You have tried ChatGPT and want the same help inside your processes, not in a browser tab.
E-mails, PDFs and forms are read, sorted and retyped by people every day — the classic case for AI workflow automation.
Clinics, law firms, accountants and manufacturers who need AI that runs on their own server or under strict business terms.
Your workflows and RPA bots handle fixed fields and break on free text, scans and exceptions.
Numbers from McKinsey’s 2025 State of AI survey, Gartner and the US Census Bureau — almost everyone uses AI somewhere, few have it working inside their processes.
of organisations use AI regularly in at least one business function — but only about a third say they are scaling it.
report any impact of AI on enterprise-level EBIT, and most of those put it at under 5%.
of organisations using AI have seen at least one negative consequence; nearly a third of all respondents cite AI inaccuracy.
— at least 30% of generative AI projects will be abandoned after proof of concept by the end of 2025 — over data quality, risk controls, cost or unclear value.
of organisations are scaling an agentic AI system somewhere in the business; another 39% are only experimenting.
of US businesses reported using AI in their business in May 2026 — the national rate; 37% among firms with 250+ employees.
Put your monthly volume into the calculator and see hours and dollars.
The run path of one incoming item — an e-mail, a document or a form. Model steps do the reading and writing, code checks every result, and anything uncertain stops and goes to a person.
A new e-mail in a shared inbox, an uploaded PDF, a form or a ticket starts its own run.
ReadCases that fixed rules can decide — a known sender, a fixed form, an exact keyword — skip the model entirely: cheaper, faster, predictable.
ReadA language model sorts the rest by type, urgency and owner and returns a confidence score with its answer.
ReadPulls names, amounts, dates and references into a strict JSON schema; OCR runs first on scans.
ReadTotals must add up, IDs must exist, dates must be plausible — the model’s output is never trusted unchecked.
CheckLow confidence, missing fields or a failed check stop the run and go to a person with the draft filled in.
CheckReads the customer, order or case from the CRM or ERP and searches your documents (RAG over Drive, SharePoint or Notion).
ActWrites the reply, summary or record update from your templates and only the facts it was given, with sources listed.
ActRefunds, quotes, contracts and first replies to a customer go to an approver in Slack or Teams.
ActUpdates the CRM, ERP or ticket through the API and sends approved messages — no screen clicks.
ActStores input, prompt, model, output, cost and who approved what, for audit and debugging.
LearnEdits made by people are gathered weekly into examples and rules, so the same mistake is not repeated.
LearnAccuracy, escalation rate, model spend and hours saved per process, sent to the owner.
LearnAI automation puts language-model steps inside the workflows a business already runs: classifying e-mails and tickets, extracting fields from documents, drafting replies and summaries, and — within strict limits — choosing the next step. It is not a chat window next to your work and not a model that runs the company. It is ordinary automation that can finally handle the unstructured part: free text, scans and messages that rules and RPA bots never read reliably.
We build AI business automation on n8n and Python, and we are deliberate about where the model goes. Rules and code handle everything exact — lookups, calculations, routing by known fields — and a model is called only where reading or writing is needed. Every model output is checked by code, low-confidence results and anything involving money, legal effect or a first customer contact go to a person in Slack or Teams, and every run is logged with its cost. Where data is sensitive, the models run locally with Ollama or vLLM on your own hardware.
The hours spent reading, sorting and retyping disappear, while decisions stay with people. Instead of a pilot that impresses in a demo and never ships, you get one measured process in production, a weekly report on accuracy, escalations and spend, and a clear view of where intelligent automation pays off next — and where plain code remains the better tool.
Typical minutes per incoming e-mail or document for a small operations team. Your own numbers go into the calculator below.
Based on 20 min by hand and 2.5 min with the flow per item, from the table above.
The models read, sort and draft; code checks; the workflow acts within its limits. People keep every decision with consequences.
Refunds, quotes, contracts and commitments are always approved by a person.
When the model is unsure or a check fails, the case goes to a person with the draft ready.
New templates and first replies are reviewed until the accuracy is proven.
Which tools the AI may use, what it may write and where it stops — set and changed by you.
Cloud model, local model or none at all is decided per process, with you.
Someone on your side reviews errors, costs and escalations every week.
Flip a switch to hand a step to the flow or take it back.
Benchmarks from McKinsey’s 2025 State of AI survey and Gartner: the companies that get value from AI redesign the workflow around it, measure it and keep people on the decisions that matter.
We map the process as it runs today, count the minutes and agree what the flow must never do on its own.
A working flow on your real data, in a sandbox. You see every run and every exception.
Edge cases, approvals and alerts, then the switch-over — with the old way kept as a fallback.
Monitoring, fixes when a vendor changes a format, and a monthly report of hours saved.
Use a language model where a person used to read, interpret or write: free-text e-mails, varied documents, summaries, drafts. Use plain code and rules for anything exact — calculations, lookups, fixed forms, routing by known fields. Most good AI workflows are mostly code with a few model steps.
Classic automation handles fixed fields and breaks on free text, scans and exceptions. AI process automation adds model steps exactly there, and connects systems through their APIs rather than clicking through screens.
The model works inside a strict schema, every result is checked by code, replies may only use facts pulled from your systems, and low-confidence cases go to a person. Money, legal and customer-facing actions always need approval, and every run is logged.
Yes. For sensitive data we run open-weight models with Ollama or vLLM on your own server or GPU machine, so documents never leave your network. Cloud models are used under business terms that exclude training on your data, where you allow it.
Hosting plus model usage. Many steps skip the model entirely because rules decide them, and every run logs its cost, so the weekly report shows spend per process next to the hours saved.
An agent chooses the next step from a set of tools you allowed. They are useful for multi-step requests; for most processes a fixed workflow with a few model steps is cheaper, more predictable and easier to audit — we start there.
One process, metrics agreed before building — accuracy, escalation rate, hours saved — and production within weeks, with a weekly quality report. If it cannot be measured, we do not start it.
Describe it in two sentences — we reply within a day with a workflow sketch.
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