Practical AI built on your own data: knowledge assistants, LLM integrations, and automated workflows that give answers your team can trust, and keep working after launch.
Most businesses don't need another generic chatbot. They need AI that can read their own policies, contracts, manuals, and records, and give accurate answers from them.
Andriosol Technologies designs and builds custom AI systems for companies across the United States. We start with the problem you're trying to solve, like repetitive questions flooding a team, knowledge buried in PDFs, or manual steps nobody has time for. Then we build an AI system that fits the tools and data you already have.
Everything we build is grounded, traceable, and maintained. Answers come from your real documents, you can see where each answer came from, and we stay on after launch to keep quality high as your content changes. And because we also do custom software development, the AI plugs into real systems instead of living in a separate tab.
From a single internal assistant to AI woven through a whole workflow, we build what solves the problem, and nothing it doesn't need.
Internal assistants that answer staff questions from your policies, handbooks, SOPs, and documentation in plain language, with answers traceable to the source.
Add large language model features to the software you already run: summarizing records, drafting responses, classifying requests, or extracting data from documents.
Customer- or employee-facing chat grounded in your real content, with clear fallbacks to a human when the question is out of scope.
Use AI to handle the repetitive middle of a process, like triaging inboxes, routing tickets, or pre-filling forms, while people approve what matters.
Turn stacks of PDFs, contracts, and scanned records into searchable, structured data your systems can actually use.
Not sure where AI fits? We identify the highest-value use case and prove it on your real data before you commit to a full build.
We built and deployed a retrieval-augmented generation assistant that lets employees ask HR policy questions in plain language, like leave, benefits, or reimbursements, and get answers grounded in their company's own policy documents.
It's built on Kotlin and Micronaut with Meta's Llama-3.3-70B-Instruct model, and runs in production at multiple companies. The same architecture extends to any department's knowledge base: IT, operations, compliance, or sales.
It depends on scope: how many data sources the system reads, how it's hosted, and what it has to integrate with. After a short discovery call we give you a written estimate with a clear scope, so there are no surprises mid-project.
No. We design systems so your documents stay under your control. Depending on your requirements, we can run open-weight models such as Meta's Llama on infrastructure you control, or use a hosted model provider under terms that exclude training on your data.
Retrieval-augmented generation (RAG) means the AI first looks up the relevant passages in your own documents, then writes its answer from them. Answers are grounded in your real policies and data, and can be traced back to the source, instead of being a plausible-sounding guess.
Yes. We're a remote-first team and work with businesses anywhere in the US, with regular video check-ins and shared project tracking throughout the build.
We monitor answer quality, keep the document index up to date, and adjust prompts and retrieval as your content changes. Every project includes a maintenance handoff.
Further reading: RAG vs. Fine-Tuning: Which Does Your Business AI Actually Need?