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Deterministic Natural Language to SQL

Stop relying on generic AI models that hallucinate database tables. Arcli utilizes a Schema-Grounded Compiler to translate conversational intent into production-grade, dialect-perfect SQL instantly.

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Core Capabilities

The technological foundation behind the unified engine. Designed to completely bypass manual RevOps bottlenecks.

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Strategic Deployment

Real-world orchestration patterns deployed by our top enterprise partners.

Instant Financial Auditing

Calculate Net Revenue Retention (NRR), blended CAC, and Daily Active Users (DAU) without writing complex date-interval logic or cohort matrices.

Live Inventory & Cart Analytics

Analyze cart abandonment drop-offs, LTV by acquisition channel, and SKUs with low turnover using natural language.

Event-Driven Telemetry

Unnest JSON event payloads to track user feature adoption funnels instantly, without waiting on data engineers for ETL pipelines.

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Expert Insights

Everything you need to know about implementing Arcli's engine into your stack.

How does the Schema-Grounded Generation Engine physically prevent LLM hallucinations?

Instead of dumping your entire schema into a prompt, Arcli creates a vector embedding of your metadata. Upon querying, we execute a similarity search to inject only the exact 3-5 relevant tables (and their foreign keys) into the compiler's bounded context.

Does Arcli exfiltrate our actual database row data to OpenAI/Anthropic?

Absolutely not. Arcli operates a strict Zero-Copy Architecture. The LLM only processes DDL metadata (table names, types). Execution happens securely via Read-Only roles in your VPC. Row-level data never reaches an LLM.

Can a non-technical employee accidentally delete a production table?

Impossible. We mandate Read-Only database credentials. Furthermore, our execution orchestrator utilizes strict Abstract Syntax Tree (AST) validation to strip and block any destructive SQL commands (DROP, ALTER, DELETE).

What happens if a user asks a highly ambiguous, mathematically impossible question?

Arcli acts as a defensive proxy. If the schema graph cannot resolve the user's intent definitively, it pauses and triggers a clarification prompt asking the user to specify metrics, rather than hallucinating bad data.

Does the AI understand our highly specific internal business acronyms?

Yes. Through Arcli's Semantic Metric Governance, you define what "Active MQL" means in SQL once. When a user asks about it, the compiler rigidly injects your predefined, deterministic CTE block into the generation.

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