangements shift. Behind the assistant, Genie Agents automate overnight fund operations: ingesting custodian files, pricing positions, reconciling records, and producing a preliminary NAV for review. Every figure is traceable to its source, permissioned by role, and provable to investors or regulators, enabling finance teams to see forming risks early. The system does not make investment or valuation decisions; a human remains in the loop for every action.
The Post argues that AI agents are breaking a long-standing assumption in enterprise data: that a human analyst can always resolve ambiguity. Richard Tomlinson explains that while analysts rely on tribal knowledge—like which revenue table is trusted or what ‘active customer’ means—agents lack this context. This gap causes inconsistent answers and erodes trust in self-service tools. The conversation examines why previous ontology efforts stalled and what must be true for an ontology to support agents. The ontology is positioned as the missing context layer that companies have not yet built.
Glyph addresses enterprise data lakes where tables accumulate faster than human stewards can document or classify them, leaving column descriptions and governance labels incomplete. It models column description generation and sensitivity-ontology tagging as cooperating LLM agents orchestrated as stateful graphs. The Descriptor uses active Retrieval-Augmented Generation to retrieve pipeline source code from an enterprise GitHub, while the Tagger runs description, line-of-business regex, and metadata strategies in parallel before combining ranked outputs with Reciprocal Rank Fusion. A fine-tuned six-layer MiniLM metadata encoder raised same-tag retrieval on an in-distribution held-out split from NDCG@10 0.55 to 0.92 and MAP@100 0.19 to 0.90, relative to the stock base encoder. Glyph also evaluates end-to-end multilabel tagging with a recall-weighted F2 objective across three groups and an ablation, emphasizing value-free, code-grounded operation, per-tag provenance, and graceful degradation for auditable production cataloging.
The post argues that healthcare AI governance must extend beyond security controls to include knowledge, context, and ontology: security governs access, while semantics determine what data means and whether models can trust it. DEP frames classification tags, model cards, data contracts, and lineage as machine-readable governance artifacts, with Unity Catalog serving as the metadata runtime for agents. Build agents can generate ETL, testing, de-identification, and deployment, while analytic agents answer questions over individual data products; human stewards approve proposals and own accountability. A shared lifecycle certifies the data product and AI track through gates using scores for governance, quality, semantics, and ownership, with certification expiring after relevant changes or failures. The approach applies data-layer ABAC to SQL and vector retrieval, logs agent prompts with lineage, and connects readiness metrics to model economics by enabling smaller models when cataloged data supplies sufficient context.
Genie Ontology is presented as a way to give enterprise AI agents shared business context beyond a semantic model, including definitions, relationships, business rules, authoritative sources, and permissions. It combines Unity Catalog Semantics—Metric Views, Pages, and Domains—with context inferred from governed tables, queries, dashboards, notebooks, and other supported assets. The guidance recommends six progressive layers, beginning with clean gold data and resolved golden records, then metadata, semantic modeling, enterprise context, governance, and evaluation. Critical implementation details include declaring informational primary and foreign keys, defining canonical measures in Metric Views, adding synonyms and example queries, and using permissions plus human-reviewed automation. Rather than waiting for complete coverage, it advises starting with one high-value domain and metric, then using feedback, telemetry, benchmarks, and drift reviews to strengthen trust over time.
The post examines why an agent can cite accurate return-policy evidence yet reject an eligible customer when decisive facts are distributed across systems. Its washing-machine scenario combines a product classification, an extended-policy rule, membership tier, purchase amount, and purchase date to show that retrieving enough evidence differs from retrieving relevant evidence. GraphRAG addresses the gap by extracting entities and relationships into a knowledge graph, then using multi-hop retrieval to follow connections from WM-500 to the applicable policy and supporting records. The implementation also uses explicit pointers from graph entities to structured data, while separating the TBox blueprint from ABox instances and requiring business validation of the schema. The diagnostic framework distinguishes missing retrieved context, omitted business rules, and broken record mappings, while noting that these examples are not benchmark results and that correct evidence can still be used incorrectly.
Telecom finance teams face revenue leakage when services go unbilled or uncollected, fraud and partner-settlement errors drain charges, and customer churn removes future spending. The source argues that fragmented ordering, billing, ERP, and spreadsheet data makes month-end reconciliation too slow, turning discrepancies found weeks later into write-offs rather than recoverable revenue. It presents Databricks Genie as a data-smart AI coworker whose ontology captures business meaning across systems, stays current, and grounds sourced answers in governed data. Genie can answer billing, invoice, spend, and variance questions, surface anomalies, and support action while a person remains responsible for decisions; Lumen Technologies is cited as using it across the Office of the CFO. The text says Genie does not make billing, fraud, or pricing decisions.
Media finance teams are tasked with understanding how audience value flows across subscriptions, advertising, and content investment so the business can protect margin. Streaming has turned one wholesale audience into multiple monetization strategies, while ad-supported tiers now account for 59% of new streaming sign-ups, making timely measurement and pricing analysis more important. Ontology preserves the meaning and context of figures as audiences, titles, channels, and business conditions change, distinguishing a correct answer from one that is merely accurate. Databricks Genie is described as a governed, data-smart AI coworker that answers sourced natural-language questions, learns from interactions, and shows its work; Genie-powered apps let hundreds of DIRECTV analysts and leaders query more than 1,200 customer-level attributes, while people retain decision authority.
Databricks announces the Public Preview of Domains and the Discover page, powered by Unity Catalog, to help people and agents identify relevant, high-quality, and safe data and AI assets. Domains organize tables, dashboards, notebooks, queries, metric views, Genie Agents, and apps by business structure, while Discover provides an internal marketplace with search, certification signals, popularity and trending indicators, and AI-powered recommendations. Data stewards can create domains and subdomains, certify assets, add descriptions and contacts, and curate page sections and pinned content. Domains extend Unity Catalog Semantics and feed Genie Ontology, giving agents business context for narrowing retrieval, prioritizing trusted assets, and interpreting metrics within each user’s existing permissions. The features are available in Public Preview through a Databricks workspace, where organizations can create domains and curate assets.
AI-native tech companies must protect unit economics as agents accelerate changes in compute consumption, pricing, and revenue recognition, while gross margins remain below classic software levels. Finance teams built around extracts, spreadsheets, and monthly reconciliation can miss repricing changes, metering errors, and compute-commitment risk. The proposed foundation is an evolving ontology that keeps product, plan, usage, and cost meanings current, with Stripe data entering Unity Catalog through OpenSharing and Lakebase providing transactional Postgres on the lakehouse. Genie One uses that ontology to answer governed, sourced questions about gross margin, consumption revenue at risk, and compute spend, while people retain decision authority. The post describes organizations using Databricks to consolidate reporting, forecasting, workflows, and finance applications, positioning a shared data-and-AI platform as the path from an initial answer to an ongoing finance platform.
Enterprise AI assistants often produce fluent answers yet fail to improve forecast calls, deal reviews, and operational standups because decision context is scattered across systems, teams, and competing definitions. Genie One addresses this by using a shared context layer spanning Databricks data, documents, SaaS applications, and operational systems, allowing questions and follow-up work to retain business meaning. Genie Ontology organizes terms, metrics, entities, and relationships into a living knowledge graph, learning from data, dashboards, queries, documents, and connected applications while ranking definitions and signals using usage and certified-asset links. Together with Unity Catalog, it applies permissions, certified data, shared definitions, and governance controls to answers, actions, and agents. The stated outcome is faster movement from decision preparation to action, with less manual reconciliation while preserving accuracy and control.
Omni-channel retail has spread margin, cash, and markdown decisions across more channels, fulfillment paths, and return routes, while agentic systems increase the speed and complexity of change. The post presents ontology as a way to preserve the meaning and business context behind finance figures, keeping definitions, channels, and cost drivers current. Databricks Genie is described as a data-smart AI coworker that answers finance questions in plain language, grounding responses in an evolving ontology, source traces, permissions, and governed AI costs. It focuses on margin after fulfillment and returns, inventory cash tied up in the wrong place, and full-price revenue at risk from markdowns and returns, then prepares actions for a person to approve. Unilever deployed Genie to more than 1,200 finance and business users; analysis that took days now takes minutes, with expected multi-million-euro annual cost avoidance.
Vibe Data Modeling is presented as a way to build a governed Silver-layer data model from a plain-English description of a business, addressing the months or years often required to hand-build one or adapt a generic industry template. The single-notebook implementation uses four widgets and a multi-model LLM agent to organize organizations into divisions, domains, subdomains, products, and attributes, then generate a logical model and deploy it to Unity Catalog. Its pipeline advances through four generate-and-validate stages, applying 251 rules, deterministic structural gates, two architect reviews, and a retry loop that changes strategy when checks fail. The authoritative model.json also produces schemas, Delta tables, informational foreign keys, classification tags, metric views, an RDFS ontology, DBML, and synthetic sample data, while plain-English refinements create auditable, reversible versions.
Databricks Forward Deployed Engineering introduces Decision Execution Platforms (DEPs), an enterprise analytics category intended to connect KPI signals, executive decisions, operational execution, and measured outcomes. The proposal addresses workflows in which dashboards reveal problems but meetings, decks, spreadsheets, and messaging threads leave implementation fragmented and impact measurement disconnected. DEPs run the four-stage loop on governed Databricks infrastructure: agents recommend actions, alternatives, predicted impact, and reasoning; approved choices execute through systems of record; and results persist in a Decision Log for continuous learning. Their architecture combines a foundation of Lakebase, Genie, Unity Catalog, Lakehouse, Agent Bricks, and MLflow with an SDK of reusable primitives and a Databricks Apps executive surface. A retailer case used a DEP to unify fulfillment data and enable simulated, controlled rerouting, with scaling aimed at measurable bottom-line and customer-satisfaction outcomes.
At Data + AI Summit 2026, Databricks announced expansions to Genie Code for complex, agentic data and ML work. The changes include a full-page command center for managing concurrent threads and assets, upgrades across production ML engineering, and scheduled tasks that run prompts while users are away. For ML workflows, Genie Code uses Databricks production expertise and Genie Ontology, integrates with MLflow and Model Serving, and can move GPU jobs to AI Runtime while using workspace environment features. It can write features, coordinate edits, run and debug code, compare candidates, inspect endpoint health, and diagnose issues, with users deciding what to keep. Scheduled tasks are described as coming soon, creating reviewable threads from prompts and optional Databricks assets.
Databricks announces Genie One, Genie Agents, and Genie Ontology to help enterprises answer business questions and act on data whose context is scattered across dashboards, queries, documents, tickets, and chats. Genie One connects data and business tools through Lakehouse federation, Lakeflow Connect, native integrations, Slack, Teams, mobile apps, schedules, alerts, document creation, custom skills, and MCP support. Genie Agents evolve Genie Spaces into domain-specific agents that can reason over structured and unstructured data, execute multi-step workflows, and be created from a prompt. Genie Ontology builds a permission-aware living graph from enterprise assets, weighting sources by authority, usage, certification, and freshness. In an internal 28-question benchmark, Genie answered 84.5% correctly on the first attempt and delivered twice the speed of the strongest coding agent.
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