Grounded answers, with citations.
Retrieval looks across your documents, the model composes the answer, and every claim is anchored to a source your reviewers can verify.
- Cites the exact source for every assertion
- Access-checked against the asking user
- Logged end-to-end for audit + improvement
LLM.co enables fully private, domain-specific AI chatbots trained on your internal documents, support data, and brand voice—deployed securely on-premise or in your VPC. Whether for internal teams or customer-facing portals, our chatbots deliver accurate, on-brand responses using retrieval-augmented generation, role-based access, and full control over tone, behavior, and data exposure.
Enterprise AI Features
LLM.co delivers fully private, highly customizable chatbots powered by your data, your workflows, and your tone of voice. Whether you're enhancing internal support, automating customer communication, or embedding AI into your product, our custom chatbot engine gives you total control—deployed securely on-premise or in your virtual private cloud, with no reliance on public APIs or shared models.
Why Teams Choose LLM.co for Custom Chatbots
Built with Your Data. Hosted in Your Environment. Our chatbots are trained on your internal documentation, product specs, SOPs, knowledge bases, and brand content—so responses are accurate, contextual, and consistent with your operations. Deploy them entirely within your infrastructure to maintain compliance, privacy, and ownership.
Completely Customizable Tone, Behavior, and Workflows. From casual support agents to formal legal assistants, our chatbots reflect your brand voice, escalation rules, and industry-specific terminology. You define how they respond, what data they can access, and when they hand off to a human.
Integrate Anywhere You Need Conversations. Embed LLM.co-powered bots on your website, in-app experience, internal tools, or customer portals. Seamlessly connect with CRMs, help desks, file systems, and databases for real-time answers and end-to-end automation.
Retrieval-Augmented Generation for Accuracy. All chatbot responses are grounded in your documents using RAG pipelines. That means no hallucinated answers—just reliable, cited responses with links to the source content your team or users can verify.
Role-Based Access and Scoped Capabilities. Create bots for specific departments or functions—HR, IT, legal, customer support, operations—with granular access to only the data they need. Support both internal and external use cases while maintaining full control over permissions and data boundaries.
Secure by Design. Unlike SaaS chatbot builders that rely on third-party models, LLM.co keeps your data private and your conversations protected. Perfect for regulated industries and organizations with strict compliance requirements.
Key Use Cases
Internal IT & HR Chatbots. Answer questions about policies, onboarding, benefits, software access, and support requests. Reduce ticket volume and help employees get what they need, instantly.
Customer Support Assistants. Handle common FAQs, order issues, product inquiries, and service policies—on-brand and 24/7. When needed, escalate seamlessly to human agents with full conversation history.
Sales Enablement Bots. Empower reps with instant access to product specs, case studies, pricing logic, and objection handling—directly within the tools they already use.
Legal & Compliance Chatbots. Surface relevant contract clauses, explain internal policies, or assist with compliance Q&A. Enable legal teams to operate more efficiently and reduce bottlenecks.
Developer & Product Documentation Bots. Help engineering teams and end users search across technical docs, code references, changelogs, and architectural decisions in seconds.
Client-Facing Knowledge Agents. Create domain-specific, permission-controlled bots for clients or partners to ask questions about documentation, contracts, onboarding, or deliverables.
Chatbots That Are Smart, Secure, and Fully Yours
Private hosting on-prem or in your VPC
BYOD ingestion from PDFs, emails, wikis, spreadsheets, and databases
Role-based access and scoped data permissions
Retrieval-augmented generation with source citations
Native integrations with Slack, Teams, Webflow, HubSpot, Salesforce, and more
Custom UI or headless deployment options for flexible embedding
Full observability and audit logs via Model Context Protocol (MCP)
Who Uses LLM.co for Custom Chatbots
Enterprise IT and HR departments looking to automate internal requests
Customer service teams aiming to scale without sacrificing brand tone
Legal, compliance, and finance teams needing secure, traceable Q&A
Product and engineering teams searching across deep documentation stacks
SaaS platforms and agencies embedding branded bots into client portals
Deliver Conversations That Convert—and Comply
LLM.co gives you the power to build intelligent, on-brand chatbots that respect your data boundaries and business rules. From internal tools to client-facing automation, you'll get AI assistants that truly work for your organization—and no one else's.
Guardrails, Multilingual Support, and Governed Escalation
Enterprise deployments require more than accurate responses—they demand predictable, policy-aligned behavior at scale. LLM.co custom AI chatbots are built with configurable guardrails that enforce topic boundaries, flag out-of-scope queries, and prevent hallucination by grounding every response through a RAG pipeline against your verified document corpus. Llama Guard-compatible safety layers can be added for regulated environments requiring HIPAA, SOC 2, or PCI DSS alignment.
Multilingual support enables a single deployed chatbot to serve global teams and customers across languages without maintaining separate model instances. When queries exceed configured confidence thresholds or touch flagged topics, structured escalation routes conversations—with full history—to a human agent via Slack, Microsoft Teams, or your existing help-desk workflow. Data never leaves your controlled environment throughout the handoff.
Channel Integrations and Fine-Tuning for Domain Depth
LLM.co chatbots deploy across every channel your organization already uses—website widgets, in-app embeds, Slack, Microsoft Teams, SharePoint, Confluence, ServiceNow, Salesforce, and HubSpot—through a headless API or pre-built connectors. Role-based scoping ensures that each channel surfaces only the data its audience is authorized to access, enforced at the retrieval layer rather than the UI. Explore how internal search and knowledge-base assistants extend the same architecture across your documentation and operational content.
For use cases where a well-tuned retrieval layer is not sufficient—highly specialized terminology, domain-specific reasoning chains, or proprietary classification tasks—fine-tuning on your own data sharpens model behavior without exposing training data to third-party infrastructure. All model weights and conversation logs remain under your data privacy controls, deployed in an air-gapped or VPC environment that satisfies enterprise governance requirements.
Common questions
01Does my data leave my environment when using LLM.co custom AI chatbots?
No. LLM.co deploys entirely within your on-premise infrastructure or virtual private cloud. Conversation data, retrieved documents, and model weights remain inside your security perimeter at all times. There are no calls to shared public APIs and no third-party model providers that retain your data.
02Can the chatbot support multiple languages without separate deployments?
Yes. Multilingual capability is built into the base model layer and the RAG retrieval pipeline, allowing a single deployment to handle queries in multiple languages. Document ingestion supports multilingual corpora, and responses are generated in the language of the user's query without requiring separate model instances per language.
03How does human escalation work, and what context is passed to the agent?
Escalation is triggered by configurable rules—confidence thresholds, topic guardrails, or explicit user requests. When a conversation escalates, the full session history, retrieved source documents, and any structured metadata are passed to the receiving agent via Slack, Microsoft Teams, or your help-desk integration. No context is lost during the handoff.
04What is the difference between RAG and fine-tuning for a custom AI chatbot, and which does LLM.co use?
RAG retrieves relevant documents at inference time and grounds the model's response in your verified content—ideal for keeping answers current as your knowledge base evolves. Fine-tuning adjusts model weights on domain-specific data to improve reasoning on specialized terminology or classification tasks. LLM.co uses RAG as the default architecture for accuracy and maintainability, and offers fine-tuning as an additional layer for use cases where retrieval alone is insufficient.
Private AI On Your Terms
Tell us your use case and constraints — on-prem, cloud, or edge — and we'll map a compliant deployment within one business day.
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