Every hour your team spends copying data between systems is an hour they're not doing the job they were hired for.
Manual data entry is one of the most expensive operational inefficiencies in growing businesses — not because the data entry itself is expensive, but because it introduces error, creates delay, and consumes skilled team members' time on a task that should be automated. We eliminate manual data transfer with purpose-built integration infrastructure.
Someone on your team spends several hours per week copying data from one system into another — from your CRM into your billing system, from your project management tool into your invoice generator, from supplier emails into your inventory system.
Manual data entry persists in businesses that have outgrown their original toolset but haven't yet invested in the integration infrastructure that would automate the data flows. The pattern is consistent: System A is the source of truth for a category of data. System B needs that data to perform its function. There's no direct integration between System A and System B, so someone manually copies the data between them.
The immediate cost is obvious: the time to do the manual transfer. But the second-order costs are higher. Manual data entry has an error rate — different studies put it between 1% and 4% for experienced data entry workers. In a business where data errors have downstream consequences (incorrect billing, wrong inventory levels, incorrect customer records), the error remediation cost can exceed the original data entry cost.
The third-order cost is the most significant: the person doing the manual data entry is usually not a data entry operator. They're a sales ops person, an account manager, an operations coordinator, or a finance analyst. They were hired to apply judgment, build relationships, or make decisions — not to copy numbers between spreadsheets. Every hour of manual data work is an hour of skilled work not done.
Zapier and Make solve the easy version of this problem — when both systems have pre-built connectors and the data mapping is simple. They don't solve the complex version: custom business logic in the transformation, conditional routing based on data values, error handling for invalid data, or systems without pre-built connectors.
Automated data flows that eliminate the manual transfer, reduce error rate to near-zero, and give the team members who were doing the manual work back to work that actually requires their skills.
Data pipeline design
We map the exact data flow: source system, data model in the source, transformation logic, destination system, data model in the destination, and error conditions. The map is the specification for the automation.
Custom transformation logic
Business-specific data mapping that goes beyond field renaming: calculated fields, conditional routing, data validation, deduplication, and enrichment from additional sources.
Bidirectional sync where required
Some data flows are unidirectional; others require bidirectional sync with conflict resolution. We implement the sync logic that's appropriate for the specific data relationship.
Error handling and reprocessing
Invalid data is captured and flagged for human review rather than silently dropped or incorrectly transformed. The operations team sees exactly which records need attention without having to check whether the automation worked.
Audit trail
Every automated data operation is logged with the source record, the transformation applied, the destination record, and the timestamp. The audit trail supports debugging, compliance, and dispute resolution. Built on Node.js queue workers, Postgres, TypeScript, with API integrations to your specific source and destination systems.
One honest number to start.
Fixed-scope, fixed-price. The number below is the starting point — final scope is built from your brief.
Automated data flows that eliminate the manual transfer, reduce error rate to near-zero, and give the team members who were doing the manual work back to work that actually requires their skills.
Three steps, every time.
The same repeatable engagement on every project. No surprises, no mystery, no billable ambiguity.
Brief & discovery.
We send you questions, then get on a call. Output: a written scope with every step, feature, and integration listed.
Build & ship.
Fixed schedule, weekly reviews. No scope creep unless you change the scope — and if you do, we reprice it transparently.
Warranty & retainer.
30-day warranty on every launch. Most clients stay on a monthly retainer for ongoing features and maintenance.
Why Fixed-Price Matters Here
Automation projects have a defined ROI: hours saved per week × team cost per hour × weeks per year. A fixed-price build evaluated against that ROI calculation is the right commercial structure. Fixed scope, fixed price.
Related engagements.
Your team is running a business process on a spreadsheet. That breaks at scale — every time.
Read more02Your business runs on API integrations that fail silently and break at the worst possible time.
Read more03You've tried every SaaS tool in the category. None of them fits how your business actually works.
Read moreQuestions, answered.
Zapier is the right tool when: both systems have native Zapier connectors, the data mapping is simple (field to field, same types), error handling requirements are low (you're OK with errors being flagged manually), and the volume is below Zapier's limits. Custom development is the right tool when any of those conditions don't hold.
Source data quality problems (missing required fields, invalid formats, duplicates) need to be addressed in the automation pipeline or at the source. The automation can include validation logic that flags invalid records for human review before they're written to the destination system. Fixing the source data quality is typically a separate project.
Both patterns are possible. Event-driven automation (triggered by a webhook when source data changes) produces near-real-time sync. Scheduled batch automation (runs every hour or every night) is appropriate when real-time sync isn't required. The right pattern depends on the business requirement.
A custom data automation pipeline with business logic, error handling, and audit logging across 2–4 systems typically runs $25k–$45k. Fixed-price.
6 to 10 weeks for a production automation pipeline from requirement definition to live operation.
Tell Ryel about your project.
Describe what you’re building and what outcome you need. You’ll have a written, fixed-price scope within the week.