AI still needs checking.
Generated answers can be wrong or incomplete. We choose review points around the consequences of a mistake, and keep a manual route available.
AI & automation.
Your team has better things to do than move the same information twice. We build AI assistants and workflows that take care of the busywork.
Give the repetitive work somewhere else to go.
We turn a repeated task into a workflow your team can actually use. That might mean sorting enquiries, preparing documents, updating records, or finding answers across your internal information.
We separate the predictable steps from the parts that need judgment. Rules handle the predictable work; AI can help with language and interpretation. Your team reviews the decisions that need a person, with a clear route for anything the system cannot handle.
Give your team some time back.
An enquiry arrives. The right person gets a useful head start.
An example of an assisted intake workflow, built around your existing inbox and CRM.
Read the enquiry
A new message starts the workflow. Relevant details are extracted and missing information is flagged.
Prepare the next step
The system suggests a category, creates a draft record, and prepares a response using approved information.
Review and send
A team member checks the draft, makes the call, and sends it. The record keeps a history of what happened.
How we’ll work
together.
4–6 weeks
One focused workflow connecting two existing tools.
Week 1
Follow the task.
We walk through the current process with your team, collect examples, and agree what a useful result looks like.
Week 2
Test the uncertain part.
We try the hardest step on sample data, decide where AI helps, and agree approval points before connecting the full workflow.
Weeks 3–4
Connect and check.
We build the workflow, add failure handling, and test ordinary cases alongside incomplete, unusual, and duplicate inputs.
Weeks 5–6
Start with a small group.
Your team tries it in daily work. We review the results, tune the handoffs, and document how to operate and maintain it.
Access to the tools, representative examples, and feedback from the people doing the task affect the schedule. More workflows or sensitive data need additional scoping. We agree dates after scoping the work.
What you get.
We agree what’s included before starting.
Workflow map
A map of the workflow and its approval points.
Working connections
The agreed automation and tool connections.
Tested examples
Checks using representative examples from your work.
Recovery controls
A way to spot failures and retry safely.
Operating guide
Configuration, prompts, and operating instructions.
Team walkthrough
A walkthrough with the people using it.
What we need
from you.
- A person who knows the task and can review results
- Example inputs and the outcomes you would expect
- Agreed access to tools and permission to use the necessary data
Things to consider.
A few things to agree before we start.
A messy process stays messy.
If nobody agrees how the task should work, automating it usually moves the confusion faster. We settle those decisions first.
Running costs continue.
AI usage, hosting, and third-party tools can carry ongoing fees. We discuss those dependencies and expected usage before choosing the setup.
Common questions.
Something else on your mind?
What does an automation project cost?
We scope the workflow first: the tools involved, the decisions it makes, and the checks it needs. You get a project estimate and a separate view of any ongoing platform or AI usage costs before work begins.
How long before we can try it?
A focused workflow usually takes four to six weeks. We test the uncertain step early, then share a working version with a small group before introducing it to everyone.
Will we be able to change the workflow ourselves?
We show your team how to use the controls included in the project and document the important settings. Changes to the logic or connected systems may need development and testing; we agree who handles those.
How do we know the automation is helping?
We agree what to measure before building, such as time spent per task, errors, or the amount of manual review. We compare those measures on real examples instead of judging success by how much has been automated.
Does everything need AI?
No. A rule, a form, or a direct connection between tools may solve the problem more simply. We use AI for the parts where its ability to work with language or varied inputs is useful.
Can this work with our existing tools?
We check their connection options and access limits during scoping. If a tool cannot support a reliable integration, we explain the alternatives before building around it.
What happens to our data?
We map what information each step needs and where it would be processed. Access permissions, provider settings, retention, and any human review are agreed before real data enters the workflow.