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Datalab Project Offer and Pipeline Summary

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Key
datalab_project_offer_and_pipeline_2026_03_20
Source
contextkeep
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none
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Created
2026-03-20 16:44
Updated
2026-03-20 16:44
Doc Version
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automation business contextkeep csv datalab homelab monetization offer pipeline
Project reviewed on 2026-03-20 by connecting to host `datalab` (192.168.4.111) as `svc-admin`. Current technical state: - Primary project path: `~/pipelines` - Secondary/simple test path: `~/pipelines_v2` - Main script: `~/pipelines/scripts/process_reports.py` - Inputs: - `~/pipelines/inputs/sales_export.csv` - `~/pipelines/inputs/refunds_export.csv` - Output: - `~/pipelines/outputs/consolidated_report.csv` - Logs: - `~/pipelines/logs/pipeline.log` - `~/pipelines/logs/cron.log` - Automation: - cron runs daily at 06:00 - crontab entry: `0 6 * * * /home/svc-admin/pipelines/scripts/process_reports.py >> /home/svc-admin/pipelines/logs/cron.log 2>&1` - Quarantine path for bad inputs: - `~/pipelines/processed/quarantine` What the current pipeline does: - Reads two CSV exports with different schemas: - sales export uses columns like `order_id`, `order_date`, `customer`, `amount_usd` - refunds export uses columns like `refund_id`, `ref_date`, `customer_name`, `refund_amount` - Validates required columns for sales input. - Normalizes multiple date formats into `YYYY-MM-DD`. - Maps both inputs into one normalized schema: - `type` - `date` - `customer` - `amount` - Converts refunds into negative amounts. - Writes a consolidated CSV report. - Logs success/failure. - On schema or processing failure, moves input files into quarantine. Plain-English explanation: - This project is a small business data-cleaning / reconciliation pipeline. - It is not web hosting and not an AI product in its current form. - It takes two ugly recurring exports from different systems and turns them into one clean report. Business interpretation / marketable angle: - Best framing: `recurring CSV reconciliation and reporting automation for small businesses` - Sell the outcome, not the script. - Core promise: - `Send me exports from system A and system B, and you get one clean report every day or week without manual spreadsheet work.` Ideal first customers: - small e-commerce sellers - bookkeeping/accounting-adjacent small businesses - operations-heavy small businesses - owner-operators or office managers manually reconciling exports in Excel - any small team reconciling sales, refunds, orders, payments, or inventory across two systems Recommended first offer: - A narrow service, not a platform. - One workflow only: - 2 recurring input files - 1 scheduled output report - fixed schema mapping - logging and bad-file handling - Best initial positioning: - `I automate recurring spreadsheet/report cleanup between two systems so you stop manually reconciling exports.` Suggested pricing discussed: - Fast-first-money path: - one-time setup: `$100-$300` - optional monthly support: `$25-$100` - More standard productized service path: - setup fee: `$500-$1,500` - monthly support/hosting: `$149-$399/month` - Higher-value business-critical workflows could justify more later. Practical timeline discussed: - 1-2 weeks to make the existing demo into a presentable micro-offer. - 2-6 weeks to land a first small paying customer if actively pitched. - First `$100` is plausible sooner if an existing contact has spreadsheet pain. Strategic conclusion: - `datalab` should be treated as the seed of a productized back-office data automation service. - Do not position it as generic web hosting. - Do not wait to build a full SaaS before selling. - The fastest path is to sell one narrow recurring automation outcome to one real customer, then iterate. --- **2026-03-20 04:06:00 UTC | AI Update via MCP** --- **2026-03-20 16:44:55 UTC | Created via MCP**

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