Private AI Assistants
Branded assistants on your data.
Specialist capabilities
Generative AI Solutions
Hexmon builds private Generative AI systems, RAG platforms, AI assistants, model integrations, fine-tuned workflows, and secure enterprise AI applications.
Overview
Hexmon builds secure Generative AI systems, RAG platforms, knowledge assistants, and AI workflows that connect with your business systems, documents, and users.
Bring the data, the users, and the use case. Hexmon will design, build, and deploy the GenAI system around them.
What we build
RAG & Knowledge Search
Grounded answers from your sources.
Workflow Automation
AI inside real business processes.
Document Summarization
Long docs into clear briefs.
AI Copilots
In-product assistants for users.
Private ChatGPT-Style Systems
Conversational AI on your stack.
Model Fine-Tuning
Models trained on your domain.
Enterprise Integrations
Connected to CRM, ERP, and tools.
Secure AI Dashboards
Controlled, observable, auditable.
System architecture
Data Sources
Docs, DBs, apps, drives.
Parsing
Extract, clean, structure.
Embeddings
Semantic representation.
Vector DB
Fast similarity retrieval.
LLM
Hosted or open-source.
Guardrails
Safety, policy, scope.
API Layer
Apps, tools, integrations.
Assistant UI
Where users meet AI.
Logs
Every prompt and answer.
Monitoring
Quality, drift, usage, cost.
Capabilities
LLM Integration
Hosted and open-source.
RAG Pipelines
Retrieval-grounded answers.
Prompt Engineering
Structured, tested prompts.
Fine-Tuning
Domain-specific behavior.
Model Evaluation
Accuracy and regressions.
Hallucination Reduction
Grounding and constraints.
Access Control
RBAC and scoped data.
Audit Logs
Full traceability.
Cost Optimization
Routing, caching, batching.
Private Deployment
Your cloud, your boundary.
How we deliver
Strategy
Use cases and outcomes.
Data Readiness
Sources, access, quality.
Architecture
Models, RAG, boundaries.
Prototype
Working slice end-to-end.
Integration
Connect to real systems.
Evaluation
Accuracy, safety, cost.
Deployment
Private or cloud rollout.
Optimization
Tune, observe, improve.
Built for
Enterprise knowledge assistants
Document-heavy organizations
Customer support automation
Internal copilots
Private search
Workflow automation
Compliance-sensitive AI
Frequently asked questions
Can GenAI be deployed privately?
Yes. Hexmon deploys GenAI inside your cloud, a private VPC, or on-prem — with your data, your keys, and a clearly defined boundary that nothing crosses.
Do you use OpenAI or open-source models?
Both. We pick the model per use case — hosted models like OpenAI, Anthropic, or Gemini, or open-source models like Llama, Mistral, and Qwen when privacy, cost, or control demands it.
How do you reduce hallucinations?
Through grounding (RAG over your real sources), scoped prompts, guardrails, structured outputs, evaluation suites, and citations so users see where answers came from.
Can GenAI connect to our CRM, ERP, or internal tools?
Yes. The assistant sits behind an API layer that connects to CRMs, ERPs, databases, ticketing systems, and internal tools — with role-based access on every action.
How do you control GenAI cost?
With model routing (cheap models for easy work, strong models for hard work), caching, batching, prompt discipline, and token-level observability so cost is tracked alongside quality.
What happens after deployment?
We monitor accuracy, drift, latency, and usage, run regular evaluations, and tune prompts, retrieval, and models as the data and user behavior evolve.
Private Gen Ai Architecture
Source Documents
Raw Enterprise Content Steps: PDFs Docs Manuals Internal knowledge
Ingestion / Parsing
Content Structuring Steps: OCR / extraction Chunking Metadata Cleaning
Embeddings
Semantic Encoding Steps: Vector generation Semantic mapping Representation Indexing prep
Vector Store
Knowledge Index Steps: Similarity search Knowledge base Private storage Indexing
Retrieval Layer
Relevant Context Steps: Query matching Relevant chunks Contextual fetch Source recall
Prompt Orchestration
Instruction Assembly Steps: Prompt templates Role instructions Tool calling Response shaping
LLM Engine
Generation Core Steps: Inference Reasoning Completion Answer generation
Guardrails
Safety & Policy Steps: Moderation Access policies Output filtering Compliance checks
User Application
End-user Access Steps: Chat interface Enterprise portal API delivery Business workflows