Automate repetitive work with AI that stays under human control
We connect AI, business rules and your existing systems to take repetitive steps out of a workflow, with a person reviewing wherever the decision matters.
AI business automation is using models, rules and integrations to handle the repetitive steps of a defined workflow, with a person reviewing wherever the outcome matters, rather than adding AI to a product for its own sake.
Most useful automation is not a chatbot. It is a repeatable process, such as qualifying an enquiry, reading a document, sorting an email or compiling a report, where some steps follow rules, some need a model and some need a person.
We start by mapping the workflow as it runs today. AI is one step in it, grounded in your own content and wrapped in fallbacks for when it is wrong. We are a software studio that integrates AI into business workflows, not a machine-learning research team, and we will say so when a problem needs one.
What this service is actually solving
Repetitive work takes skilled time
The same sorting, copying and first-pass reading, done by people who could be doing the part that needs judgment.
The AI answers confidently and is wrong
A model answering from general knowledge about your specific business is wrong often enough to cost more than it saves. Grounding it in your actual content is the difference between a feature and a liability.
Nobody designed for the model being wrong
No source shown, no way to hand over to a person, a blank panel while it thinks. Users trust it once, get burned and never use it again.
Who this is for, and who it is not
Naming the wrong fit saves both of us a call. If your situation is in the right-hand card, say so and we will point you somewhere better.
- Repetitive work that follows a pattern, such as enquiries, emails, documents or reports
- A support or onboarding job suited to an assistant grounded in your own content
- Content or data work that benefits from a first pass plus human review
- A knowledge base or documentation that needs genuinely better search
- Teams that want AI in the workflow without losing control of the outcome
- Adding AI because it should be there, with no defined use case
- Training or fine-tuning custom models
- Assistants expected to give authoritative advice in regulated domains
What you get
Concrete outputs, not activities. Everything here is something that exists at the end of the engagement.
AI lead qualification
Incoming enquiries classified and summarized so the right ones reach a person first, with the reasoning visible.
AI customer support
An assistant scoped to a defined support job and grounded in your own content, with sources shown and a clear handoff to a person.
AI document processing
Information extracted from documents into a structured form, with a person confirming what matters.
AI reporting and insights
Reports and summaries compiled from your data, with the underlying figures one click away.
AI email workflows
Incoming email sorted, drafted against and routed, with a person approving what is sent.
CRM and workflow automation
Integrations that connect the tools you already run, where a model is one step in the flow rather than the point.
Human review and fallbacks
Review steps, refusal states and escalation paths designed in from the start, not added afterwards.
How it runs
Define the workflow
One specific job, how it runs today and what a good result looks like, stated concretely enough to test.
Establish the source of truth
What the automation is allowed to answer from, and how that source stays current.
Build the loop
Retrieval, model step, interface states and fallback path, built as one workflow rather than three.
Evaluate honestly
Test against real cases, including the ones it should refuse, then set the boundaries from what we find.
How it is built, and why that matters to you
Integration-level work: API-driven models, retrieval over your own content, workflow tooling and the interface around them. Model training and fine-tuning are outside what we offer, and we will say so rather than take the work.
- LLM and model APIs
- Gemini API
- Streaming responses
- Rate limiting and cost control
- Retrieval-based interfaces
- Knowledge base structuring
- Source citation
- n8n workflow automation
- REST integrations
- Human-review steps
- Scheduled pipelines
- Streaming UI states
- Fallback and refusal states
- Human handoff
Where the scope starts and stops
Stated up front so it is a shared understanding rather than a negotiation halfway through.
- Workflow scoping against a defined, testable use case
- Integration, retrieval and the interface layer
- Fallback behavior and human handoff paths
- Evaluation against real cases before launch
- Model training, fine-tuning or machine-learning research
- Regulated-domain advice systems where a wrong answer causes harm
- Guarantees about model accuracy, savings or return on investment
Related insights
First-hand notes that go deeper on the approach behind this service.
- AI IntegrationHow to Add AI Features to an Existing Web ApplicationStart from the job the feature does, not the model doing it — the model call is rarely the hard part.Read
- AI IntegrationYou Don't Need to Rebuild Your Product to Add AIThe pressure to "add AI" feels like it demands a new architecture. Usually it doesn't — it needs one well-chosen feature.Read
Common questions
Repeatable steps with a clear definition of done: sorting and summarizing enquiries, extracting fields from documents, drafting a first reply for review, compiling a recurring report. What is generally not realistic is an open-ended assistant expected to answer anything about anything.
By retrieving from a defined source of truth and constraining the model to it, then showing the source so a user can check. If nothing relevant is found, the correct behavior is to say so and hand over to a person, not to generate something plausible.
Wherever the outcome matters: approving what is sent, confirming what was extracted, handling what the automation refuses. The review step is part of the system, not a convention.
No. This is integration work: API-driven models, retrieval over your content and the product layer around them. Training or fine-tuning needs a machine-learning engineer, and we will tell you that rather than take the project.
Anything that exposes an API or can be reached through workflow tooling. The right set is decided when the workflow is mapped, and we do not promise an integration before checking it can be done.
Have a repetitive process in mind?
Describe the workflow and what it needs to read, decide and update. We will tell you whether it is a good fit for automation, including if we think it is not.
- You send the project contextThe current site, a Figma file, a repository, API notes, or a few lines describing the problem. It does not need to be a finished brief: whatever exists is enough to start from.
- We review the problem, constraints and likely scopeA real read of what is actually in the way, what it would take to fix, and whether it is smaller than you were expecting. No call needed to get this far.
- We reply with the recommended next stepWhat we would tackle first, and what we would need in order to estimate it properly. If we are the wrong fit, we will say so and point you somewhere more useful.
Prefer to skip the form? Email works just as well — the fields are only a prompt for what is useful to include.
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