AI-powered applications and digital solutions engineered for the future.
Get in touch →About BKI
BKI is a technology company specialising in AI systems, intelligent applications, and digital products. We design and develop solutions that harness artificial intelligence to automate, optimise, and elevate how businesses operate.
Whether it's a custom AI pipeline, a smart web application, or a data-driven tool — we engineer it with precision. Our work sits at the intersection of research and real-world deployment, delivering systems that are not just functional, but genuinely impactful.
We are a small, focused team that values quality over quantity. Every project we take on receives our full attention — from initial design through to production and beyond.
Our work spans a wide range of domains, from natural language processing and computer vision to recommendation systems, predictive analytics, and process automation. We approach every problem with curiosity and rigour, balancing engineering pragmatism with research-driven thinking. The result is software that is both technically sound and genuinely useful in the hands of real users.
How we work
We believe great software is built by combining deep technical skill with a clear understanding of the problem it's meant to solve. Here's how we get there.
We start by listening. Before writing a single line of code, we work to understand the problem, the users, and the constraints. The right question matters more than the first answer.
We design systems that are simple where possible and sophisticated only where needed. Architecture, data models, and user experience are shaped together — never in isolation.
We build, test, and ship in tight iterations. Working software in real hands beats perfect software on paper. From there, we measure, learn, and improve.
What we believe
Substance over hype. Artificial intelligence is a tool, not a buzzword. We use it where it genuinely improves outcomes — and we're equally comfortable saying when a simpler solution is the right one.
Long-term thinking. Software that lasts is software that respects its future maintainers. We write code that's clear, tested, and documented, so that what we ship today still makes sense five years from now.
Honest collaboration. We treat clients as partners, not ticket queues. That means transparent communication, realistic timelines, and a shared commitment to getting things right.
What we do
From machine learning infrastructure to polished end-user products, we cover the full spectrum of intelligent software development.
Custom AI pipelines, machine learning models, and intelligent automation built to solve real business problems at scale.
Intelligent applications for web and mobile platforms — fast, reliable, and designed with the end user in mind.
APIs, data tools, backend systems, and custom digital products that integrate seamlessly into your existing workflows.
Knowledge Base
Practical guides and in-depth articles on AI, machine learning, and intelligent software from our engineering team.
AI is reshaping every industry — but what does it actually mean? We break down core concepts and real-world applications.
Read article →Key decisions, tooling, and architecture patterns for building a production-grade AI application.
Read article →From demand forecasting to customer support automation — ML is a present competitive advantage.
Read article →Topics we cover
Our blog covers a wide range of topics across AI, engineering, and business technology.
Common questions
In practice, AI refers to software that learns from data to make predictions, classify inputs, generate content, or automate decisions — without being explicitly programmed for each case. Examples include spam filters, product recommendations, fraud detection, medical image analysis, and language assistants like ChatGPT.
Machine learning is a subset of AI. AI is the broad concept of machines performing tasks that normally require human intelligence. Machine learning is the approach of learning patterns from data rather than following hand-coded rules. Deep learning is a further subset that uses multi-layer neural networks and excels at tasks like image and speech recognition.
Timelines vary significantly by scope and data maturity. A focused AI feature or integration typically takes 4–8 weeks. A full AI pipeline or intelligent application build typically takes 3–6 months. We always start with a discovery phase before committing to a timeline.
Our core stack includes Python, PyTorch, TensorFlow, Hugging Face Transformers, FastAPI, React, and PostgreSQL. On the infrastructure side we work with AWS and GCP. We choose tooling based on what the problem requires, not what is fashionable — sometimes the right answer is a simple statistical model, not a large neural network.
It depends on the approach. Modern pre-trained models (large language models, vision transformers) can be fine-tuned on surprisingly small datasets. For supervised classification tasks, a few hundred well-labelled examples can be enough. Training custom models from scratch requires significantly more. We assess your data situation in the discovery phase and design accordingly.
An AI pipeline is the automated infrastructure that connects raw data to a model's output — ingesting data, cleaning and transforming it, running it through a model, and delivering the result to an application or report. Pipelines are where most of the real engineering work lives in production AI systems.
RAG is a technique that allows large language models to answer questions using your specific data, rather than relying solely on what was in their training set. It works by storing your documents in a vector database and retrieving the most relevant ones at query time, then feeding them to the model as context. This reduces hallucination and keeps answers grounded in real information.
Yes. We work remotely with clients across Europe and beyond. All communication can be conducted in English or Polish depending on your preference.
AI is the right solution when: (1) the task requires handling variability or learning from examples that cannot be reduced to fixed rules, (2) you have data to learn from, and (3) the value of automating or improving the task justifies the investment. We help clients think through this in a free initial consultation — including being honest when a simpler approach would work better.
Prompt engineering is the practice of designing and optimising the instructions given to large language models to get reliable, high-quality outputs. It ranges from simple instruction phrasing to sophisticated techniques like chain-of-thought prompting, few-shot examples, and structured output formatting. It is now a core skill in building LLM-based applications.
Contact
Have a project in mind or want to learn more about what we do? Drop us a message — we'd love to hear from you.
✉ biuro@bki.info.pl