AI development company: AI workflows that streamline your operations

Where AI workflows help a business

Large language models are good at reading, sorting and writing text. That makes them useful for the parts of a working day that are mostly text and judgement on routine cases. These are the tasks we look for first:

• Someone reads incoming invoices, orders or forms and types the details into another system.
• A shared inbox or helpdesk needs every message read, labelled and sent to the right person.
• Staff answer the same questions about policies, products or procedures from long internal documents.
• Weekly reports are assembled by hand from several tools and then summarised for management.
• Data has to be copied between a CRM, an accounting tool and a spreadsheet.

AI is not the right tool for everything. Exact calculations, legal decisions and anything that cannot tolerate an occasional mistake without review stay with ordinary code or with people. Part of our job is telling you which is which before you spend money on it.

How we build an AI workflow

Every workflow starts small, proves itself on your own data, and only then takes on more work.

1. Pick one task. We sit with the people who do it today and write down the inputs, the steps, the exceptions and what a good result looks like.
2. Collect real examples. A set of past documents, emails or tickets with the answers your team gave becomes the test set for everything that follows.
3. Build a prototype. We connect a model to the task, write the instructions and tools it needs, and run it against the examples.
4. Measure and adjust. We compare its output with your team's answers, fix the cases it gets wrong, and decide where a person must check the result.
5. Connect it to your systems. The workflow reads from and writes to the tools you already use, with logging for every run.
6. Launch with a small group. One team uses it first; we watch the results and the review queue before rolling it out wider.

AI integration services for the tools you already use

Most of the value of an AI workflow comes from the integration, not the model. A model that cannot see your documents or update your records is just a chat window. We build the connections: email and shared inboxes, helpdesks, CRMs, accounting and ERP systems, shared drives and your own databases, through their APIs or, where none exists, through a small service we write for the purpose.

We work with models from providers such as OpenAI, Anthropic and Google, and choose per task: a larger model where the reasoning is hard, a smaller and cheaper one for high-volume sorting. The workflow is written so that the provider can be changed later without rebuilding it. When the work grows beyond one workflow into a larger system, it becomes a project for our custom software development service.

AI agent development with a human in the loop

An AI agent goes one step further than a workflow: it decides which action to take next, such as creating a ticket, drafting a reply, updating a customer record or requesting a document. That is useful and it needs limits. Every agent we build has:

• A short list of actions it is allowed to take, and nothing else
• A human approval step before anything that sends, pays, deletes or changes data that matters
• A log of what it saw, what it decided and why, which your team can review
• A clear handover to a person when it is unsure or a case falls outside its rules

As your team gains confidence in a specific action, the approval step can be relaxed for that action alone. That decision stays with you.

Who we build AI workflows for

Our AI work is for companies that already have a working process and want it to take less of their people's time. Typical clients are:

• Small and mid-sized companies whose office team spends hours on invoices, orders and email
• Customer support teams that answer the same questions every day from a growing knowledge base
• Finance and fintech businesses that process documents and need every step logged
• Software companies adding an assistant or an AI feature to a product they already sell

Most engagements start as a fixed-scope project for one workflow, so you can judge the result before committing further. If AI is the core of a new product rather than a tool for your team, our MVP development service is the better starting point: it tests the idea with real users first.

Data privacy, access control and human review

Using AI should not mean losing track of your data. We explain in plain terms where each piece of data goes and who can see it:

• Provider terms. We use the providers' business APIs, review their data use and retention terms with you, and choose the options and region that fit your policy.
• Access control. An assistant answers each user only from the documents that user is already allowed to open.
• Less data out. We send the model only what the task needs and remove personal details where they are not required.
• Human review. Outputs that affect customers, money or records go to a person first, and every run is logged.

We do not train models of our own. We use the providers' models and give them your context at the moment of each request, which keeps your data in systems you control. Once a workflow is live, our software maintenance and support service keeps it monitored and updated as the models change. To talk about a first workflow, contact us with the task you would like to take off your team's desk.