System integration means your CRM, ERP, billing and internal tools exchange data automatically instead of through people copying between them. We map the fields and rules first, wire the systems with APIs, webhooks and queues, then run the syncs with retries, idempotent handling and alerts — so a failed sync is noticed the same minute, not discovered a month later.
Staff move data by hand between systems that should just talk to each other.
Brittle one-off scripts break quietly and no one notices until it hurts.
The same record says different things in different systems.
When a sync fails, there’s no alert and no trail to debug it.
We model the data, fields, and rules across every system involved.
APIs, webhooks, and queues wire the systems together.
Data syncs with transforms, retries, and idempotent handling.
Dashboards and alerts catch failures before they spread.
Every field, transform and default written out as a table your team can review, rather than buried in code only we can open.
A failure is queued, retried with backoff and surfaced as an alert with the payload attached, instead of dissolving into a log.
Anything that creates orders, invoices or payments is keyed, so a retry cannot double it.
Throughput, error rate, lag and the last successful run for each integration, on one page.
Credentials for both systems and, where one exists, a test environment. Building straight against production is possible but slower.
Half an hour each with the people who know why the data looks the way it does saves weeks of guessing.
Which system wins when the two disagree. That is a business decision, not a technical one, and it cannot be deferred.
If a native integration already covers your case, use it — we will say so during the mini-audit.
A one-off data migration is a different job, usually cheaper as a script than as an integration.
We do not build against systems with no API and no supported export: scraping a vendor interface breaks on their next release.
Each page states the workflow, the systems it integrates with, what it costs and when it is the wrong choice.
Reads incoming invoices, checks totals, tax and supplier against the ERP, and posts only what clears the rules.
Watches tender portals for matching notices, extracts the requirements and flags the ones worth bidding on.
Pulls figures from the accounting stack into a recurring report, with variance explained in plain language.
from order to shop floor
Read the case →shipments on time
Client under NDA. The figures are the client’s own, comparing the periods before and after launch.
This is the project behind the case above, described the way it happened. A serial manufacturer, around 200 people, one site. Orders arrived into a spreadsheet, material requirements were worked out by hand from a bill of materials kept in another spreadsheet, against stock figures that were a day or two old. Every re-keying cost a day: an order reached the job list in three to five days, batches stalled waiting for a part nobody had ordered, and roughly every third order shipped late.
The order and its specification are entered once and linked to the bill of materials, so the composition of a product stops living in a file on somebody’s machine.
The requirement is calculated against actual stock and open purchase orders rather than against a figure from two days ago.
The job list is built from what is backed by material and what has to move. The production manager approves it or changes it. Order to job list went from three to five days down to hours.
Material is written off against actual output rather than once a week, and stock stopped drifting away from the accounting system.
The manager and the customer can both see where an order stands, and nobody walks onto the floor to ask. On-time shipments went from 70% to 92%.
Cost is calculated per order, and missed dates are broken down by cause — material shortfall, rework, downtime — instead of collapsing into one “we were late”.
Four months, the longest of the projects described on this site, and most of it went into the exchange with the accounting system and into the bills of materials, which had to be cleaned up before anything could be calculated from them. The four days between an order and the shop floor were never lost on the floor — they were lost between systems. What deliberately stayed with people: norms and routing, approving the job list, negotiating with suppliers, and quality acceptance.
One to two weeks. Fixed, and credited in full against the build.
A fixed quote against the scope the audit produced.
On this project. Two to four months is the range across our cases.
Model and infrastructure usage plus maintenance. Moves with volume.
Full audit + a working pilot on one slice of the process. Fully credited to the build.
Full rollout with monitoring, escalation, and documentation. Priced on scope.
Ongoing tuning, new scenarios, and monthly reporting. Cancel anytime.
We use webhooks, file exports, RPA, or direct DB access where sanctioned — there’s almost always a reliable path.
We don’t propose an off-the-shelf product either. The audit is there precisely to see where your process differs from the ordinary one, and the build is shaped around that difference. And if it turns out a standard tool covers you well enough, we’ll say so plainly — the process map stays with you in any case.
It’s a fair thing to ask, and responsibility matters more here than accuracy. A person answers for it, and the system is built so that they can: a doubtful case is never posted quietly, it goes to a review queue. Low confidence isn’t an error for us, it’s a route. Around 70% clears straight through and a person looks at the rest — and where exactly that line sits is yours to decide.
That’s a common requirement, and a reasonable one. We fit the setup to your data rules: if nothing may leave, the model runs locally inside your own perimeter, and the documents never cross it.
That’s not unusual, and it’s fine. Integrating without an API is the most underestimated line in a quote, which is why integration surface comes first among the four cost drivers, and why we don’t name a fixed price before the audit. Working without an API is perfectly possible — files, exports, email — it simply costs more, and it’s better to know that early. Which is why, fairly often, we build the API for you.
It happens often, and usually for one reason: the pilot was measured on the happy path and the exceptions were left for later — though the exceptions are where most of the work turns out to be. So we look at the share that clears without a person rather than at extraction accuracy, and we agree in advance who handles the rest, and how.
No, that isn’t what this is about. What goes is the retyping, not the people: decisions stay with a person — the disputed document, the non-standard transaction, the conversation with the client, the signature under the reporting. On one accounting project, closing a client month went from three days to four hours, not because anyone was let go, but because a qualified specialist stopped keying in details by hand. The time that frees up most often goes into growth: more clients with the same team, without the costs rising alongside.
It’s a fair question, and sometimes doing it yourselves really is the right answer. We say so when the volume doesn’t justify a build: below roughly 300 documents a month the arithmetic usually doesn’t work. The calculator on this site includes running costs, so you can weigh that up before you ever talk to us.
They do change, and that’s exactly what we build for: the model is a replaceable part here, not the foundation. The source of truth is our own database — the document, the counterparty, the entry. The model is attached to the side, and we update it whenever something better appears; for you that’s planned maintenance, not a rebuild.
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