// What we automate
Automate data entry between systems
Automating data entry between systems means replacing the manual copy-paste shuffle — spreadsheet to ERP, inbox to CRM, portal to database — with a pipeline that reads from one side, reshapes the records, validates them, and writes them to the other, unattended and logged. It is the least glamorous automation we build and frequently the one with the fastest payback, because the work being replaced is pure overhead that nobody defends.
The tell is a recurring calendar block with a name like “update the sheet”.
What the manual version costs
Easier to measure than most, because the work is usually someone’s named responsibility.
Count the time directly: how long the transfer takes, times how often. Then add the two costs that do not appear on that line:
- Transcription errors. Manual re-keying has a non-zero error rate, always, and errors found weeks downstream cost far more than the original keying.
- Staleness. If the sync happens on Fridays, every decision made on Wednesday used four-day-old numbers. That is usually the expensive part, and nobody attributes it to the process.
Published estimates put the annual cost of manual data entry per employee in the tens of thousands of dollars (Lido), but your own arithmetic will be more persuasive internally than anyone’s benchmark.
What automating it actually involves
Getting the data out. An API if one exists. Otherwise a database connection, a scheduled export, a parseable email report, or — last resort — an agent driving the interface the way a person does. Establish which of these is available before anyone quotes, because it is the single biggest swing factor in the price.
Reshaping it. The unglamorous middle, and where the hours actually go. Field names differ. Dates are formatted three ways. One system allows a blank customer reference and the other does not. Currencies. Duplicates. The transformation rules are the project.
Writing it in, safely. Validation before the write, not after. Idempotency, so a re-run does not duplicate everything. A log of every run showing what moved and what did not.
Where a human still has to stay in the loop
The failure mode that matters here is not a wrong value — it is a silent one.
A pipeline that quietly stops on a Tuesday and is not noticed until month-end has done more damage than the manual process it replaced, because everyone downstream trusted it. So: records that fail validation go to a queue with the reason attached, every run is logged whether or not anything moved, and a failed or suspiciously empty run tells somebody.
That last one is not optional. Most pipeline incidents we have seen are not bad data, they are a job that stopped running and nobody knew.
What it costs and how long it takes
Fixed price after a 30-minute scoping call. What moves the number, in order: whether both systems can be reached programmatically, how complex the transformation rules are, and how much error handling the data justifies.
Six weeks at most to a first working version moving real records, with a working demo every week. You own the code and the accounts.
When not to build a custom pipeline
This is the automation where an off-the-shelf tool most often wins, and we will tell you when it does.
- Two systems, simple mapping, low volume. Zapier, Make or your systems’ native integration will do it in an afternoon for a fraction of the cost. Use them.
- The transfer is rare. A quarterly migration is not a pipeline, it is a task.
- The source data is a mess. Automating a transfer out of a spreadsheet nobody maintains just moves bad data faster. Clean the source first, or automate the cleaning as its own project with its own review queue.
- The two systems are about to be replaced. Building a bridge to something being decommissioned next quarter is a way to spend money twice.
Havoric is an AI automation and web development agency based in Ahmedabad, India, working with clients worldwide. Data pipelines are part of our AI automation work, and they usually ship with a small dashboard showing run history and anything held for review — because a pipeline nobody can see into is a pipeline nobody trusts.
Common questions
- How do you automate data entry between two systems?
- A pipeline reads from the source on a schedule or a trigger, reshapes the records to match the destination, validates them, and writes them across — logging every run so you can see what moved and what did not. Anything that fails validation is held for a person rather than written in broken.
- What if the systems have no API?
- There is usually a route: a database connection, a scheduled export, an email report that can be parsed, or as a last resort an agent driving the interface the way a person would. An integration is more reliable whenever one exists, and the availability of one should be checked before scoping, not during.
- Is this different from Zapier or Make?
- For simple two-system, low-volume flows those tools are often the right answer and we will say so. Custom work earns its cost when the transformation logic is complex, the volume makes per-task pricing painful, the error handling matters, or you need the thing to be yours rather than rented.
- What happens when a record fails to transfer?
- It should never fail silently — that is the failure mode that destroys trust in a pipeline. Failures go to a queue with the record and the reason, the run is logged, and someone is told. A pipeline nobody can see into is one nobody will rely on.
- How long does it take to build?
- Six weeks at most to a first working version moving real records, with a working demo weekly. Most of the time goes on the edge cases in your data rather than on the connection itself.