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Train AI On
Your Business.
A private AI trained on your documents, policies, and processes — running on your infrastructure, answering from your actual knowledge, with your data never leaving your environment.
The Problem
Most Business Knowledge is Invisible to the People Who Need It.
It exists in documents nobody can find, in the heads of employees who might leave, and in email threads buried three years back. Private AI makes it accessible.
Information scattered across documents
Procedures, specs, pricing, and policies exist in PDFs, folders, and email threads that nobody can find when they need them.
Employees asking repetitive questions
The same questions get asked again and again — to managers, to senior staff, to whoever happens to know the answer that week.
Slow onboarding
New staff take weeks to get productive because institutional knowledge lives in people's heads, not in a system they can query.
Knowledge silos
What the operations team knows, the sales team doesn't. What engineering documents, support never sees. Knowledge doesn't move.
The Solution
AI That Knows Your Business. Not the Internet.
Polemica builds Private AI systems using RAG — Retrieval-Augmented Generation. Your documents are indexed into a private vector database. When a user asks a question, the AI retrieves the most relevant documents from your knowledge base and generates an answer grounded in your actual content, with source citations.
No hallucination from general internet knowledge. No data sent to third-party servers. No answers that contradict your own procedures. The AI retrieves from what you have indexed and nothing else.
Faster employee onboarding
New staff query the knowledge base instead of interrupting experienced colleagues. Ramp time drops significantly.
Consistent answers
Everyone gets the same answer to the same question — from the same source, with the same level of accuracy.
Better customer support
Support staff resolve queries faster when the AI retrieves the right documentation and drafts the response.
Knowledge retention
When a key employee leaves, their knowledge stays — indexed, searchable, and available to whoever replaces them.
Use Cases
Four Ways to Deploy Private AI.
Internal Knowledge Base
An AI trained on your documents, procedures, policies, and historical decisions — so staff get instant answers without searching or asking.
- Searches across all company documents
- Answers with source citations
- Always up to date when documents change
- Role-based access control
AI Sales Assistant
An AI trained on your products, pricing, objection responses, and sales playbooks — giving your team instant answers mid-conversation.
- Product and pricing lookup
- Objection handling prompts
- Proposal and quote drafting
- CRM note drafting after calls
AI Support Assistant
An AI trained on your support documentation, common issues, and resolution procedures — resolving customer queries without escalating to staff.
- Resolves tier-1 support queries
- Drafts responses from your knowledge base
- Escalates with context when needed
- Learns from resolved tickets
AI Business Coach
An AI that knows your KPIs, goals, and business context — available on demand for decision support, analysis, and planning.
- Reviews KPIs and flags anomalies
- Answers questions about your business data
- Drafts reports and summaries
- Coaches based on your defined goals
Technology
Built on Proven Technology.
Open-source components where possible. Enterprise models where capability matters. Local deployment where privacy requires it.
RAG
Retrieval-Augmented Generation — connects the AI to your documents so it retrieves relevant content before generating a response. Keeps answers accurate and grounded in your actual data.
LangChain
Open-source framework for building AI pipelines — chaining retrieval, reasoning, and tool calls into a coherent workflow.
Vector Databases
Stores document embeddings for semantic search — the engine that lets the AI find relevant content by meaning, not just keywords.
OpenAI
GPT-4 and GPT-4o as the reasoning model — used via private API with your data never used for model training.
Anthropic
Claude as the reasoning model — strong on long-document tasks and safety-conscious output for internal business use.
Local Models
LLaMA, Mistral, and Phi models deployed on your own VPS or on-premise server — zero data leaves your infrastructure.
Comparisons
How It Compares
Private AI vs ChatGPT
Private AI
- Trained on your specific documents
- Runs on your infrastructure
- Data never leaves your environment
- Answers cite your actual sources
- Access controlled per user role
ChatGPT
- General knowledge only
- Data processed on OpenAI servers
- Prompts may train the model
- Answers may be hallucinated
- No access control
ChatGPT works for general tasks. Private AI works when the answer must come from your specific knowledge.
RAG vs Fine-Tuning
RAG
- No model retraining required
- Documents update instantly
- Lower cost to deploy
- Answers cite sources
- Works with any base model
Fine-Tuning
- Requires retraining on new data
- Stale until retrained
- Higher compute cost
- No source citations
- Tied to a specific model version
RAG is the right approach for most business knowledge bases. Fine-tuning suits narrow, high-frequency tasks.
Local LLM vs Cloud LLM
Local LLM
- Zero data leaves your server
- No per-token API cost
- Works air-gapped (offline)
- Full infrastructure control
- Lower capability on smaller models
Cloud LLM
- Data sent to API provider
- Per-token usage cost
- Requires internet connection
- Provider controls infrastructure
- Highest model capability available
Choose local for strict data privacy. Choose cloud for maximum capability with private API access.
Industries
Where Private AI Has the Most Impact.
Manufacturing
Maintenance manuals, quality procedures, and compliance documentation — searchable by anyone on the floor.
Construction
Spec sheets, safety procedures, subcontractor requirements, and project documentation queried in the field.
Engineering
Technical documentation, design standards, and project knowledge indexed and searchable across the team.
Healthcare
Clinical protocols, compliance policies, and administrative procedures — private, secure, and always current.
Professional Services
Client knowledge, case files, methodology playbooks, and onboarding materials available on demand.
Common Questions
What People Ask Before They Start.
20 questions covering what Private AI is, how it works, what it costs, and when it is — and is not — the right approach.
Private AI is a language model system trained or augmented with your specific business knowledge — documents, procedures, pricing, and data — that runs on your own infrastructure or a dedicated private environment. Unlike public AI tools, your data never leaves your control and the AI answers from your actual content, not general internet knowledge.
Get Started
Give Your Team AI That Knows Your Business.
We scope the knowledge base to your document set, your use case, and your infrastructure — so you know exactly what you are getting before we build anything.