Skip to content
Book a Consultation

AI workflow automation

AI Workflow Automation That Fits Real Business Processes

Add AI where it saves time and keeps people in control: sorting and routing messages, extracting data from documents, summarizing calls and notes, and drafting responses for review — connected to the systems you already use.

Discuss an AI WorkflowSee how it works
ClassificationData extractionSummariesDraft repliesHuman reviewCost control

Overview

AI as a step in the workflow, not a gimmick

Large language models are good at reading unstructured text — emails, documents, form answers, transcripts — and turning it into structured information or a first draft. They are less reliable when asked to make final decisions unsupervised.

We design AI workflows around that reality. The AI step produces structured output that is validated, logged and, for important actions, reviewed by a person before anything is sent or changed. You get the time savings without handing control of customer communication to an unchecked model.

Signs you need this service

  • Your team reads every inbound email just to decide who should handle it.
  • Data is retyped from PDFs, emails or forms into a system.
  • Call notes and meeting notes are rarely summarized or logged.
  • Replies to common questions are written from scratch each time.
  • You want to try AI but need to control cost and data exposure.

Problems we solve

Where AI helps most

Inbox triage

Classifying inquiries by topic, urgency and intent so they reach the right person faster.

Document data entry

Pulling fields from invoices, purchase orders, applications or resumes into structured records.

Repetitive writing

First drafts of replies, summaries and descriptions that people edit instead of writing from zero.

Hard-to-search information

Answers buried in policies, manuals and past tickets that staff need quickly.

Unused notes

Calls and meetings with no structured summary in the CRM.

Uncontrolled AI use

Staff pasting customer data into public chat tools without any process or record.

What we build

AI workflows we build

Message classification & routing

Label inquiries by category and urgency, then route them in your CRM or helpdesk.

Document extraction

Read PDFs and emails, return structured fields and flag low-confidence results for review.

CRM summaries

Summaries of calls, notes or email threads saved to the contact record.

Draft responses

Suggested replies based on your guidelines, placed in a review queue rather than sent automatically.

Knowledge assistants

Internal Q&A over approved documents with source references.

Guardrails & logging

Input limits, output validation, usage tracking and records of what the AI produced.

Sample workflow

Sample workflow: AI-assisted inbox triage

This example shows how incoming emails can be classified by an AI model and routed automatically, with a person reviewing anything uncertain.

The model returns structured JSON, which is validated before it is allowed to change anything in the CRM.

This is a hypothetical example built to explain the approach. It is not a description of a specific client project.

Demonstration — sample data, not a client project

Inbound email → AI classification → routing and review

  1. 1Email receivedNew message in the shared inboxwaiting
  2. 2Content preparedSignature and quoted history removed; length limitedwaiting
  3. 3AI classificationModel returns category, urgency and a one-line summary as JSONwaiting
  4. 4Output validatedValues checked against allowed categorieswaiting
  5. 5Ticket routedTagged and assigned in the helpdesk or CRMwaiting
  6. 6Low-confidence reviewUncertain results go to a person insteadwaiting
Press “Run the workflow” to watch the sample data move through each step.

How this workflow is specified

Trigger
New email in a shared inbox or new ticket in a helpdesk.
Applications
Email or helpdesk, an automation platform (n8n, Make or code), an AI model API, CRM.
Data inputs
Message subject and body, sender, attachment names, allowed categories and routing rules.
Processing logic
Clean text, send to the model with instructions and a schema, validate response, route or queue for review.
Expected outputs
Category, urgency, short summary and an assigned owner.
Failure points
Ambiguous messages, unexpected languages, model errors or timeouts, output outside the schema.
Error handling
Schema validation, confidence threshold, fallback to manual review, retry on API errors, usage logging.
Testing
A labeled sample of past messages to measure accuracy before switching routing on.

Platforms & tools

Models and tools

We choose the model and provider based on accuracy for your task, data-handling terms and cost, and connect it through n8n, Make, Zapier or custom code.

OpenAI APIAnthropic Claude APIGoogle Gemini APIStructured JSON outputn8n AI nodesMake & Zapier AI stepsGoogle Apps ScriptVector searchGoHighLevelHelpdesk toolsOCR for scansUsage monitoring

Implementation process

How an AI automation project runs

  1. Discovery call

    We review the current process, the applications involved and the outcome you need, and ask for examples of real (or anonymized) records.

  2. Written scope

    You receive a short plan: trigger, steps, data fields, error handling, what is included and what is out of scope.

  3. Build

    We build with sample data, write and test the instructions and output schema, and measure results against examples you have labeled before connecting live actions.

  4. Test

    Normal, edge and failure cases are tested — missing fields, duplicates, bad formats and authentication problems.

  5. Go live

    The workflow is switched on with you, checked against live activity and monitored during the first days.

  6. Handover

    You receive notes on how it works, where logs live, how to pause it and what to check if something changes.

Practical considerations

Honest limitations

  • AI models can produce incorrect output. Important actions — sending messages to customers, changing financial records — should include review or strict validation.
  • Results depend on the quality of instructions and examples; we test against your real (or anonymized) data before going live.
  • AI API usage has ongoing per-use costs; we estimate and monitor them.
  • Sending data to an AI provider has privacy implications; we review which data is sent and the provider’s terms with you.

Troubleshooting

When an AI step gives poor results

We treat AI quality problems like any other bug — with evidence:

  • Review failures — collect examples of wrong outputs and group them by type.
  • Check the input — noisy or truncated text often explains poor results.
  • Tighten the schema — fixed categories and required fields reduce vague answers.
  • Improve instructions — add clear rules and representative examples.
  • Re-test — measure against the same labeled sample to confirm improvement.

FAQ

AI automation questions

Will AI replace our staff?

That is not the goal. The workflows we build handle sorting, extraction and first drafts, so people spend their time on decisions and conversations.

Is our data used to train AI models?

It depends on the provider and plan. Many business API offerings state they do not train on API data by default, but terms vary and change. We review current terms with you before sending any data.

How accurate will it be?

We cannot promise a number in advance. We measure accuracy on a sample of your own data during testing, and design review steps for cases where the model is unsure.

What does it cost to run?

Model usage is billed by the provider based on volume. We estimate costs from your expected volume and can set limits and monitoring.

Can AI send replies to customers automatically?

It can, but we usually recommend a review step first, especially for new workflows or sensitive topics.

Have a process that could use AI?

Describe the repetitive reading, sorting or writing your team does. We will tell you whether AI is a good fit.

Discuss an AI WorkflowBook a Consultation

Related

Related services