Nectar LLM — your industry’s AI content aggregator, in a box.

It continuously gathers content from the sources that matter in your industry, organizes it by topic, and lets your audience ask questions and get answers drawn from that content — with the sources cited. Think of it as a specialist that reads your field all day and answers in plain language.

During the proof-of-concept phase on non-sensitive content, inference runs on a trusted hosted service. For production — especially regulated content — the system runs on hardware you control, so your data never leaves your environment. There’s no third-party model provider in the loop to vet or worry about.

We do not make compliance claims. Compliance is determined solely by you and your legal and compliance advisors against your specific obligations. What we provide is an architecture that is compliance-aware by design — it keeps regulated data in an environment you control and keeps answers traceable to their sources — giving your advisors a foundation to evaluate. It is one component of your broader compliance program, not a substitute for it.

An open model is one whose underlying file (its “weights”) can be downloaded and run on hardware you control — Llama, Mistral, and Qwen are examples. A closed model, like OpenAI’s GPT, Anthropic’s Claude, or Google’s Gemini, can only be reached through the provider’s API; you can never bring it in-house. We build on open models so the exact system you start with can later move onto your own hardware with a single configuration change — no rebuild, no lock-in.

Two reasons. First, a system that runs continuously, 24/7, becomes cheaper to own than to rent once you run it for the long term. Second, owning the hardware means your data never leaves your environment — which matters enormously for regulated content. You start fast on a hosted service, then land on owned hardware when it goes live.

Yes — that’s the whole point of building on an open model. You can launch in days on a hosted service, prove the concept, and then migrate the identical model onto your own machine when you’re ready. Because it’s the same model throughout, the move is a configuration change, not a re-engineering project.

The proof-of-concept proves the pipeline works — ingesting, organizing, and answering — on a leaner machine. The production setup is the always-on engine: more memory, error-correcting hardware, and room to grow. The AI capability is the same on both; the difference is the platform’s durability and headroom for 24/7 operation.

Every answer is generated only from the content the engine has gathered, and the sources are kept with the answer. You can present answers in a verification-forward style, where citations are shown prominently, or a reading-forward style, where sources are tucked alongside — but both are grounded in real sources. Answers are never invented from thin air.

The engine ingests continuously, checking your sources on a set schedule and adding new content as it appears — so the material stays fresh without manual effort. The update frequency is configurable to fit your industry and how fast your sources move. For controlled demos, it can also run against a fixed snapshot so results stay identical every time.

The engine is built to respect how sources are used. It keeps each item tied to its origin and can be configured to favor short, attributed extracts with links back to the source rather than republishing full content wholesale. Which sources you aggregate, and the terms under which you may use them, are decisions you make with your own legal advisors — we help you configure the engine to match those terms, but we do not determine or clear licensing on your behalf.

Because we build on open models you can run yourself, you’re never dependent on a single provider’s decisions. If a hosted provider changes pricing, retires a model, or has an outage, you can run the same model on your own hardware. That independence is a core design choice, not an afterthought.

No. The engine is the “brain” that does the work; your website (for example, WordPress) is the “skin” that presents it. They connect through a clean interface, so your site stays your site and the engine can be upgraded or moved without touching your public pages.

Yes. The industry is a configuration, not a rebuild — the sources, topics, and terminology are set per field. We prove the concept in one vertical first, then the same engine adapts to yours by swapping in your sources and taxonomy.

A proof-of-concept can stand up quickly because it starts on a hosted service — no waiting on hardware. The timeline to a full production deployment depends on your sources, your industry, and your data-control needs. We map the specifics during a short discovery conversation.

Cost depends on scope — how much content you aggregate, whether you run on hosted or owned hardware, and the scale of the production deployment. Rather than quote a number that may not fit your situation, we size it with you during discovery so the figure reflects what you actually need. A lightweight proof-of-concept is intentionally low-commitment to start.

Not to use it — your team interacts with it through your website like any other page. Running the engine itself takes some technical setup, which we handle or guide, and a production system on owned hardware benefits from occasional technical oversight. We scope the right level of support with you rather than assume you have a team in place.

You do. The sources you aggregate, the setup we build, and the outputs the engine generates are yours. Building on open models and, in production, your own hardware means there’s no third party holding your data or your system hostage.