// EXECUTION_PIPELINE

Implementation Pipeline

Our engine handles the complexity of data movement while you focus on high-level decision logic.

PHASE_1 // Incremental Bulk Ingestion

The salesforce_connector utilizes the SFDC Bulk API v2 to incrementally sync standard objects (Accounts, Opportunities) and custom objects into a columnar Parquet format without hitting API rate limits.

PHASE_2 // Cross-Platform Entity Resolution

Arcli automatically resolves the messy reality of B2B sales: merging HubSpot Contact IDs with Salesforce Lead IDs and mapping them to parent Account hierarchies.

PHASE_3 // Semantic RevOps Layer

Define what 'Qualified Pipeline' actually means to your business in Arcli's governance layer. The AI agent strictly adheres to this definition, preventing hallucinated calculations.

PHASE_4 // Automated Slack Watchdogs

Deploy an agent to monitor the semantic layer. If next quarter's pipeline generation drops below the historical 30-day moving average, it instantly alerts the VP of Sales in Slack.

// STRATEGIC_SCENARIO

Deep Data Retrieval

How Arcli grounds AI in your exact schema to generate highly-optimized, dialect-specific execution logic.

The Engine Room: Cross-Platform Pipeline Velocity

To calculate true Pipeline Velocity, Arcli bridges the gap between Marketing (HubSpot) and Sales (Salesforce) dynamically.

THE EXECUTIVE FILTER (ROI)

Allows Go-To-Market leaders to instantly pivot marketing spend toward the campaigns that generate the fastest revenue, bypassing the need for complex Data Warehouse ETL pipelines.

  • Fully optimized for DuckDB SQL (Embedded Execution) constraints.
  • Bypasses semantic layer hallucinations via strict schema grounding.
DuckDB SQL (Embedded Execution)_COMPILE
-- Generated by Arcli AI Semantic Router
WITH hubspot_attribution AS (
    SELECT 
        hs_contact_id,
        email,
        recent_conversion_event_name AS campaign_source
    FROM tenant_workspace.hubspot.contacts
    WHERE createdate >= CURRENT_DATE - INTERVAL 180 DAY
),
salesforce_opps AS (
    SELECT 
        o.id AS opportunity_id,
        c.email AS primary_contact_email,
        o.amount,
        o.is_won,
        DATE_DIFF('day', o.created_date, o.close_date) AS days_to_close
    FROM tenant_workspace.salesforce.opportunities o
    LEFT JOIN tenant_workspace.salesforce.opportunity_contact_roles ocr ON o.id = ocr.opportunity_id
    LEFT JOIN tenant_workspace.salesforce.contacts c ON ocr.contact_id = c.id
    WHERE o.is_closed = TRUE AND o.created_date >= CURRENT_DATE - INTERVAL 180 DAY
),
unified_pipeline AS (
    SELECT 
        s.opportunity_id, s.amount, s.is_won, s.days_to_close,
        COALESCE(h.campaign_source, 'Outbound / Unattributed') AS lead_source
    FROM salesforce_opps s
    LEFT JOIN hubspot_attribution h ON LOWER(s.primary_contact_email) = LOWER(h.email)
)
SELECT 
    lead_source,
    COUNT(opportunity_id) AS total_opportunities,
    ROUND(SUM(CASE WHEN is_won THEN 1 ELSE 0 END) * 100.0 / COUNT(opportunity_id), 2) AS win_rate_pct,
    ROUND(AVG(CASE WHEN is_won THEN amount ELSE NULL END), 0) AS avg_deal_size_usd,
    ROUND(AVG(CASE WHEN is_won THEN days_to_close ELSE NULL END), 1) AS avg_sales_cycle_days,
    -- Pipeline Velocity ($/Day)
    CASE 
        WHEN AVG(CASE WHEN is_won THEN days_to_close ELSE NULL END) = 0 THEN 0
        ELSE ROUND((COUNT(opportunity_id) * (SUM(CASE WHEN is_won THEN 1 ELSE 0 END)::FLOAT / COUNT(opportunity_id)) * AVG(CASE WHEN is_won THEN amount ELSE NULL END)) / AVG(CASE WHEN is_won THEN days_to_close ELSE NULL END), 0)
    END AS pipeline_velocity_per_day
FROM unified_pipeline
GROUP BY 1 HAVING COUNT(opportunity_id) > 5
ORDER BY pipeline_velocity_per_day DESC;