Enterprise AI Assistant. Fully Private. Fully Yours.
We deploy an LLM-powered AI assistant inside your own AWS, Azure, or GCP environment, connected to your internal data sources. Your employees get the power of ChatGPT — configured for your data, running on your cloud compute, with zero egress by default.
Your internal knowledge base, searchable by AI. Deployed on your servers.
Most enterprise AI tools are SaaS products — your employees' questions, your internal documents, and your business context are all processed on someone else's servers.
We deploy AI Chatbot using retrieval-augmented generation (RAG) to answer questions from your actual documents — connected to Confluence, SharePoint, PDFs, databases, or any file store you use.
We configure role-based access so departments only see their own data, integrate with Slack or Teams, and deploy everything on your own AWS, Azure, or GCP environment. After handoff, your team operates it independently.
Built for Enterprise. Not a Demo.
LLM on Your Own Compute
Run OpenAI-compatible models, local models via Ollama, or your own fine-tuned models on your own GPU or CPU infrastructure.
Connect Your Data Sources
Ingest internal documents, wikis, databases, and file stores. The chatbot answers from your data, not the public internet.
Zero Data Egress by Default
No data leaves your environment unless you explicitly configure an external LLM API. Private by design, not by configuration.
Role-Based Access Control
Assign permissions per team, department, or data source. Employees only see what they're authorized to see.
RAG-Powered Retrieval
Built on retrieval-augmented generation — answers are grounded in your actual documents, not hallucinated from training data.
Slack & Teams Integration
Deploy the chatbot directly into Slack or Microsoft Teams via webhook integration. Meet your team where they already work.
Audit Logging
Full audit trail of all queries and responses. Compliance-ready logging for regulated industries.
Fine-Tuning Support
Train custom models on your internal knowledge base for domain-specific accuracy beyond general-purpose LLMs.
Multi-Tenant Deployment
Run separate instances per team or department from a single deployment, with isolated data and permissions.
Version-Controlled Config
All chatbot configuration — prompts, data connectors, model settings — is version-controlled and auditable.
Streaming Responses
Real-time streaming responses for a natural chat experience, even with large context windows.
Vector Database Support
Qdrant and Weaviate supported out of the box. Pinecone and other databases available via adapter.
We Deploy It. You Own It.
We handle the full deployment inside your environment — configured for your data, your team, and your compliance requirements. You leave with a running production system and full operational ownership.
Discovery & Architecture Review
We understand your use case, data sources, team structure, and compliance requirements. We recommend the right LLM strategy — local model, private API, or hybrid — based on your privacy and performance needs.
Configuration & Customization
We configure the chatbot for your data sources, set up RBAC per department, integrate with Slack or Teams if needed, and tune the retrieval pipeline for accuracy on your specific documents and knowledge base.
Deployment in Your Environment
We deploy the full stack — chatbot, vector database, and supporting services — inside your AWS, Azure, or GCP environment. All processing stays on your infrastructure. Your data never leaves.
Handoff & Training
We walk your admin team through the system, document the configuration, and ensure you can manage data ingestion, user access, and model updates independently.
Ready to deploy a private AI assistant?
Get in touch and we'll scope the deployment for your environment, data sources, and team.
Request a Deployment