3-Day Spreadsheet Jobs.Done in Under 10 Minutes.
Most operations teams spend days pulling files, running VLOOKUPs, applying business rules, and formatting reports before anyone sees a number. We replace that entire process with an automated pipeline that runs on a schedule, applies your logic, and delivers clean output to the right people without anyone touching a spreadsheet.
Every problem. Directly solved.
Manual data processing is slow, inconsistent, and fragile. Here's what changes after the pipeline goes live.
The pipeline runs on infrastructure you own. All source credentials, business logic, and output data stay in your environment. No third-party platform sits between your data and your reports. If we part ways, the pipeline keeps running.
What We Build
Eight components covering the full pipeline from source ingestion to final delivery. Every step runs automatically after deployment.
Multi-Source Data Ingestion
Connects to spreadsheets, databases, APIs, FTP servers, email attachments, and cloud storage. All sources unified into one pipeline with no manual downloads or copy-paste between files.
Business Logic Layer
Applies your rules automatically: deduplication, lookups, conditional formatting, currency conversion, date normalization, and anything else your team currently does by hand. The logic lives in code, not tribal knowledge.
Data Merging & Joining
Combines datasets that previously required manual VLOOKUP chains or copy-paste across tabs. Any number of sources merged on any key, reconciled, and validated before output.
Scheduled Execution
Runs on a schedule you define: hourly, daily, weekly, or triggered by a file drop or API call. No one presses a button. The pipeline starts, runs, and delivers without supervision.
Clean Output Delivery
Outputs formatted spreadsheets, PDFs, database records, or API payloads — wherever the data needs to go. Formatted consistently every time, with no version-naming or manual export.
Automated Distribution
Sends finished reports to the right people on the right schedule. Email, Slack, shared drive, or direct upload to another system. Recipients get the file without anyone remembering to send it.
Data Validation & Error Alerts
Checks incoming data against expected ranges and formats before processing. If something is wrong — missing fields, unexpected values, failed source — the pipeline flags it and alerts your team instead of silently producing bad output.
Audit Trail & Run History
Every pipeline run is logged with source snapshots, row counts, transformation steps, and output hashes. If a number is ever questioned, you can trace it back to the raw data.
5 Weeks from Kickoff to Live Pipeline
Walk through every manual step in your current process. Map all data sources, transformation rules, output formats, and distribution recipients. Identify the three slowest steps first.
Build source connectors for each input. Stand up the pipeline runtime and configure scheduling. Validate that raw data arrives clean before any transformation work begins.
Implement all business logic in code. Merges, lookups, filters, calculated fields, conditional rules. Run against historical data to verify the output matches what your team produced manually.
Build output formatters and delivery routes. Configure email, Slack, or file drop destinations. Full end-to-end test run with real data and real recipients in a staging environment.
Deploy to production. First live run with team present. Walkthrough of monitoring, error alerts, and how to adjust schedules or outputs. Retainer begins for ongoing additions.
One Build. No Manual Processing.
Pipeline builds are priced as a one-time build fee based on source complexity and transformation depth, followed by an optional monthly retainer for new sources, logic updates, and monitoring.
If one analyst spends 3 days per week processing data at $30/hr, that's $1,440/month in labor just to run reports. At $1,200 to start, this pays for itself in the first month and reclaims over 140 hours a year of skilled analyst time.
Is This Right for Your Team?
This build is a strong fit if your team runs the same data process repeatedly and that process takes longer than it should every single time.
- A recurring process that takes more than a few hours per run
- Data pulled from more than one source or system
- Business logic that lives in someone's head or a shared spreadsheet
- Output that looks different depending on who ran it
- Reports that go out late because the person who runs them is busy
Stop Paying Staff to Process Spreadsheets
Tell us what process takes the most time. We'll scope the pipeline in 30 minutes.