Emails to sort, documents to retype, questions answered for the tenth time, reports built by hand. We give that work to AI agents connected to your tools, with a person checking the exceptions.
AI process automation combines AI models with ordinary automation to handle work that needs reading and judgment: sorting a shared inbox, extracting data from orders and invoices, drafting answers from your help docs or summarising meetings. Clear cases run automatically; unclear ones go to a person with the reason marked.
In most growing companies, skilled people spend hours every week reading, copying and classifying: an order email becomes a line in the ERP, a customer question becomes the same answer again, a meeting becomes notes nobody writes. Rule-based automation breaks on messy input. AI agents can read it, and fixed workflows move the result safely into your systems.
Each one starts with your real emails, documents or tickets, so we can measure accuracy before anything runs on its own.
Reads everything arriving in sales@, info@ or support@, classifies it, extracts the key details and routes it to the right person or system.
Turns orders, invoices, delivery notes, contracts and RFPs into structured data, checks it against your records and fills your systems.
Drafts replies to customer questions from your help centre, product docs and past answers, in your tone.
Answers staff questions about products, pricing rules, processes and policies, with a link to the source document.
Summarises internal and client meetings, records decisions and creates tasks in your project or CRM tool.
Pulls numbers from your CRM, finance and marketing tools and writes the weekly summary: what changed, what is at risk, what to look at.
| STEP | BEFORE: BY HAND | AFTER: AI PROCESS AUTOMATION |
|---|---|---|
| Arrives | Order PDF lands in a shared inbox and waits until someone opens it. | The agent reads it within minutes and recognises it as an order. |
| Data entry | A person retypes customer, items, quantities and dates into the system. | Fields are extracted and checked against the customer record and price list. |
| Checks | Errors are found later, sometimes by the customer. | Mismatches, such as an unknown product code, go to a person with the issue highlighted. |
| Confirmation | Confirmation is written when there is time. | A confirmation draft is ready; clear orders are confirmed once the flow is proven. |
An illustrative example of a typical flow, not a client case. Every process is different, which is why we test on your own documents first.
We map one process end to end and gather 30-100 real emails, documents or tickets as a test set.
We connect your tools, write the rules and confidence thresholds, and test until accuracy is good enough to launch with review.
We track accuracy and exceptions weekly. As quality holds, more cases run automatically and the next process is added.
Accuracy on the test set, share of cases handled without edits, time from arrival to done, exception rate and hours returned to your team each week.
Operational data is sensitive. These rules come with every automation we build.
Each workflow knows when it is unsure. Low-confidence or high-value cases always go to a person.
Agents can read and write only the systems and folders their job needs, with credentials you control.
Every document, decision and change is logged, so you can see what the agent did and why.
We agree which AI providers process your data and where, apply GDPR and your retention rules, and keep personal data to what the process needs.
Workflows, prompts and integrations run in your accounts and are documented, so your team can take over at any time.
AI process automation uses AI models together with ordinary automation to handle work that needs reading and judgment, not just fixed rules: sorting emails, extracting data from documents, drafting answers or summarising meetings. Classic automation moves data; AI process automation also understands it.
Processes that happen often, follow a recognisable pattern and currently need a person to read something and retype or classify it. Typical first candidates are shared inbox triage, order or invoice data entry, repeat customer questions and weekly reporting.
RPA and tools like Zapier or Make follow fixed rules and break when the input changes. AI process automation adds a model that can read an unstructured email or PDF, decide what it is and pull out the right fields. We usually combine both: AI for understanding, fixed automation for moving data reliably.
It stops and asks. Every workflow has a confidence rule: clear cases go through, unclear or unusual ones land in a review queue for a person, with the reason highlighted. Those cases are also used to improve the rules.
Yes, if the documents are accessible and reasonably up to date. The assistant answers only from the sources you approve, links to the document it used and says when it cannot find an answer. Access follows your existing permissions.
A first process is usually live in about two weeks: mapping the process and collecting examples, building and testing on real documents or emails, then a supervised launch. We then monitor it and add the next process during ongoing care.
In a 30–45 minute call we'll show you 3–5 tasks AI can take over. Free, and useful whether or not we work together.