CEO at Kaelio
How to Build a Context Layer in Minutes, Not Months
By Luca Martial, CEO & Co-founder at Kaelio | Ex-Data Scientist | 2x founder in AI + Data | ex-CERN, ex-Dataiku ·
Every data team that has tried to build a semantic layer knows the pattern. You start with good intentions: standardize metric definitions, create a single source of truth, eliminate the conflicting dashboard problem. Then the project drags on for six months, then nine, then a year. The backlog grows. Engineers burn out. And the business keeps making decisions off competing spreadsheets. According to McKinsey's 2025 survey on data transformation, 65% of data platform initiatives fail to deliver value within the first year, primarily due to implementation complexity. Deloitte's 2025 State of AI in the Enterprise report confirms this pattern, noting that organizations consistently underestimate the time required to prepare data infrastructure for AI. At Kaelio, we built a different approach: an automated context layer that captures schema, lineage, semantic models, dashboard logic, and domain knowledge from your existing stack, in minutes rather than months.
At a Glance
- Traditional semantic layer projects take 6 to 12 months of manual curation, custom integration work, and dedicated engineering headcount. Context layer implementations at other vendors typically run 8 to 16 weeks.
- Kaelio's Deep Context Engine automatically extracts and infers business context from your connected tools, building a governed context layer through a three-phase workflow: Connect, Govern, Activate.
- The Connect phase uses 900+ pre-built connectors spanning warehouses, BI tools, transformation layers, and documentation platforms, eliminating custom integration work entirely.
- The Govern phase auto-generates definitions, surfaces inconsistencies, and presents suggestions for your team to review, approve, reject, or refine.
- The Activate phase exposes the governed context to any AI agent via MCP (Model Context Protocol) or REST API, following the principles outlined in the MCP specification for governed AI data access.
- The system continuously learns from how teams query data, suggesting refinements and new definitions over time without requiring manual maintenance.
Why Traditional Semantic Layer Projects Take So Long
To understand why Kaelio's approach matters, it helps to understand what makes the traditional path so painful.
A typical semantic layer project begins with a scoping exercise. A data architect or analytics engineer audits the existing stack: which tables exist in the warehouse, how metrics are currently defined across dashboards, what business logic lives in SQL views or dbt models, and where the undocumented tribal knowledge resides. This audit alone can take weeks at a company with dozens of data sources. Organizations looking for AI analytics tools that work with dbt models often discover this complexity firsthand.
Next comes the modeling phase. If you are using dbt metrics, this means writing YAML definitions for every metric, dimension, and entity in the MetricFlow specification. If you are using LookML, it means building explores, views, and derived tables in Looker's proprietary syntax. If you are using AtScale or Cube, it means designing a semantic model that maps to your warehouse schema. Each approach requires engineers who are fluent in the specific tool's modeling language.
Then comes the reconciliation phase, which is often the most time-consuming. You discover that "revenue" means something different in the finance team's Tableau dashboard than it does in the sales team's Salesforce report. You find that "active users" has three competing definitions across Mixpanel, the product team's internal dashboard, and the board deck. Each conflict requires cross-functional meetings, stakeholder alignment, and a decision about which definition becomes canonical. This is precisely the kind of problem that governed analytics copilots are designed to solve. A 2024 survey by Monte Carlo Data found that 77% of data teams report metric inconsistency as one of their top three data quality issues.
Finally, there is ongoing maintenance. Source schemas change. New tools get added. Teams create new metrics that bypass the semantic layer entirely. According to Gartner's 2025 research on data and analytics, data teams spend roughly 40% of their time on maintenance and quality tasks rather than generating new insights. The NIST AI Risk Management Framework further emphasizes that AI systems require well-maintained data governance to remain trustworthy over time. A semantic layer built through manual curation inherits this maintenance burden permanently, which is one reason enterprises are looking for AI data analyst software that automates governance rather than adding to the manual workload.
The result? Many organizations abandon the project before it delivers value, or ship a partial implementation that covers only a fraction of their metrics. The semantic layer becomes shelfware, and the conflicting-dashboards problem persists.
The Context Layer: Beyond the Traditional Semantic Layer
Before diving into the implementation guide, it is worth clarifying what distinguishes a context layer from a traditional semantic layer.
A semantic layer maps raw data to business-friendly metric definitions. It tells you that SUM(payments.amount) WHERE status = 'succeeded' AND refunded = false equals "Net Revenue." This is valuable, but it is only one dimension of the context that AI agents and business users need.
For a detailed comparison of these two concepts, see our guide on context layer vs. semantic layer: why you need both for AI. A context layer captures four dimensions of business knowledge:
Schema and Lineage. Where does data originate? How does it flow through your stack? What transformations happen along the way? Understanding lineage is critical for debugging query results and assessing data freshness. Tools like dbt capture transformation lineage within the warehouse, and platforms like Snowflake and BigQuery offer governance features at the warehouse level. But a full context layer tracks lineage across your entire tool ecosystem.
Semantic Models and Metrics. This is the traditional semantic layer territory: metric definitions, dimensions, relationships between entities, and the business logic that governs calculations. The difference is that a context layer infers these definitions automatically from your existing tools rather than requiring you to write them by hand. Without this automated inference, semantic layers alone will not stop AI hallucinations because they lack the broader business context that AI agents need.
Dashboard Logic. Your BI tools contain enormous amounts of encoded business knowledge. Every filter, calculated field, and visualization choice in Tableau, Looker, Metabase, or Power BI represents a decision about how data should be interpreted. A context layer captures this logic so that AI agents can reference established dashboard patterns rather than generating queries from scratch.
Domain Knowledge. Business context does not live exclusively in databases and dashboards. It lives in Confluence pages, Notion docs, Slack threads, and the heads of senior analysts. A context layer ingests and indexes this documentation, connecting informal definitions to formal data models.
Kaelio's Deep Context Engine captures all four dimensions. This is what enables AI agents to answer questions with the full business context, not just raw data, behind their responses. For a deeper exploration of why this matters, see our guide on why every growing company needs a semantic layer.
The Three-Phase Workflow: Connect, Govern, Activate
Kaelio's implementation follows a structured workflow designed to get teams from zero to a governed context layer as quickly as possible. Each phase builds on the previous one, and the entire process is designed to require minimal engineering effort.
Phase 1: Connect
The Connect phase is where you plug in your existing data stack. This is the step that eliminates the months-long integration work that plagues traditional implementations.
Kaelio provides 900+ pre-built connectors that span the full range of tools in a modern data stack:
- Data warehouses and lakes: Snowflake, BigQuery, Databricks, Amazon Redshift
- Transformation layers: dbt Cloud, dbt Core
- BI and visualization tools: Tableau, Looker, Metabase, Power BI, Sigma
- CRM and sales: Salesforce, HubSpot
- Documentation and knowledge bases: Confluence, Notion
- And hundreds more across billing, support, product analytics, engineering, and operations
Each connector is pre-built and maintained by Kaelio. There is no custom integration code to write, no API wrappers to maintain, and no ETL pipeline to configure. You authenticate, select the resources you want to include, and Kaelio begins ingesting context.
What happens during ingestion is where the Deep Context Engine earns its name. For a connected warehouse, it reads schema metadata, table relationships, column descriptions, and query history. For a connected BI tool, it reads dashboard definitions, calculated fields, filter logic, and usage patterns. For a connected transformation layer like dbt, it reads model definitions, tests, documentation, and lineage graphs. For a connected knowledge base, it indexes relevant pages and links them to corresponding data assets.
The Connect phase typically takes less than an hour. The time depends on how many tools you connect and the size of your data estate, but there is no multi-week integration project involved. This stands in stark contrast to traditional approaches where connecting and mapping a single new data source can take weeks of engineering work.
Phase 2: Govern
The Govern phase is where Kaelio's automation meets your team's domain expertise. This is the step that distinguishes a truly governed context layer from a raw metadata catalog.
Once your tools are connected, the Deep Context Engine begins analyzing the ingested context. It performs several key operations:
Auto-generates metric definitions. By examining how metrics are calculated across your dashboards, dbt models, and warehouse queries, Kaelio infers canonical definitions. If "Monthly Recurring Revenue" is calculated in three slightly different ways across your stack, the engine identifies all three variants and proposes a canonical definition based on the most authoritative source.
Surfaces inconsistencies. The engine compares definitions across tools and flags conflicts. For example, it might detect that your Tableau dashboard filters out trial accounts from revenue calculations while your Looker dashboard includes them. Instead of silently picking one, it presents both definitions to your team and asks for a resolution.
Detects undocumented business logic. Many organizations have critical business logic embedded in SQL views, spreadsheet formulas, or BI tool calculated fields that have never been formally documented. The Deep Context Engine surfaces this hidden logic and proposes formal definitions, bringing tribal knowledge into the governed layer.
Maps entity relationships. The engine identifies relationships between entities across tools. It understands that "Account" in Salesforce corresponds to "customer_id" in your warehouse, which maps to "organization" in your product analytics platform. These cross-tool entity mappings are essential for conversational analytics to work accurately.
All of these auto-generated suggestions are presented to your team for review. This is a critical design principle: Kaelio builds, your team reviews and approves. No definition enters the governed context layer without human sign-off. The review interface lets team members approve, reject, or refine each suggestion. Data engineers can validate technical accuracy. Business stakeholders can confirm that definitions match their understanding. Domain experts can add nuance that the automation might miss.
The Govern phase typically takes one to two days for an initial review, depending on the size of your data estate. Compare this to the months of stakeholder meetings and manual documentation that traditional approaches require. And because Kaelio has already done the heavy lifting of identifying definitions and conflicts, your team spends their time on decision-making rather than discovery.
Phase 3: Activate
The Activate phase is where the governed context layer becomes useful to AI agents and applications. This is the step that turns a static catalog into a live intelligence layer.
Kaelio exposes the governed context through two interfaces:
MCP (Model Context Protocol). MCP is an open protocol, originally developed by Anthropic, that enables AI agents to access external data and tools in a standardized way. Kaelio's MCP server makes your governed context layer available to Claude and any other MCP-compatible AI agent. When an agent receives a question like "What was our net revenue retention last quarter?", it queries Kaelio's MCP endpoint to retrieve the governed definition of net revenue retention, the relevant data sources, and any associated business rules before generating a response.
REST API. For custom agents, internal applications, or workflows that do not use MCP, Kaelio provides a REST API that serves the same governed definitions. This enables integration with any programming language, any orchestration framework, and any application architecture. Whether you are building a Slack bot, a custom analytics portal, or an embedded analytics feature for your product, the REST API provides the same governed context that MCP clients receive.
Both interfaces enforce the same access controls. If a user's role restricts them from seeing certain metrics or data sources, that restriction applies regardless of whether they access the context layer through MCP, the REST API, or Kaelio's own interface.
The Activate phase is instantaneous once the Govern phase is complete. There is no deployment step, no infrastructure to provision, and no API keys to manually configure beyond the initial setup. Your governed context is immediately available to any connected agent.
What the Deep Context Engine Actually Captures
To appreciate the depth of Kaelio's automated extraction, it helps to walk through the four context dimensions in more detail.
Schema and Lineage
When you connect a warehouse like Snowflake or BigQuery, the Deep Context Engine reads table schemas, column types, primary and foreign key relationships, view definitions, and stored procedures. When you also connect a transformation tool like dbt, it overlays the transformation graph: which raw tables feed which staging models, which staging models feed which marts, and which marts power which metrics. The result is a complete lineage map from source to consumption that spans your entire stack, not just the warehouse.
This lineage context is what allows AI agents to answer questions like "Where does the 'pipeline' number in the board deck actually come from?" with a precise, auditable answer that traces back through every transformation step. Forrester's research on semantic layer adoption highlights lineage as one of the top requirements enterprises cite when evaluating analytics infrastructure.
Semantic Models and Metrics
The engine examines dbt metric definitions, LookML explores, Tableau calculated fields, Power BI measures, and Metabase custom expressions to build a unified catalog of how your organization defines and calculates business metrics. It identifies canonical definitions, tracks variants, and surfaces conflicts.
Importantly, the engine does not just capture current definitions. It maintains a version history so you can see how a metric's definition has evolved over time and who approved each change. This kind of auditability is central to whether AI analytics tools can be trusted with business metrics. This audit trail is essential for regulated industries that require documentation of how metrics are calculated.
Dashboard Logic
BI dashboards contain a wealth of implicit business logic. Filter defaults, date range selections, conditional formatting rules, drill-down paths, and layout choices all encode decisions about how data should be interpreted and presented. The Deep Context Engine reads this logic from connected BI tools and incorporates it into the context layer.
This matters because AI agents that lack dashboard context tend to reinvent the wheel. They generate queries that ignore established conventions, apply different filter logic than what the team expects, or present data in formats that conflict with existing reports. With dashboard context, agents can align their outputs with established patterns while still offering the flexibility of natural language interaction. This is a key differentiator for teams evaluating conversational analytics solutions for their existing dbt and BI workflows.
Domain Knowledge
Connecting Confluence, Notion, or other documentation platforms allows the Deep Context Engine to ingest written business context: data dictionaries, metric governance documents, onboarding guides, process documentation, and meeting notes that describe how the business thinks about its data.
The engine links documentation to the corresponding data assets, so when an AI agent encounters a metric or entity, it has access to not just the technical definition but also the business rationale, known caveats, and historical context. This is the layer of understanding that separates a genuinely useful AI data agent from one that simply translates English to SQL. It is also why executives are increasingly asking for analytics copilots that can go beyond basic query generation.
Continuous Learning: The Context Layer That Gets Smarter Over Time
A static context layer is better than no context layer, but it degrades over time as the business evolves and new data sources emerge. Kaelio addresses this with a continuous learning loop.
After the initial Connect, Govern, Activate workflow, the Deep Context Engine keeps monitoring your connected tools. It detects schema changes in your warehouse, new dashboards in your BI tools, updated dbt models, and fresh documentation in your knowledge base. When it identifies something new or changed, it generates a suggestion and surfaces it to your team for review.
The engine also learns from query patterns. When users ask questions through connected AI agents, Kaelio observes which definitions get used, which queries succeed, and where confusion arises. If multiple users ask about a concept that does not yet have a governed definition, the system suggests creating one. If a definition consistently leads to follow-up clarification questions, the system suggests refining it.
This creates a virtuous cycle. The more your team uses the context layer, the more accurate and comprehensive it becomes. Traditional semantic layers degrade without active maintenance. Cube's documentation on semantic layer caching illustrates one approach to keeping data fresh, but it focuses on query performance rather than definition governance. Kaelio's context layer improves with use because it governs both the data and its meaning.
Teams that adopt conversational analytics on top of Kaelio's context layer often find that the first week generates a burst of suggested refinements as the system encounters real-world usage patterns. By the second week, the suggestion volume drops as the context layer converges on accurate, stable definitions. By the end of the first month, the context layer is typically more comprehensive and more accurate than any manually curated semantic layer the team has built before.
How This Compares to the Traditional Timeline
To put the time savings in perspective, here is a side-by-side comparison of the two approaches for a mid-sized company with 10 to 15 data sources.
Traditional semantic layer project:
- Scoping and audit (2 to 4 weeks). A data architect inventories all data sources, existing metric definitions, and stakeholder requirements.
- Tool selection and setup (2 to 4 weeks). The team evaluates and configures a semantic layer tool (dbt metrics, LookML, AtScale, Cube, etc.).
- Initial modeling (4 to 8 weeks). Analytics engineers write metric definitions, entity models, and relationship mappings for the highest-priority data sources.
- Reconciliation and alignment (4 to 8 weeks). Cross-functional stakeholders review definitions, resolve conflicts, and agree on canonical metrics.
- Integration and testing (2 to 4 weeks). The semantic layer is connected to downstream consumers and tested for accuracy.
- Rollout and training (2 to 4 weeks). Teams are trained on how to use the new layer and old workflows are migrated.
- Ongoing maintenance (indefinite). Engineers continuously update definitions as the data stack evolves.
Total time to initial value: 16 to 32 weeks (4 to 8 months).
Kaelio's Connect, Govern, Activate workflow:
- Connect (under 1 hour). Authenticate and connect your tools using pre-built connectors.
- Automated extraction (minutes to hours). The Deep Context Engine ingests and analyzes context from all connected tools.
- Govern (1 to 2 days). Your team reviews and approves auto-generated definitions and resolved conflicts.
- Activate (immediate). The governed context layer is exposed via MCP and REST API.
- Continuous improvement (automatic). The system learns and suggests refinements over time.
Total time to initial value: 1 to 3 days.
The difference is not just speed. It is also about who does the work. The traditional approach requires dedicated analytics engineers for months. Kaelio's approach requires your existing team to spend a day or two reviewing and approving suggestions. The engineering effort shifts from building to reviewing, which is a fundamentally different (and lighter) workload.
When to Choose Each Approach
Kaelio's automated approach is ideal for most organizations, but there are situations where each approach makes more sense.
Choose Kaelio's automated approach when:
- You want to be operational in days, not months
- Your team's analytics engineering capacity is limited or fully allocated
- You need context that spans your entire tool stack, not just the warehouse
- You plan to expose your context layer to AI agents via MCP or REST API. See our guide on how to choose an analytics copilot you can actually trust for evaluation criteria
- You want continuous improvement without manual maintenance
- You need to support non-technical users with natural language access to governed data
Consider a traditional approach when:
- You have a large analytics engineering team with available capacity and a preference for code-defined models
- Your semantic layer needs to function as a query engine (e.g., Cube's pre-aggregation layer for high-concurrency workloads)
- You are already deep into a dbt metrics or LookML implementation and need to complete, rather than replace, that project
In practice, many organizations use both. Kaelio can connect to your existing dbt semantic layer and enrich it with the additional context dimensions (dashboard logic, domain knowledge, cross-tool lineage) that traditional tools do not capture. This is not an either-or decision.
Getting Started: A Practical Checklist
If you are ready to build a context layer with Kaelio, here is a practical checklist to prepare for each phase.
Before you Connect:
- Inventory the tools in your data stack. Which warehouses, BI tools, transformation layers, and documentation platforms does your team use?
- Identify the admin credentials or service accounts you will need for each tool. Kaelio's connectors require read-only access in most cases.
- Decide which data sources to include in the initial scope. You can always add more later, so start with your highest-impact sources. If you use Snowflake, see our guide on the best AI data analyst tools for Snowflake users. For Looker environments, see conversational analytics for Looker users.
Before you Govern:
- Identify 2 to 3 team members who will review auto-generated definitions. Ideally, include at least one data engineer (for technical accuracy) and one business stakeholder (for business context).
- Document any known metric conflicts or definition inconsistencies ahead of time. This accelerates the review process.
- Set expectations with stakeholders. The Govern phase is a review exercise, not a modeling project.
Before you Activate:
- Decide which AI agents or applications will consume the context layer. Will you use MCP-compatible agents like Claude? Custom agents via the REST API? Both?
- Configure access controls. Determine which roles should have access to which metrics and data sources through the context layer.
- Plan a pilot. Start with a small group of users or a single use case (e.g., revenue team analytics) and expand from there.
Frequently Asked Questions
How long does it take to build a context layer with Kaelio?
Most teams complete the initial Connect phase in under an hour thanks to 900+ pre-built connectors. The Govern phase, where your team reviews and approves auto-generated definitions, typically takes a day or two depending on the size of your data stack. By contrast, traditional semantic layer projects take 6 to 12 months of manual engineering work, and context layer implementations at other vendors typically run 8 to 16 weeks.
What is the difference between a semantic layer and a context layer?
A semantic layer maps raw data to business-friendly metric definitions, typically within a single tool like dbt or LookML. A context layer goes further by capturing four dimensions of business knowledge: schema and lineage, semantic models and metrics, dashboard logic, and domain knowledge from across your entire tool stack. This richer context is what AI agents need to generate accurate, trustworthy answers rather than simply translating English to SQL. For teams evaluating tools in this space, see our guide to the best AI analytics tools for governed data.
Does Kaelio replace my existing BI tools or data warehouse?
No. Kaelio sits on top of your existing stack and connects to it via 900+ pre-built integrations. It reads from your warehouse, BI tools, transformation layer, and documentation to build a governed context layer. Your existing tools continue to function exactly as before. For more on how Kaelio complements existing BI, see our guide on the best AI analytics tools that sit on top of existing BI.
How does Kaelio expose the context layer to AI agents?
Kaelio exposes the governed context layer via two interfaces. MCP (Model Context Protocol) provides native integration with Claude and other MCP-compatible AI agents. A REST API enables integration with any custom agent, application, or workflow. Both interfaces serve the same governed definitions and enforce the same access controls.
What happens after the initial setup? Does the context layer stay current?
Yes. Kaelio continuously monitors your connected tools for schema changes, new metrics, and definition drift. It learns from how teams query data and suggests new or refined definitions over time. Your team reviews and approves these suggestions through the same Govern workflow used during initial setup, keeping the context layer accurate and current without manual maintenance.
Sources
- https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights
- https://www.montecarlodata.com/blog-the-state-of-data-quality-2024/
- https://www.gartner.com/en/information-technology/topics/data-and-analytics
- https://www.gartner.com/en/articles/data-and-analytics-trends
- https://docs.getdbt.com/docs/build/metrics-overview
- https://docs.getdbt.com/blog/dbt-semantic-layer-whats-next
- https://cloud.google.com/looker/docs/what-is-lookml
- https://www.atscale.com/solutions/universal-semantic-layer/
- https://modelcontextprotocol.io/introduction
- https://www.anthropic.com/news/model-context-protocol
- https://www2.deloitte.com/us/en/insights/focus/cognitive-technologies/state-of-ai-and-intelligent-automation-in-business-survey.html
- https://www.nist.gov/artificial-intelligence/executive-order-safe-secure-and-trustworthy-artificial-intelligence
- https://www.forrester.com/report/the-semantic-layer-is-reborn/RES180365
- https://docs.snowflake.com/en/user-guide/data-governance
- https://cloud.google.com/bigquery/docs/column-data-masking-intro
- https://cube.dev/docs/product/caching