AI Engineering & Automation
AI development services
AI that works inside the systems you already run. We integrate AI into your product or your daily operations, for product teams and businesses buried in manual document and data work. The senior engineers who build it also maintain it.
Why integration
The hard part is the integration, not the model
In the projects we have delivered, the model was rarely the hard part. The work is getting it to reach your data and fit into the tools your people already use. Then it has to stay reliable once real work depends on it.
So we build the system around the model. That means the connections that feed it data, the review step after it, and monitoring that shows when quality slips.
So we build the system around the model. That means the connections that feed it data, the review step after it, and monitoring that shows when quality slips.
We choose the model to fit the job
We pick whichever AI model suits your problem and build so it can be swapped later. You are not locked into one provider, and the value stays in the system we build around it.
AI features in your product
Assistants, classification, extraction, summarisation and search, built into the product you already ship.
Agents and workflow automation
Multi‑step automations that read from and write to your real systems, with the checkpoints a business process needs.
Document and report automation
Turning scattered notes, records and files into structured documents a professional can review and sign off.
Proof: healthcare
Referral reports drafted in seconds
A Canadian dental clinic built each referral report by hand: clinician notes, appointment records and visit images, gathered from separate places and formatted. Each report took days.
We built the system that does the gathering. It pulls the records from the clinic's practice management software and writes a complete draft. The report owner gets it as a Word document to review. The draft takes seconds. Sending it stays a human decision.
We built the system that does the gathering. It pulls the records from the clinic's practice management software and writes a complete draft. The report owner gets it as a Word document to review. The draft takes seconds. Sending it stays a human decision.
Built into the clinic's own systems
The system connects directly to the software the clinic already used and runs on the clinic's own servers, inside its private network. Built with Java and Spring Boot.
Proof: logistics
Financial insight without the subscription bloat
A shipping company and a logistics company wanted to see what their invoices said about their business. The usual answer is a finance subscription platform: a fee every month, for far more software than they needed.
We built each of them a focused tool instead. Staff upload an invoice, and the system reads it with AI-assisted text recognition, extracts the figures and stores them. The insights they care about appear in one clear view.
We built each of them a focused tool instead. Staff upload an invoice, and the system reads it with AI-assisted text recognition, extracts the figures and stores them. The insights they care about appear in one clear view.
Only what they need, checked by a person
Both companies run the same pipeline. Someone reviews what the system extracted before it feeds the insights.
How we work
How an AI engagement runs
- Stage 01 · 1–2 weeks Discovery
We look for assembly work: tasks where a capable person spends hours gathering information from several places to produce something with a familiar pattern. Then we confirm the data is reachable and the workflow is stable enough to build on.
- Stage 02 · 1–2 weeks A narrow first version
Aimed at one workflow. It does a single job end to end, so you can judge it on one question: does it help your team?
- Stage 03 · 2 weeks Evaluation
We test it against real examples where the correct answer is already known. If it does not clear that bar, it does not ship, and we tell you so.
- Stage 04 · 4–6 weeks Integration
It goes into the tools your people already use. A review step sits wherever the work has consequences, and monitoring shows how well it performs.
- Stage 05 · ongoing Production
In our experience, this is the stage AI projects most often fail to reach, so we plan for it from the first week. We keep measuring quality, so we notice early when results start to slip.
- Stage 06 · ongoing The next workflow
In our work, the second automation costs less than the first, because the data connections, review steps and test cases already exist. Choosing the right order matters more than doing everything at once.
Our approach
What we sort out before reaching for AI
The parts of an AI project that decide whether the result is usable.
The data problem comes before the AI problem
The information an AI system needs usually exists already. It is spread across a database, a cloud tool, a shared drive and someone's inbox, in different formats, with no agreed meaning. We build the connections that gather it and keep every answer tied to a source you can check. Where records disagree, we show the disagreement instead of letting the AI blur it into something confident and wrong.
Where we will tell you not to use AI
A simple rule, a fixed template, or fixing the process that created the mess is often cheaper and more reliable. We say so. A consultancy that recommends AI for everything is selling rather than advising, and we are usually the ones who would maintain it. An AI step doing a job a rule could do costs you forever, in speed, running costs and debugging.
AI you can put in front of a professional
When the work carries consequences, the person stays in charge. They start from a finished draft instead of a blank page, and they decide what goes out. Output lands in the tools your people already use, everything the AI produces stays editable, and the review step is part of the design.
Scope
What an engagement can cover
We scope each project to the pieces it needs, and your proposal lists them.
Discovery & data
- Finding where AI will help
- Connecting and cleaning your data
- Search over your own documents and records
Build & integrate
- AI features (LLMs) inside your existing product
- Automated multi-step workflows
- Document and report generation
- Connections to your existing systems
- Screens for reviewing AI output
Stack
Technologies we work with
- Python
- TypeScript
- Node.js
- Claude
- Ollama
- LangChain
- LangGraph
- CrewAI
- LiveKit
- Vector databases
- Tesseract OCR
- Apache Airflow
- n8n
- Zapier
- Docker
FAQ
AI development, answered
Mostly integration work. We find the workflow worth automating and get reliable access to the data it depends on. Then we build and test the AI step and connect its output to the tools your people already use. Wherever the work has consequences, a person reviews it.
A first workflow usually reaches production in 8–12 weeks. That covers 1–2 weeks of discovery, 1–2 weeks for a first version and 2 weeks of testing against real examples. Integrating it into your tools takes another 4–6 weeks. After that it keeps running and improving.
We go through a high-level list of your requirements together and talk through where AI could help and where it would not. The call is a free consultation.
Whichever fits the problem, and we can swap it later without rebuilding the system. Where your constraints require it, that includes models running on your own servers.
Yes. We deliver to cloud, hybrid and fully on-premise environments, including systems inside a closed network where no data leaves your premises.
We design for it. Output stays editable, and a named person reviews it before anything is sent or acted on. Failures are logged where you can see them. We keep testing in place, so quality drift gets caught early.
No. Your data is used to do your work. We do not train models on client data, and where your requirements demand it, we deploy so that data never leaves your own systems.
Yes, that is what discovery is for. We look at where your people spend time gathering, looking up and reformatting information, then come back with the two or three tasks worth automating. Some turn out to be cheaper to solve without AI, and we tell you which.
Yes. Agencies bring us in as a white-label delivery partner for AI work. We build and maintain the system, and you keep the client relationship under your brand.
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Next step
Tell us about the work that eats your team's time
In a free discovery call we go through your requirements together, and you hear plainly whether AI is the right fix.
Our Office
Greece Leoforos Kifisias 44 15125 Athens