LLM Integration for Businesses
Ethereal Horizon helps businesses put large language models (LLMs) to work inside their own products and operations. We connect models such as OpenAI GPT, Anthropic Claude, Google Gemini and open-source models to your data and software, with the security and controls a business needs.
What is LLM integration?
LLM integration means connecting a large language model to your company's data, applications and processes so it can answer questions, write, summarise and take actions using your information rather than generic internet knowledge. It usually involves retrieval (RAG) over your documents, API connections to your systems, and guardrails on what the model may say and do.
What we integrate LLMs into
- Internal knowledge assistants that answer staff questions from your policies, manuals, contracts and past tickets.
- Customer-facing chat and email assistants trained on your products, prices and help articles.
- Your own software product: AI features such as summaries, smart search, drafting and classification.
- CRMs, help desks and ERPs: auto-summarised records, suggested replies and data extraction.
- Document workflows: reading PDFs, forms and emails and turning them into structured data.
How we approach LLM integration
We start by choosing the right model for the job, balancing quality, speed, cost and where your data may be processed. Sometimes that is a frontier model through an API, sometimes a smaller or self-hosted open-source model.
Next we ground the model in your own information with retrieval-augmented generation (RAG): your documents are indexed so the model looks up relevant passages and cites them, which reduces made-up answers. We then connect it to your systems through APIs so it can read and update records where you allow it.
Finally we add guardrails and evaluation: input and output checks, permission limits, human review for sensitive actions, logging and a test set of real questions so quality is measured before and after every change.
Security and data control
- Access limited to the data each use case needs, with role-based permissions.
- Options for providers and regions that fit your data-handling requirements.
- No training of public models on your data through the business APIs we use.
- Full logs of prompts, sources used and actions taken, for review and audit.
How we work
- 1
Discovery call
We learn how the process works today, who is involved, which tools you use, and what a good result looks like in numbers.
- 2
Pilot plan
We pick one workflow, agree on the success measure (hours saved, response time, error rate) and the guardrails the AI must follow.
- 3
Build and connect
We build the solution and connect it to your existing software: email, CRM, help desk, spreadsheets, databases or internal APIs.
- 4
Supervised launch
Your team reviews the AI's work while it runs on real cases, and we tune it until results are reliable.
- 5
Measure and scale
We compare results with your starting numbers and expand to more workflows only when the pilot clearly pays off.
Frequently asked questions
Which LLM should our business use?
It depends on the task, budget, speed and data rules. We compare a few suitable models on your real examples during the pilot and recommend the one that gives the best result for the cost.
Can an LLM use our private company data?
Yes. With retrieval-augmented generation the model reads relevant passages from your documents at the moment it answers, without your data being used to train a public model.
How do you stop the model from making things up?
We ground answers in your sources, ask the model to cite them, restrict it to approved topics, and test it against a set of real questions before launch. Sensitive answers can require human approval.
Can you add LLM features to our existing app?
Yes. We build the integration through APIs and can work alongside your developers, or deliver a ready-to-use component they can maintain.
What does LLM integration cost to run?
Running costs depend on usage and the model chosen. We estimate them during the pilot using your real volumes so there are no surprises.