Delphina vs Omni: context layer vs semantic layer for AI analytics

The short answer

Delphina and Omni are both serious AI-native analytics platforms, but they're built on different architectural bets. Omni is a modern BI tool whose distinguishing thesis is that the semantic layer is the trust foundation for AI — every answer flows through a governed semantic model that defines metrics, dimensions, and business logic. Delphina is an AI-managed context layer whose thesis is that the semantic layer is necessary but not sufficient — accurate answers on real enterprise data require the semantic model plus the tribal knowledge that lives outside of it. The practical differences come down to three questions:

1. Semantic layer or context layer? Omni's AI is grounded in a semantic model the team builds and maintains. Delphina's context layer ingests the semantic layer and schema, dashboards, Slack threads, wiki pages, ticket histories, lineage tools, git, CRM, and the institutional knowledge that only lives in your best analysts' heads — then has customer experts validate it via AI-generated evals.

2. What's the team investment? Omni rewards organizations that have invested in semantic modeling and treat it as a first-class practice — typically dbt-heavy teams. Delphina is built so the AI does the heavy lifting of context building and refresh; the team invests in validating outputs, not in cataloging every metric definition by hand.

3. How is accuracy measured in production? In addition to agent-created evals for humans to review, Delphina publishes per-customer accuracy outcomes scored by the customer's experts — including a top media company that lifted answer accuracy from roughly 50% to 95%+ and a major international airline running Delphina across 600+ business users. Omni's evals framework is less publicly detailed; the accuracy claim sits on the trust in the governed semantic model itself.

Pick Omni if you're deep in dbt, you want your BI dashboards and your AI assistant grounded in the same semantic model, and your team is committed to semantic modeling as an ongoing practice. Pick Delphina if your context lives in more places than the semantic layer can capture, if you want AI-managed context maintenance, or if accuracy on messy enterprise data and serving non-technical business users at scale are the top priorities.

Delphina vs Omni at a glance

How Delphina and Omni are architected

How Delphina works

Delphina is an AI-managed context layer for messy enterprise data, built to deliver the highest-accuracy data agents on top of your existing warehouse, semantic layer, and BI tools. The differentiator is what the context layer captures: not just schema and metric definitions from dbt, Cube, Omni, or your own model, but also the tribal knowledge that lives in query logs, source code, dashboards, Slack threads, wiki pages, ticket histories, lineage tools, git commits, and CRM notes — plus the institutional knowledge that only lives in your best analysts' heads.

Delphina learns how those analysts think, validates that understanding against your existing work, and grounds every agent answer in that foundation. Customer domain experts approve, edit, or reject AI-generated context candidates via AI-generated evals. When schemas drift or metrics get redefined, Delphina detects the change, updates, and re-runs the evals. A critic agent reviews every output in real time before it reaches the user; every answer exposes the SQL, the sources consulted, and the reasoning so your team can verify.

Downstream of the context layer sit the agents: deep research for multi-step investigations, proactive monitoring for recurring reports and anomaly detection, data apps for packaged workflows, and an MCP server endpoint that lets external agents — Claude Code, Claude Desktop, ChatGPT, Cursor, and custom internal agents — operate against Delphina's validated context.

Supported integrations. Snowflake, Databricks, BigQuery, Redshift, Postgres, dbt, Cube, Omni, Looker, Tableau, Slack, Notion, Confluence, Jira, GitHub, GitLab, Salesforce, lineage tools, and warehouse-native AI surfaces (Cortex, Genie, Gemini) via MCP.

Pricing. Delphina's pricing aims to align to value and outcomes and typically includes a mix of a platform fee and key usage metrics.

Implementation lift. The Delphina context layer can be operational against your stack quickly — typically days to a small number of weeks for a meaningful first deployment, depending on the breadth of systems being ingested and how much customer-expert validation the team wants to run before going live.

Trust posture: SOC 2 Type II, read-only warehouse access, sandboxed execution with no internet egress, and full audit trail on every query.

How Omni works

Omni is a modern BI tool with AI grounded in a built-in semantic layer. Queries flow through the semantic layer, so the AI uses the same metric definitions, dimensions, relationships, and business logic that power the team's dashboards and reports. The thesis is that the semantic model is the trust foundation: when the model is right, AI answers are governed by definition; when the model is wrong, the team fixes the model and the AI inherits the correction.

Omni's AI is positioned as an agentic system with a coordinator mechanism that plans actions, selects tools, executes queries, evaluates results, and decides what to do next. Teams can add additional context layered on top of the semantic model — instructions like "only use this dataset when asked about closed won deals" — to tune behavior. Omni integrates tightly with the dbt Semantic Layer and treats AI as another interface to the governed model, not as a chatbot bolted on.

Omni was founded in February 2022 in San Francisco by Colin Zima, Jamie Davidson, and Chris Merrick — a team with deep roots at Looker, Stitch, and Talend. The company is well-funded and is building momentum with new investment in 2026; Omni has emerged as one of the more credible voices in the "AI grounded in a semantic layer" camp.

Omni ships an MCP server that sits on top of the semantic layer. External AI assistants — Claude, Cursor, ChatGPT, VS Code — can ask questions of Omni from outside the platform, with queries inheriting the semantic layer's metric definitions, access controls, and business logic. The MCP server exposes a small, focused tool set (pick a model, pick a topic, get data) so the agent traverses the governed model rather than the warehouse directly.

Supported integrations. Snowflake, Databricks, BigQuery, Redshift, Postgres, MySQL, ClickHouse, the dbt Semantic Layer, Claude (connector + skills), Cursor, ChatGPT, and VS Code via MCP.

Pricing. Omni offers per-user pricing across product tiers with enterprise terms negotiated for larger deployments; the company does not publish a public price list.

Implementation lift. SaaS-pointed-at-warehouse is fast, but the semantic-layer-grounded AI is only as good as the semantic model — building a comprehensive model is typically a months-to-quarters undertaking that compounds with the team's analytics-engineering investment.

Where the architectures actually differ

The thirty-thousand-foot architectures share the same instinct — that ungoverned text-to-SQL on a bare warehouse is the wrong abstraction for enterprise AI. The ground-truth differences show up in three places.

What context the AI can reach. Omni's AI reaches the semantic layer and whatever instructions a team has layered onto it. That ceiling is the ceiling of the semantic model. Delphina's context layer reaches the semantic layer and the tribal knowledge that lives outside it — dashboards, Slack threads, wikis, lineage tools, git commits, CRM, query logs, and the institutional knowledge in your analysts' heads. The semantic layer is necessary; the rest is what closes the accuracy gap on real enterprise questions where definitions and history overlap in messy ways.

Who builds context, and how often. Omni rewards organizations that have invested in semantic modeling and treat it as an ongoing practice — building the model is the team's work, and the AI inherits the team's correctness. Delphina is AI-managed: the system generates context candidates, customer experts validate them via AI-generated evals, and the layer refreshes itself as schemas, metrics, and business logic evolve. For teams that can dedicate analytics-engineering hours to semantic modeling indefinitely, the Omni shape works; for teams that can't, the AI-managed shape removes a maintenance burden that compounds.

Where AI lives in the workflow. Omni's AI lives inside the BI product — alongside the dashboards. The natural workflow is "build a dashboard, also ask the AI a question, in the same product." Delphina's AI sits in front of whatever BI tool you already use; the validated context layer powers agents that surface answers through web, MCP, custom agent frameworks, or proactive monitoring — and the team's existing dashboards keep working. For teams that want to keep their BI tool but add AI accuracy on top, Delphina is the additive choice; for teams looking to replace BI with an AI-native product, Omni is the integrated choice.

Delphina vs Omni on accuracy

Accuracy is the question that decides these deals, so it's worth being direct.

Delphina has published the most quotable production accuracy outcome in the category: a top media company that lifted answer accuracy from roughly 50% to 95%+ after deploying Delphina, scored by the customer's own experts, on the customer's own questions. A major international airline runs Delphina in production across 600+ business users. These are real deployments with real measurements, graded by customer experts, not by Delphina.

Omni's accuracy story is grounded in the semantic model — the claim is that AI answers are governed by definition when the semantic layer is correctly built. That's an honest position, and it works well when the semantic model covers the question. The risk shows up when the question depends on context the semantic model doesn't capture — overlapping metric definitions across teams, point-in-time exceptions, business rules that haven't made it into the model yet. Omni doesn't publish per-deployment accuracy outcomes in the way Delphina does; the closest equivalent is the trust an organization places in its own semantic model.

For any AI data agent, including Delphina and Omni, the right answer to "is this accurate enough?" is a proof-of-value (PoV) against your own data, scored by your own experts, against the questions your team actually asks.

Delphina vs Omni on team investment

This is where the architectural bet maps to operational cost.

Omni's shape rewards investment in semantic modeling. A team that has invested in dbt models, metric definitions, and a coherent semantic model gets an AI assistant that inherits all of that work by definition. The work compounds: better model → better AI. The risk is that semantic modeling is itself a years-long undertaking, and the AI's accuracy ceiling is set by how much of the business has been modeled. For organizations with dedicated analytics engineering and a mature semantic-modeling practice, this trade is straightforward.

Delphina's shape minimizes ongoing context investment. Delphina ingests context from the systems where it already lives, generates candidates, has customer experts validate the candidates via AI-generated evals, and refreshes itself when the underlying systems change. The team's work shifts from "model every metric by hand and keep the model fresh" to "validate the most important context once and let the system maintain it." For organizations where analytics engineering bandwidth is a constraint — and that's most enterprises — this is a meaningful change in posture.

Neither shape is wrong. The question is whether your team is positioned to invest in semantic modeling as a core practice, or whether the practical reality is that context lives in too many places and changes too fast for any team to keep up with by hand.

Delphina vs Omni on ecosystem and MCP

Both platforms ingest context from enterprise systems and expose MCP server endpoints. The architectural difference is what each MCP server exposes.

Delphina's MCP server exposes its validated context layer. External agents — Claude Code, Claude Desktop, ChatGPT (via MCP), Cursor, custom frameworks — consume governed answers grounded in customer-validated context, with the critic agent reviewing outputs. One Delphina deployment powers many front-end agents, against the full context layer.

Omni's MCP server exposes the Omni semantic layer. External agents can query Omni from Claude, Cursor, ChatGPT, or VS Code, with queries inheriting metric definitions, access controls, and business logic from the semantic model. The exposed surface is intentionally narrow — pick a model, pick a topic, get data — so the agent stays inside the governed semantic model. Omni also publishes a dedicated Claude connector and Claude skills.

The connector breadth also differs. Delphina ingests from a deliberately wide range of systems where context lives outside the warehouse — Slack, wikis, ticketing, CRM, lineage, git, dashboards, query logs. Omni's connectivity is centered on the warehouse and the semantic layer (with a first-class dbt Semantic Layer integration); tribal knowledge enters Omni by being modeled into the semantic layer.

When Omni is the better fit

Be honest with yourself about the shape of your project. Omni may be the better choice when:

• You're deep in dbt and committed to the semantic layer. Omni's integration with the dbt Semantic Layer is first-class, and the architectural bet rewards teams that treat semantic modeling as a core practice.
• You want BI dashboards and AI grounded in the same governed model. Omni's AI lives inside the BI product, alongside the dashboards your team already uses. One platform, one model, one set of metric definitions.
• Your team has analytics engineering to spare. Building and maintaining a comprehensive semantic model is a years-long undertaking. If your team has the dedicated capacity for it, the work compounds.
• You're looking to replace or augment your existing BI tool with an AI-native option. Omni is positioned as a BI tool first; if you're shopping for a BI tool with the best AI on the market, it's a credible candidate.
• Semantic-layer purity matters to your governance posture. For teams where governance is anchored in "every query goes through the model," the architecture is a direct match.

When Delphina is the better fit

Delphina is the clearer pick when:

• You want the highest accuracy bar in the category. "95%+ in production, measured by the customer's experts" is the shape of Delphina's public evidence — including a top media company that went from roughly 50% accuracy to 95%+.
• Your context lives beyond the semantic layer. Slack threads, dashboards, wikis, lineage tools, git, CRM, query logs, and tribal knowledge in analysts' heads carry the answers to most real enterprise questions; the semantic model is necessary but not sufficient.
• You don't want to model every metric by hand. Delphina's AI-managed context layer reduces ongoing analytics-engineering burden — the team validates context candidates, the system maintains the layer.
• You want to keep your existing BI tool. Delphina sits in front of the BI stack rather than replacing it; existing dashboards, Looker models, Tableau workbooks, and Omni semantic layers continue to work and feed into Delphina's context.
• You want one validated context layer powering many agents. Delphina's MCP server is designed so a single deployment becomes the context backend for Claude Code, Claude Desktop, ChatGPT, Cursor, and any custom framework — across analysts and non-technical users alike.
• You want a critic agent reviewing every output in real time. Delphina's critic agent validates outputs before they reach the user, and every answer exposes SQL, sources, and reasoning for verification.

Vendor questions to ask both

Whether you end up with Delphina, Omni, or another tool entirely, six questions separate production-ready AI data agents from demos.

1. What context does the AI actually reach? A semantic model is a great start; ask what else the AI can see — dashboards, Slack threads, wikis, lineage, git, query logs — and how that context gets captured and refreshed.
2. What's the team-side maintenance burden? Building and maintaining a semantic model is real work. Ask how the system handles refresh when schemas, metrics, and business logic evolve.
3. What's your evals framework, and can I see accuracy from existing customer deployments? Real evals are ongoing, not one-time. Ask for per-customer numbers, not aggregate benchmarks.
4. How do you handle schema drift and metric redefinitions? Ask for a demo of what happens when a column is renamed or a metric definition shifts.
5. What happens when the agent doesn't know? Tools that always answer hallucinate. The right answer includes decline behavior — explicit "I don't have context for this" responses.
6. Can I use this from Claude Code, ChatGPT, Cursor, and my own agent framework? Confirm what each MCP server actually exposes — the platform's semantic model, or a validated context layer that arbitrary agents can build on.

The bottom line on Delphina vs Omni

Delphina and Omni are both serious AI-native analytics platforms, built on different architectural bets that map to different team realities.

For teams deep in dbt with the analytics-engineering capacity to invest in semantic modeling — and a desire to have BI dashboards and AI grounded in the same governed model — Omni is the natural choice. For teams whose context lives in too many places for any semantic model to fully capture, who want AI-managed context maintenance, or who need the highest accuracy bar on messy enterprise data with hundreds of non-technical business users, Delphina is the better fit.

The two often coexist. Delphina ingests Omni's semantic layer alongside dbt, dashboards, Slack, and the rest of the context surface — so the work an Omni-committed team has already done in semantic modeling pays in directly to Delphina's context layer without re-modeling.

The worst choice is to pick either without running a real PoV against your own data. Every production-grade AI data agent deployment starts with that step.

Delphina is an AI-managed context layer for messy enterprise data, built for accuracy on real-world enterprise systems. Book a demo with your data to see the context layer running against your warehouse, your metrics, and your team's questions.

Frequently asked questions

What's the main difference between Delphina and Omni?

Omni is a BI tool whose AI is grounded in a built-in semantic layer; queries flow through the semantic model and inherit its metric definitions and governance. Delphina is an AI-managed context layer that ingests the semantic layer and the tribal knowledge that lives outside it — dashboards, Slack, wikis, lineage, git, CRM, query logs, and what's in your analysts' heads — then validates that context via AI-generated evals reviewed by your experts. The semantic layer is a subset of the context Delphina captures.

Is Delphina an Omni alternative?

Yes, with nuance. If you're shopping for a BI tool with strong AI, Omni is one of the most credible options. If you're shopping for the most accurate AI answers across your entire enterprise data surface — and you want to keep your existing BI tool — Delphina is the better primary, often working alongside an Omni or dbt semantic layer that it ingests as part of its context.

What is Omni's semantic layer?

Omni's semantic layer is the governed model that defines metrics, dimensions, relationships, and business logic in one shared place. The platform's AI, dashboards, and external integrations (including Omni's MCP server) all answer through this model, so queries inherit the same definitions and access controls regardless of how the question is asked. Omni integrates with the dbt Semantic Layer as a first-class source of truth.

Can Delphina work with Omni?

Yes. Delphina is designed to consume from any semantic layer — dbt, Cube, Omni, or your own — so an investment in Omni's semantic model pays in directly to Delphina's context layer. The context layer adds everything that lives outside the semantic model (Slack, wikis, lineage, git, CRM, query logs, analyst tribal knowledge) on top of what Omni already governs.

Does Omni offer enterprise deployment?

Omni offers SaaS deployment with enterprise tiers; the company has been investing in enterprise-grade capabilities as it scales. For specific deployment options — including any VPC or single-tenant configurations — verify directly with Omni at evaluation time.

Does Delphina offer enterprise deployment?

Yes. Delphina runs as SaaS or in a customer-managed VPC, with sandboxed code execution and no internet egress, read-only warehouse credentials, and full audit-grade logging for every prompt and query. The platform holds SOC 2 Type II — table stakes for regulated-industry buyers.

Can I use Delphina with Claude Code?

Yes. Delphina exposes itself as an MCP server, which means it plugs directly into Claude Code, Claude Desktop, ChatGPT (via MCP), Cursor, and any custom agent framework. One Delphina deployment becomes the validated context backend for every front-end agent your team uses.

How accurate is Delphina compared to Omni?

Delphina publishes per-customer accuracy outcomes — 95%+ at a top media company (up from roughly 50% before deployment), plus 600+ business users in production at a major international airline. Omni's accuracy story is grounded in the trust an organization places in its semantic model, which works well when the semantic model covers the question. The right way to compare any two AI data agents is a proof-of-value against your own warehouse, scored by your own experts, on your own questions.

What's the difference between a semantic layer and a context layer?

A semantic layer defines what metrics, dimensions, and relationships mean — the formal definitions that power dashboards and AI. A context layer captures the semantic layer plus everything else AI needs to be accurate on real enterprise data: tribal knowledge, dashboards, lineage, query history, business rules, and the institutional knowledge in analysts' heads. The semantic layer is a subset of the context layer.