Services / Development / AI
Custom AI agents and workflow automation
We build custom AI agents, assistant interfaces and workflow automation that fit into how a team actually works: connected to your real systems, with clear UX and a human where one belongs. Not a demo, a tool people use on a Tuesday.
Most of the work in AI is not the model. It is the interface, the data the agent is allowed to reach, the tools it can call and the guardrails around it. That is design and engineering, and it is what we do.
AI projects fail in a familiar way: someone picks a model, builds an impressive demo, and six months later nobody on the team uses it. The demo was never connected to the real systems, nobody designed where a person checks the output, and the people who were meant to use it were never asked what their day looks like. We start at the other end, with the workflow, and treat the model as the last decision rather than the first. The first weeks are a prototype on your actual data: your tickets, documents, orders, records. Not a slide and not synthetic examples. We measure how often it gets the job right, where it fails and whether the people who will live with it trust what it produces. Only then do we commit to the production build, and only if the numbers say it is worth it. That order protects your budget and ours. The production build is mostly engineering that has nothing to do with prompts: the interface people use, the integrations with your CRM, ERP, CMS or helpdesk, the permissions that decide what the agent may read and touch, the evaluation suite that tells you it still works after every change, and the logging that shows what it did and why. We build this on the same stack as everything else we ship, Next.js, NestJS and Laravel, so it is maintainable by your engineers or ours. We have done this for internal workflows, for customer facing assistants and for our own product. Brease, our AI native CMS, came out of adding AI to client sites until it made sense to build the editor around it; around ten client sites already run on it. The Pannon Turfgrass AI workflow work took the same approach on an ordering platform. Both are cases where the useful part was not the model but the fit.
What we build
- AI agents that take a task from start to finish inside your business and hand the result to a person.
- Assistant and copilot interfaces: chat, command, review and approval screens, and the admin behind them.
- Workflow automation across your systems: intake, classification, extraction, document generation, notifications.
- AI features inside products you already have: search that understands intent, generation in your CMS, summarisation, recommendations.
- Document and email processing: orders, invoices and requests turned into structured data with a human checkpoint.
- Evaluation, logging and guardrails for agents someone else built that you are not sure you can trust.
We work with the leading model providers and with open models where data has to stay inside your walls.
Stack and facts
- Models
- Leading commercial providers, or open models run in your environment
- Stack
- Next.js, NestJS, Laravel, PostgreSQL, vector search where it earns its place
- Integrations
- CRM, ERP, CMS, helpdesk, email, your own APIs
- Data
- No training on your data; GDPR compliant handling for EU clients
- Proof
- Prototype on real data before any production commitment
- Ownership
- Code, prompts, evaluation suite and repository are yours
How an AI project runs
The workflow first, the model last. Each stage ends with something you can try on real data, so the decision to build is made on evidence rather than a demo.
Workflow mapping
We sit with the people who do the job today and map it: inputs, decisions, systems, exceptions. Out of that comes what the agent should do, what it must never do, and where a person stays in the loop.
Prototype on real data
A working prototype against your actual documents, tickets or records within weeks. We measure accuracy, find the failure cases and check whether the people who will use it trust the output.
Build and integrate
The production version: interface, agent logic, tool access, integrations, evaluation suite and logging, built in sprints with the prototype's findings baked into the guardrails.
Launch, measure, improve
Rollout to a first group, then wider. We watch time saved, accuracy and escalations, and tune prompts, tools and models as real usage shows where the agent needs to get better.
Where we have put AI to work
AI work is hard to show in screenshots. These are the projects where it sits inside something real: an ordering platform for a turf producer, and a voice AI lab whose brand and site we built.
Designatives came up with a great new logo and did an excellent job redesigning our website and our whole branding material. We extended our collaboration to software development, and our clients especially love the B2B online ordering system.
Questions we get asked
Tell us about the workflow
A short note is enough: the job you want an agent to take on and the systems it has to touch. We reply within one working day with next steps, or you can book a discovery call straight away.