OptiChat
Enterprise domain-specialist RAG: private local LLMs, dual LoRA adaptation for jargon, and neuro-symbolic verification.
Generic frontier language models repeatedly fail when deployed in specialized industrial, legal, medical, and operational environments: they misunderstand proprietary acronyms, produce subtle hallucinations, and generate unpredictable outputs that violate strict technical standards.
OptiChat provides an engineering-first platform to build, fine-tune, and embed private specialist conversational systems grounded directly in your private data corpus. With high-performance hybrid retrieval-augmented generation (RAG), you can index complex technical manuals, internal wikis, standard operating procedures, and compliance documentation, and embed the resulting specialist chat into your existing web dashboards, internal portals, or operational workflows via a lightweight component.
To ensure peak precision on difficult technical data, OptiChat introduces dual LoRA adapter support: fine-tune the embedding model to recognize dense domain jargon and abbreviations, while adapting the generative model to enforce uniform voice, terminology, and structural formatting. To guarantee absolute reliability for mission-critical decisions, OptiChat couples neural generation with neuro-symbolic validation—running automated constraint solvers and strict schema verifiers that eliminate hallucinations and enforce mathematical and logical correctness before any answer is returned to the user.
Key Features
Architectural Highlights
Grounded domain RAG with embeddable widget
Ingest your proprietary document corpus and embed specialized chat interfaces directly into your enterprise portals and internal software workflows.
Air-gapped local LLM execution
Run completely air-gapped on your own hardware using Ollama, vLLM, or TensorRT-LLM with zero data leaving your private network.
Dual LoRA domain adaptation
Fine-tune dedicated LoRA adapters for both embeddings (mastering domain acronyms and jargon) and generation (enforcing style, structure, and tone).
Neuro-symbolic validation layer
Automated symbolic constraint solvers and schema validators eliminate hallucinations and ensure predictable, strictly typed responses.
Engineering Specs
Technical Profile
Technology Stack
Support & Details
Frequently Asked Questions
Can OptiChat run completely offline using local LLM models? +
Yes. OptiChat can be deployed as an air-gapped on-premise container using local inference engines such as vLLM or Ollama. All vector indexing, query embeddings, and generative inference execute entirely on your local hardware with zero external internet traffic.
How do dual LoRA adapters improve performance on domain-specific data? +
Standard models often misinterpret industry acronyms and specialized jargon. OptiChat allows you to attach one LoRA adapter to the embedding model to maximize retrieval accuracy on proprietary terms, and a second LoRA adapter to the generative LLM to enforce strict tone, style, and structural formatting.
How does neuro-symbolic validation prevent hallucinations? +
Rather than relying purely on probabilistic language generation, OptiChat passes generated candidate outputs through formal symbolic constraint solvers and rigid JSON schema validators. Outputs that violate factual constraints or schema contracts are rejected and corrected prior to delivery.
How do we embed OptiChat into existing enterprise workflows? +
OptiChat provides an embeddable web component (a script tag and custom HTML element) that drops directly into any internal portal, intranet, or ERP system, along with REST and WebSocket APIs for automated backend pipelines.
Official Documentation & Support
Support contact: support.optichat@4dc.services