Workflow audit
An honest look at whether the process is actually a good automation candidate before we build anything.
Overview
The best automation candidates are boring, repetitive and well-defined
Not every workflow is a good fit for AI automation, and the ones that are usually aren't the exciting ones: sorting support tickets by category, drafting a first pass of routine content, extracting structured data from messy documents. Tasks with a clear pattern and a reasonable tolerance for occasional human review.
We start by auditing the actual workflow before building anything, since automating a badly designed process just makes bad decisions faster. Once it's clear where AI genuinely helps, we build an agent that handles the predictable cases and escalates anything uncertain to a person, wired into the tools your team already uses rather than adding a new dashboard nobody checks.
Agents also increasingly work the other way round: your customers' AI assistants need a way into your product. We build MCP (Model Context Protocol) servers that let ChatGPT, Claude, Cursor and similar tools look up your data and take actions in your product safely, with the same permissions a signed-in user would have.
Tools & platforms we use
- OpenAI API
- Anthropic API
- Model Context Protocol
- n8n
- Zapier
- Make
What's included
Automation that knows when to hand off to a person
Agent & automation build
Built around your specific workflow logic, not a generic template that half-fits.
Integration with existing tools
Wired into Slack, your CRM, your ticketing system, wherever the workflow actually happens, or exposed to AI assistants through an MCP server.
Monitoring & guardrails
Escalation paths for anything the automation isn't confident about, and visibility into what it's doing.
Questions
Common questions about workflow automation
5 questions
What kind of workflows are actually worth automating with AI?
Repetitive, well-defined tasks with a recognizable pattern and some tolerance for occasional review: categorizing tickets, drafting routine responses, extracting data from documents. Highly judgment-heavy or rarely repeated tasks are usually poor fits, and we'll say so rather than force AI into them.
How do you handle mistakes in an automated workflow?
By designing for them from the start: confidence thresholds that escalate uncertain cases to a person, logging so mistakes are visible rather than silent, and review cycles once the automation is live rather than a set-and-forget approach.
Which tools do you integrate with?
Whatever your team already uses, Slack, your CRM, project management tools, email. We'd rather wire an automation into your existing workflow than ask your team to adopt a new tool just for it.
What is an MCP server, and do we need one?
MCP (Model Context Protocol) is the open standard AI assistants such as ChatGPT and Claude use to connect to other software. An MCP server for your product lets your users ask their assistant to look something up or make a change in your product directly. It's worth building if your customers already use AI assistants for their work, which for most B2B software is now the case.
Can we start small before automating a whole process?
That's usually how we'd recommend starting. A narrow slice of the workflow, tested and refined, tells you a lot more about whether the full automation is worth building than trying to automate everything at once.
Quick question?
Ask us about AI Agents & Workflow Automation
Not ready for a full brief? Send a question and a developer who works on this will answer it, usually within one business day. No sales call, no obligation.
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Ready to get started?
Tell us what's eating up your team's time.
Send a description of the workflow and we'll give you an honest read on whether automation fits, plus a fixed estimate.
If the first milestone doesn't match the brief, we'll revise it at no extra cost.
NDA signed before we see anything. Delivered under your brand.