↓ Skip to main content
Core-AI

Enterprise AI Chatbots

Table of Contents
Custom AI assistants trained on your internal data, running entirely on your infrastructure. Built for enterprises that need accurate, context-aware conversational AI without sending sensitive data to third-party APIs.

What We Build
#

Core-AI develops enterprise-grade chatbots powered by modern open-weight LLMs and Retrieval-Augmented Generation (RAG). These assistants are designed to integrate with your existing systems and knowledge base — allowing employees and customers to query complex documentation through a natural conversational interface.

Unlike commercial chatbot APIs, our deployments run on your hardware or private cloud. Your conversations, documents, and embeddings never leave your network.

Key Capabilities
#

  • Document Search — Instantly find information across large document collections, regardless of format (PDF, Confluence, SharePoint, databases).
  • Contextual Conversation — Maintain multi-turn reasoning and problem-solving context across long sessions.
  • Role-based Access Control — Ensure users only see information they are authorized to access, integrated with your existing SSO/IAM.
  • Multilingual Support — Communicate effectively across languages and regions using locally-deployed models.
  • Analytics Dashboard — Monitor usage patterns, query quality, and system performance over time.
  • Audit Trail — Every query and response is logged for compliance review (GDPR, HIPAA, SOC 2).

Architecture
# 

graph LR
  User((User)) --> CI((Interface))
  CI --> KR((Retrieval))
  KR --> LLM((Local
LLM))
  LLM --> Ans((Answer))

  classDef n1 fill:#3b82f6,stroke:#333,stroke-width:2px,color:#fff,font-size:20px;
  classDef n2 fill:#6366f1,stroke:#333,stroke-width:2px,color:#fff,font-size:20px;
  classDef n3 fill:#8b5cf6,stroke:#333,stroke-width:2px,color:#fff,font-size:20px;
  classDef n4 fill:#a855f7,stroke:#333,stroke-width:2px,color:#fff,font-size:20px;
  classDef n5 fill:#c084fc,stroke:#333,stroke-width:2px,color:#fff,font-size:20px;

  class User n1;
  class CI n2;
  class KR n3;
  class LLM n4;
  class Ans n5;

Common Use Cases
#

  • Internal knowledge assistant — Engineering, legal, and HR teams query company documentation in natural language.
  • Customer support augmentation — First-line agents get instant access to product manuals, policies, and resolution playbooks.
  • Compliance lookup — Regulated teams query regulations, contracts, and audit records with full traceability.
  • Onboarding assistant — New hires ramp up faster by querying institutional knowledge directly.

Related Services #

Frequently Asked Questions
#

How do you connect the chatbot to our existing documents and knowledge base?
We build custom connectors for the systems where your content lives — SharePoint, Confluence, Notion, PDF repositories, SQL databases, and more. Documents are ingested, chunked, and embedded into a private vector database. The chatbot retrieves relevant content in real time using semantic search, not keyword matching.
Can the chatbot enforce our existing role-based access controls?
Yes. Access controls are a first-class design requirement. We integrate with your existing SSO and IAM (Azure AD, Okta, and others) and apply document-level permissions so users only receive answers drawn from content they are authorized to see.
Does conversation data leave our infrastructure?
No. Both the language model and the vector database run on your infrastructure. Queries, responses, and retrieved document chunks are processed entirely within your network. You control all logs and configure retention policies.
What languages does the chatbot support?
Modern open-weight LLMs natively support dozens of languages. We benchmark the model on your specific languages during the Prototype phase to confirm accuracy. Multilingual RAG — retrieving documents across multiple languages — requires additional embedding configuration, which we include for multilingual deployments.
How do you measure chatbot quality and accuracy?
We set up a structured evaluation framework before launch: a golden Q&A test set, retrieval precision and recall metrics, and a feedback loop so your team can flag incorrect answers. Evaluation dashboards are part of every delivery so you can monitor quality continuously.
Discuss Your Chatbot Project