What Is RAG and When Your Company Actually Needs It
What RAG is, explained simply: how it works, how it differs from a chatbot and fine-tuning, and when it's actually worth implementing at your company.
Read more →DOPEST.software
An engineering team building AI that works on your company's real data. We take on projects too large for a typical agency.
Many companies have already tried AI: someone wired up a chat to some documents, built a demo, and then the project stalled because answers were unreliable, data was sensitive, or costs were unpredictable. At DOPEST.software we start from those three problems. We design systems you can hand to employees and customers without an engineer standing by.
We build RAG (Retrieval-Augmented Generation) systems that answer from your company's documents, procedures and knowledge bases, with sources cited. We deploy assistants for support, sales and HR teams. We automate processes that today run through email, spreadsheets and manual re-typing.
We work with Claude, OpenAI and open source models, including on your own infrastructure when data can't leave the company. On top of that: custom software, dashboards, integrations, APIs. The code is yours, and so is the documentation.
Search and answers grounded in procedures, contracts, manuals and knowledge bases. With access control, source citations and answer quality tracking.
Assistants for customer support, sales, HR and technical teams. Integrated with the CRM, ERP, helpdesk and systems you already use.
Document classification and routing, data extraction from invoices and contracts, quote preparation, reporting. Processes that eat up full-time roles today.
Web applications, dashboards, integrations and APIs built to spec. Next.js, Node.js, Python, Postgres. Code and documentation transfer to the client.
Models and data on your own infrastructure, when required by law, client contracts or security policy.
A review of your company's processes to find what's worth automating first, with a real cost and return estimate, not a slideshow about the future.
Together with your team, we pick one process where AI can show a measurable result within a quarter, and agree how we'll measure success.
Within a few weeks you get a working prototype on your own documents and systems. You test it with the people who'll actually use it.
Security, permissions, quality and cost monitoring, integrations. A system ready for hundreds of users.
We track answer quality, add further processes, update models. We can also hand the system over to your IT team.
RAG pairs a language model with search across your company's documents. Instead of answering from memory, the model first finds the relevant passages in procedures, contracts or manuals, then builds an answer from them with the source cited. That keeps answers current, verifiable and grounded in your own data.
It depends on the architecture we choose. We can work with cloud models under contracts that exclude your data from training, or deploy open source models on your own infrastructure so data never leaves the company. The decision is made together with your security team.
A prototype for one process usually costs several tens of thousands of PLN (roughly EUR 2,300 to 9,000) and a few weeks of work. A production deployment with integrations and security starts around 150,000 PLN (about EUR 35,000). Before starting, you get an estimate of both deployment cost and monthly running cost.
A prototype takes 3 to 6 weeks. Production deployment of the first process usually takes 3 to 5 months, including integrations and user testing. Later processes move faster since the infrastructure is already in place.
Yes, that's the default model. We define the architecture with your engineers, follow your security standards, and hand over code and documentation at the end. We can also maintain the system if you'd rather we did.
Claude, OpenAI models, Gemini, and open source models such as Llama and Mistral. We choose the model based on the task, the data and the budget, and design the system so switching models later doesn't require a rebuild.
What RAG is, explained simply: how it works, how it differs from a chatbot and fine-tuning, and when it's actually worth implementing at your company.
Read more →How to pick the first process to automate with AI, calculate the return, and run a pilot in one quarter. Concrete criteria and examples, no marketing hype.
Read more →Describe in a few sentences where your company loses the most time. We'll suggest where to start and what it could deliver.
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