LLM Systems & AI Integration
Private deployments, retrieval architectures and evaluation harnesses.
Private model deployments, retrieval architectures over your proprietary data, fine-tuning pipelines, and rigorous evaluation harnesses. We have built knowledge retrieval over 11 years of legal matter archives, claims intelligence layers trained on six years of historical settlement data, and reporting engines trained on house-style analytical frameworks.
- →Private cloud or on-prem LLM deployment
- →Hybrid retrieval over proprietary archives
- →Fine-tuning against your house style and playbooks
- →Evaluation harnesses with weekly accuracy tracking
- →Provenance-linked outputs (no black boxes)
LLM integration built for production, not demos
Most LLM implementations in production fail for one of three reasons. The model is not grounded in proprietary data and hallucinates. The system has no evaluation harness and nobody knows when quality degrades. Or the architecture was optimised for the demo and breaks under real operational load.
Zeacle builds LLM integrations differently. Every deployment starts with the question: what does this system need to do reliably under real conditions? The answer determines the architecture. Private deployment or public endpoint. Fine-tuned model or retrieval-augmented generation. Streaming or batch processing. Evaluation harness or spot-checking. Every decision is driven by the operational requirement, not the technology trend.
What LLM integration actually means
LLM integration is the process of embedding a large language model into an existing system or workflow so it performs a specific task reliably. The tasks most appropriate for LLM integration are those requiring natural language understanding, generation, reasoning, or retrieval over unstructured data.
Document intelligence covers reading contracts, claims, clinical notes, or reports and extracting structured insight. Knowledge retrieval makes years of institutional knowledge queryable in plain English. Content generation drafts correspondence, reports, summaries, and status updates in your house style. Classification and routing categorises incoming documents, queries, or cases and routes them to the right destination.
RAG systems | Making private data queryable
Retrieval Augmented Generation is the architecture that makes your proprietary knowledge accessible to a language model without requiring fine tuning on every document and without the risk of hallucination that comes from asking a general model questions it cannot reliably answer.
A RAG system works by embedding your documents into a vector database and retrieving the most relevant chunks before passing them to the language model along with the query. The model generates a response grounded in your actual documents, cited back to the source. The law firm knowledge retrieval system we built is a RAG deployment over 11 years of matter archives, precedent clauses, and partner opinions. A fee earner can now ask, in plain English, which indemnity language have we used against regulated financial clients and what were the outcomes, and receive a cited, accurate answer in under 60 seconds.
Private LLM deployment
For regulated industries and businesses handling sensitive data, public LLM endpoints are not an acceptable architecture. Every prompt sent to a public API is processed by infrastructure you do not control, under terms you cannot fully audit, with data residency implications that may violate your compliance obligations.
Zeacle deploys private LLM infrastructure inside your controlled environment. Your data does not leave your cloud boundary. The model version is fixed and audited. The infrastructure is your infrastructure, observable and compliant. This is the architecture we use by default for legal, insurance, healthcare, and financial services clients.
LLM integration case study
The knowledge retrieval agent deployed for the law firm is the clearest example of what production LLM integration looks like. The agent is a RAG deployment over a hybrid vector store (Postgres with pgvector) built on 11 years of matter archives, negotiated contracts, and internal legal opinions.
Retrieval time dropped from an average of 47 minutes to under 60 seconds. The agent is evaluated weekly against a curated test set of known queries with known correct answers. The override rate from fee earners has stabilised at 12-16%, indicating the system is trusted but appropriately challenged.
We work with OpenAI models (GPT-4, GPT-4o), Anthropic models (Claude), Mistral models, and open source models including Llama variants. Model selection is driven by the specific task requirements, data privacy constraints, and performance benchmarks against your actual use case. We do not have a preferred vendor. We use whatever performs best for your problem.
For regulated industries and sensitive data, we default to private deployment inside your controlled environment. No prompts, no documents, and no client data leave your cloud boundary. For use cases where public endpoints are acceptable, we use API contracts that exclude your data from training and maintain clear data processing agreements.
RAG retrieves relevant documents at query time and provides them as context to the model. Fine tuning trains the model weights on your specific data. RAG is preferable when your knowledge base changes frequently, when you need cited provenance for model outputs, and when you need to avoid the cost and complexity of maintaining fine-tuned model versions. Fine tuning is preferable when you need the model to acquire a specific style, tone, or domain knowledge that is stable over time.
Yes. We deploy against your provider of choice, including open-weight models in your own cloud, and architect for portability so you are not locked in.