Stop Building One-Off Dashboards.
Business teams wait weeks for a simple data pull. Data engineers drown in Slack requests. Arcli breaks the cycle by generating Read-Only SQL directly against your warehouse, enabling true self-serve analytics at the speed of thought.
"Dashboards are static debt. Stop treating them like products."
If a dashboard doesn't answer a stakeholder's *next* question, it has failed. Bypassing rigid UI constraints for a dynamic, AI-orchestrated query layer is the only way to scale organizational data literacy.
Implementation Pipeline
Our engine handles the complexity of data movement while you focus on high-level decision logic.
State your goal
Input "Track our new product launch metrics." The system automatically selects relevant tables.
AI renders the layout
The engine bypasses SQL compilation entirely, rendering a multi-chart dashboard instantly.
Instant iteration
Ask to tweak a specific chart. The UI updates instantly to reflect your new filters or formatting.
Deep Data Retrieval
How Arcli grounds AI in your exact schema to generate highly-optimized, dialect-specific execution logic.
Complex Supply Chain Gap Analysis
Ad-hoc requests often involve complex temporal gaps. Arcli generates the exact dialect-specific window functions required to surface these anomalies instantly.
Instantly surfaces high-velocity stockout risks where sales surged >25% while inventory dipped below 50 units, allowing Ops to reorder before the next day starts.
- Fully optimized for PostgreSQL constraints.
- Bypasses semantic layer hallucinations via strict schema grounding.
-- Generated by Arcli Semantic Orchestrator
WITH daily_inventory AS (
SELECT
DATE_TRUNC('day', recorded_at) AS metric_date,
product_id,
MIN(inventory_level) AS stock_on_hand,
SUM(units_sold) AS sales_volume
FROM warehouse.inventory_logs
WHERE recorded_at >= CURRENT_DATE - INTERVAL '90 days'
GROUP BY 1, 2
),
temporal_variance AS (
SELECT
*,
LAG(sales_volume, 1) OVER (PARTITION BY product_id ORDER BY metric_date) AS prev_day_sales
FROM daily_inventory
)
SELECT
metric_date,
p.product_name,
stock_on_hand,
sales_volume,
ROUND(((sales_volume - prev_day_sales) / NULLIF(prev_day_sales, 0)) * 100, 2) AS WoW_surge_pct
FROM temporal_variance tv
JOIN warehouse.products p ON tv.product_id = p.id
WHERE stock_on_hand < 50
AND sales_volume > prev_day_sales * 1.25
ORDER BY sales_volume DESC;Architecturally impossible to mutate your production data.
Arcli operates on a strict Read-Only security model. We generate the execution logic, but your warehouse executes the compute. Your data never leaves your VPC.
Read-Only RBAC Enforcement
Arcli operates via strictly provisioned Read-Only service accounts, natively inheriting your database’s Row Level Security (RLS) so users only see authorized data subsets.
Zero Row-Level Data Ingestion
We only index metadata (DDL) for semantic routing. Your proprietary row-level data stays in Snowflake/Postgres and is NEVER used for foundational model training.
Expert Insights
Everything you need to know about implementing Arcli's engine into your stack.