1. Use Case and Data Review
We look at the process you want to improve, the data available, and the people who will use the result every day.

Akveo builds AI assistants, agents and document search that work on your real data and inside the tools your team already uses. Our team works from Domaniewska 17/19 in Warsaw, in CET hours.
Describe the task, the data involved and who will use the result. Our team replies within 1 business day with a suggested approach and a proof of concept estimate.
Akveo has shipped AI features for fintech, automotive, language learning and insurance products, on top of more than ten years of software delivery.










Each one is connected to your data and tools and measured against quality checks we agree with you.
A working prototype on a sample of your data, used to decide whether a use case is worth a full build.
Typical deliverables:
Assistants that answer questions from contracts, manuals, policies or tickets and show the source behind each answer.
Typical deliverables:
Agents that look up records, call APIs and complete multi-step tasks, with limits on what they can change.
Typical deliverables:
Summaries, smart search, drafting and classification added to the software your customers already use.
Typical deliverables:
Assistants for customers or staff that answer from your knowledge base and pass the conversation to a person when needed.
Typical deliverables:
Ongoing checks that the system keeps giving correct answers at an acceptable price after launch.
Typical deliverables:
Three steps that keep the first spend small until the use case proves itself.
We look at the process you want to improve, the data available, and the people who will use the result every day.
A prototype on your own sample data, starting from $10K. You see real outputs, error rates and running costs before deciding on more.
If the results hold up, we agree on scope, integrations and a budget cap. A production system usually takes three to four months.
An EU team that treats data protection and measurable quality as part of the engineering work.
Our AI engineers work at Domaniewska 17/19 in Warsaw, inside the EU and under EU data protection law.
The proof of concept measures what each AI request costs, so the production budget is based on real numbers.
Models can run in EU cloud regions or your own infrastructure, and we sign a data processing agreement before touching any records.
Every proof of concept ends with numbers on accuracy, speed and cost per request, which makes the next decision straightforward.
Starting with a four-week proof of concept limits the risk to a fixed amount before any larger commitment.
Source code, prompts, evaluation sets and documentation are handed over and belong to your company.

[ Start Small ]
Tell us the process you want to automate and the data behind it. Our team will reply within 1 business day with next steps and a rough estimate.
Three AI systems in production, two of them for companies based in the EU.
65% average token cost savings via prompt caching
CarmyAI wanted drivers to describe a car problem and get a diagnosis, a cost estimate and repair steps. We built a Claude-based agent with 24 automotive tools that chains several tool calls per answer and streams each step so users can follow the reasoning.

Full SaaS platform delivered in 7 months
Skrivanek, an EU language services company, needed exercises that adapt to each learner's level. We built a SaaS platform with an assistant based on OpenAI GPT models that supports over 30 languages, along with user management and Stripe payments.

50% faster task execution
A productivity company wanted an AI assistant that helps people break goals into milestones and stay on track. We delivered an MVP as a progressive web app built with Next.js, OpenAI and LangGraph, with automated reminders and push notifications.

We choose models by accuracy, cost and where they are allowed to process your data.
95% of our partners continue working with us beyond the initial scope and more than 70% stay with us for 2+ years.
Tell us what you want AI to do and what data it can use. We reply with an approach and a proof of concept estimate.