// services
RAG & AI Systems
Your data, answering accurately, on infrastructure you control.
In shortTriton Foundry designs and builds retrieval-augmented generation (RAG) systems that let AI answer questions from your own documents, tickets, and databases instead of guessing. We deploy them private by default: on your servers, in your cloud tenant, or fully offline when data cannot leave the building.
What does Triton Foundry actually build?
A working retrieval pipeline around your business content: ingestion connectors for each source, document processing and chunking tuned to your content types, a vector search index, permission-aware retrieval, and a chat or API interface your staff actually use. Every build ships with source citations on every answer, an evaluation suite that measures answer accuracy against a test set you approve, and an admin view for watching what people ask. We build on proven open components rather than black-box platforms, and every system ships with its architecture, infrastructure configuration, and documentation — IP and licensing terms defined in writing per engagement.
Why custom instead of an off-the-shelf AI platform?
Platforms demo well on clean sample data and struggle on real business content: mixed file quality, permission boundaries, legacy formats, and questions that span systems. A custom build fits your actual sources and security model instead of forcing your data into a vendor’s mold. It also keeps the economics honest: you pay for engineering once instead of per-seat forever, and you are never one vendor pricing change away from losing your knowledge system. When a platform genuinely fits, we say so in discovery and quote the smaller integration project instead.
Private by design: where does the AI run?
Three deployment models, chosen during discovery. Fully local: open-weight models on GPU hardware in your building, zero external calls, suited to regulated or air-gapped environments. Private cloud: models and index inside your own Azure or AWS tenant, data stays under your agreements. API-based: commercial frontier models with your data retrieval kept on your side, suited to lower-sensitivity content where top model quality matters most. Triton Technologies has run infrastructure for regulated businesses since 2001; the same discipline applies to where your prompts and documents travel.
What does a RAG engagement look like?
Discovery first: data inventory, security requirements, success metrics, and a written recommendation you can act on with or without us. Then a scoped pilot on one or two high-value sources, measured against the evaluation set, typically a few weeks rather than months. Production hardening follows: permissions, monitoring, backup, and staff rollout. After launch, most clients continue with a fixed-scope support program through the parent company’s managed services team, the same people who already run IT for hundreds of businesses.
// common questions
RAG & AI Systems: common questions
What is a RAG system in plain terms?
RAG (retrieval-augmented generation) connects an AI model to your actual documents and data. When someone asks a question, the system first retrieves the relevant passages from your files, then the model writes an answer grounded in those passages, with sources shown. The model answers from your data, not from its memory.
Can a RAG system run fully offline?
Yes. We build RAG systems on local open-weight models running on hardware in your building or private data center. No prompt, document, or answer ever leaves your network. This is the standard architecture we recommend for regulated data such as healthcare, defense supply chain, and financial records.
What data sources can be connected?
File shares, SharePoint and Microsoft 365, ticketing systems, CRMs, SQL databases, PDFs and scanned documents, intranets, and most line-of-business applications with an API. Discovery includes a data inventory that maps every source before anything is indexed.
How is this different from just using ChatGPT?
Public chatbots answer from training memory and cannot see your internal data, so they guess. A RAG system answers only from your indexed content, cites where each answer came from, respects your document permissions, and can be hosted so nothing is shared with any outside AI provider.
Do we need to clean up our data first?
Partially, and we tell you honestly where. Retrieval quality depends on source quality, so discovery includes a content audit. We routinely find that a focused cleanup of the top few document libraries delivers more answer accuracy than any model upgrade.
// next step
Have a system in mind?
Describe what you are trying to build or fix. A senior engineer reviews every inquiry and responds directly, with a technical read on the problem.