We take out the manual work that currently connects your systems by copy and paste. With Make, with n8n or with custom code: we pick based on your case, not on what suits us to sell.
Business process automation means information moves between your systems on its own — ERP, CRM, billing, e-commerce, support — instead of someone carrying it across by hand. We are software engineers, not an agency reselling a platform, so the conversation starts with which process is costing you the most hours and ends at the right tool for that specific process. Sometimes that is a six-step Make scenario. Sometimes it is a custom service. And sometimes the correct answer is not to automate it yet.
HOW WE WORK
From the process map to a flow in production that tells you when it breaks.
Before connecting anything we map where the manual hours actually go. It is rarely the process that annoys the leadership team: it is usually some small task someone repeats forty times a day that nobody has ever counted. Automating the wrong process is expensive and nobody notices the benefit.
We do not resell a particular platform, so we can tell you which one suits you. Make works well for short, stable flows. n8n pays off when volume drives up the per-operation cost or you need to self-host. And when a flow carries real business logic, the honest answer is custom code — not a forty-step scenario nobody can debug.
ERP, CRM, billing, phone systems, e-commerce or industry-specific software. We work against the real APIs of those systems, with their rate limits, their odd fields and their failure modes. Wiring two SaaS tools together with an official connector is easy; the work is in everything that has no connector.
An automation that breaks without telling anyone is worse than no automation: for weeks nobody knows the data is wrong. We build in retries, execution logs and alerts to a named person, so a failure surfaces the same day rather than at quarter close.
WHAT YOU GAIN
WHERE IT FITS
Automation moves information between systems using deterministic rules. When the problem is that two systems will not talk to each other, that is custom API integration — and if one of them is Zoho, we have a dedicated page for it. When something has to be interpreted that will not fit a rule, that is applied AI. And if a flow carries so much business logic that the visual tool falls short, what you need is custom development. Most real projects mix several; what matters is knowing which one is solving what.
If you would rather see concrete cases before talking to us, on the blog we break down eight real processes automated with AI.
QUESTIONS
It is removing the repetitive manual work that currently connects your systems by hand: moving an order from the store to the ERP, creating the customer in the CRM when a quote is signed, generating and sending the invoice, alerting a sales rep when a lead arrives. Instead of a person carrying that information from one screen to another, the flow runs on its own, leaves a record, and warns you when something goes wrong.
It depends on volume, on logic, and on where your data lives. Make and Zapier charge per operation, so a flow triggering thousands of times a month gets expensive fast; self-hosted n8n usually works out better there, provided someone maintains the server. And when a process carries real business rules, chained conditions, or has to stand up to an audit, the visual tool turns into a giant scenario nobody can debug — that is when it should be code. We assess it with you before proposing anything, and we take no commission from any platform.
When the process changes every couple of months, when it runs four times a year, or when it is badly designed to begin with: automating a broken process only makes it break faster and with less supervision. In those cases we say so and fix the process first, or simply advise you not to touch it. We would rather lose that work than build something you have to dismantle in six months.
No, although they overlap. Process automation moves information between systems using deterministic rules: given this, do that. Applied AI comes in when something has to be interpreted that will not fit a rule — classifying an email, extracting data from a scanned invoice, replying in natural language. Plenty of projects use both, but it is worth knowing which is doing what: a rule that fails gets debugged, a model that fails gets evaluated.
A well-defined, contained flow can be in production in a couple of weeks. A project spanning several systems, with data migration or processes that need redesigning first, takes months. We always start with the process burning the most manual hours per month, put it live and measure it, so you decide whether to continue with data in front of you rather than a promise.
Tell us about your project. We'll get back to you with a concrete plan and a senior team — not a generic quote.