> ## Documentation Index > Fetch the complete documentation index at: https://docs.drpn.ai/llms.txt > Use this file to discover all available pages before exploring further. # Darpan documentation > Product, API, backend, release, and engineering docs for Darpan. Darpan is a reconciliation workspace. It connects source systems, describes source data with schemas, executes reconciliation runs, applies RuleSets, and preserves the output needed to explain each difference. Most of the work in Darpan is front-loaded: connections, schemas, and a saved run are set up once and reused. The docs follow that shape — set up your sources, build a run, then review output and handle the admin around it. If this is your first time, the quickstart walks the full path end to end. One complete path from environment selection to result review. Manage one tenant's settings, connections, saved runs, automations, and results. ## Set up sources Store the SFTP file-access connection that runs reuse. Save the NetSuite connection used to pull source records. Store the Shopify Admin API connection used by runs and automations. Store the HotWax connection used by runs and automations. Configure the AI provider — a Darpan admin task you do once per provider. ## Build a run Define source data shapes before a run depends on the fields. Save the source, schema, and primary-ID setup for repeated runs. Build the field comparisons that decide when systems line up. Execute a saved or newly configured comparison. Interpret generated differences and choose the next action. ## Admin & alerts Set the tenant timezone and register Google Chat spaces. Grant tenant access and assign roles. Schedule a saved reconciliation run, or run it on demand. Get a Google Chat message when a run finishes flagging differences. ## Find your way around The handful of terms every Darpan screen assumes you know. Open pages, setup records, saved runs, and generated outputs from one search surface. ## Run Darpan yourself Clone repositories, install dependencies, start the local stack, and verify sign-in. The contexts Darpan runs in, from local development to the hosted app. Recover from common JDK 21, Spark, and clone failures. ## For builders The engineering docs explain how the product surface maps to the backend engine: Moqui service contracts, tenant-scoped data access, Spark comparison, Drools RuleSets, and generated output. Runtime architecture behind Darpan runs. Repo boundaries and verification model used for Darpan development. Call the JSON-RPC services behind setup, execution, and review. Shipped tags and their major changes. Login, upload, connection, and run failure checks.