Dynamic AI vs. The LookML Bottleneck
Looker requires months of engineering to define its semantic layer in LookML before anyone can ask a question. Arcli maps your schema dynamically at query time using AI.
Deep Data Retrieval
How Arcli grounds AI in your exact schema to generate highly-optimized, dialect-specific execution logic.
The Information Gain: Hardcoded LookML vs. AI Inference
In Looker, simply defining a user's lifetime value requires creating a new 'view', defining 'dimensions', and writing 'measures' in a proprietary YAML-like syntax. With Arcli, the AI infers the relationships directly from your warehouse constraints.
Data engineers spend less time writing boilerplate metadata definitions and more time optimizing actual data pipelines.
- Fully optimized for yaml constraints.
- Bypasses semantic layer hallucinations via strict schema grounding.
# Traditional LookML (Requires Dev Pipeline)
view: users {
dimension: user_id { primary_key: yes, type: number, sql: ${TABLE}.id ;; }
measure: lifetime_value { type: sum, sql: ${TABLE}.total_spent ;; }
}
# Arcli AI (Zero Config - Inferred from DDL & NLP)
User Prompt: "Show me the sum of lifetime value by user cohort."
Arcli Agent Action:
1. Reads DB schema (PK/FK relationships)
2. Writes standard SQL using SUM(total_spent)
3. Renders result instantly.Strategic Deployment
Real-world orchestration patterns deployed by our top enterprise partners.
Looker for Tier-1 Financials
Use Looker strictly for board-level metrics that require absolute, version-controlled governance and formal audit trails.
Arcli for Operational Agility
Give product, sales, and marketing teams Arcli to run ad-hoc queries and daily telemetry exploration independently, relieving the LookML engineering queue.
Related Resources
Semantic Routing AI
How Arcli handles governance without LookML.
BigQuery Integration
Seamless connection to GCP.
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Get started with Arcli today.