Our Technology
Data Extraction. LLM Engineering. Agentic AI.
At Parabots, we develop technology for turning fragmented information into usable intelligence and intelligent systems into reliable tools. Our work spans the full chain from extracting and structuring data to grounding language models in real-world context and designing agent-based systems that can support or automate complex tasks.
This page focuses on the underlying technology behind that work. It shows how we build data pipelines, LLM-based systems, and agentic architectures that are not only capable, but also controllable, explainable, and suited for real operational environments.
Data Extraction
Parabots has a long background in technologies for collecting, extracting, and structuring information from complex digital sources. Much of the world’s data is not neatly stored in databases, but scattered across websites, documents, feeds, and other semi-structured or unstructured environments. We build systems that turn that fragmented information into usable, structured knowledge.
Our expertise includes web and text mining, classification, entity recognition, sentiment analysis, monitoring, and domain-specific extraction pipelines. Depending on the use case, this may involve identifying relevant sources, extracting fields from noisy content, linking related information, or transforming raw text into datasets that can be searched, analyzed, or used downstream in software systems.
This foundation remains highly relevant today. Strong AI systems do not begin with prompting alone; they begin with reliable access to the right information. Our data extraction expertise helps clients create the structured, high-quality context needed for analytics, search, LLM applications, and agent-based automation.
LLM Engineering
Large language models become most valuable when they are connected to the right context. Parabots builds LLM-powered systems that go beyond generic chat by grounding outputs in business-specific knowledge, workflows, and data sources. This includes assistants, domain-specific RAG pipelines, and custom integrations that make LLMs useful inside real products and organizations.
Our work in this area includes retrieval pipelines, prompt and response design, tool integration, knowledge base connections, and infrastructure choices ranging from cloud APIs to locally hosted models. We help connect LLMs to internal documents, databases, software platforms, and legacy systems so that responses are informed by the client’s own reality rather than by generic public knowledge alone.
The goal is not simply to add an LLM, but to engineer a dependable system around it. That means careful handling of context, security, performance, privacy, and output quality. The result is AI that is more relevant, more controllable, and better suited for professional and operational use.
Agentic AI
Agentic AI builds on LLM engineering by allowing AI systems not only to respond, but also to act. Parabots designs agent-based architectures that can reason through tasks, use tools, work across multiple steps, and support or automate business processes. This moves AI from simple assistance toward practical execution.
In practice, this can involve agents that retrieve information, validate outputs, call software tools, trigger actions, summarize results, or coordinate subtasks within a larger workflow. Depending on the use case, we can design systems with human review steps, bounded tool access, and control mechanisms that keep automation reliable and transparent.
We focus on agentic AI where it provides real operational value: streamlining repetitive work, supporting decision processes, and embedding intelligent automation directly into products and internal workflows. The emphasis is always on useful, grounded systems rather than autonomous behavior for its own sake.
