Enterprise Document Search
Find anything across your stack.
Specialist capabilities
Private RAG & Knowledge Systems
Hexmon builds private RAG systems, knowledge assistants, document search, enterprise AI search, vector databases, and secure data retrieval systems.
Overview
Hexmon builds private knowledge systems that help teams search documents, retrieve trusted context, and generate answers from controlled business data.
Hexmon will design, build, and deploy a private retrieval system grounded in the data you already trust.
What we build
Internal Knowledge Assistants
Q&A grounded in your docs.
Private RAG Systems
Your data, your boundary.
Policy & SOP Assistants
Answers from real procedure.
Support Knowledge Bots
Resolve from cited sources.
Legal & Compliance Search
Clause-level retrieval.
Training & Onboarding
Self-serve, always current.
Secure File-Based AI Search
Permissioned by source.
System architecture
Documents
Files, pages, records.
Parsing
Text, tables, structure.
Chunking
Right-sized segments.
Embeddings
Vector representation.
Vector DB
Indexed and filtered.
Retrieval
Top-k with rerank.
LLM
Compose grounded answer.
Guardrails
Scope, policy, safety.
Cited Answer
With sources attached.
Sources
PDFs
Word Documents
Spreadsheets
Databases
Websites
Knowledge Bases
CRMs
Internal Portals
APIs
Control
Access Permissions
Source-level ACLs.
Role-Based Retrieval
Scope by user role.
Audit Logs
Every query traceable.
Private Deployment
VPC, on-prem, air-gapped.
Source Citations
Every answer attributed.
Hallucination Reduction
Grounded, scoped output.
Data Isolation
Tenant and project walls.
Monitoring
Drift, quality, usage.
How we deliver
Data Audit
What exists, what matters.
Knowledge Design
Structure, ownership, scope.
Retrieval Architecture
Index, chunking, rerank.
Prototype
End-to-end on real docs.
Evaluation
Accuracy, citation, latency.
Deployment
Private or cloud rollout.
Monitoring
Tune, refresh, improve.
Frequently asked questions
What is RAG?
Retrieval-Augmented Generation grounds an LLM in your own data. Instead of relying on what the model memorized, the system retrieves relevant passages from your documents at query time and uses them to compose a cited answer.
Can it search private documents?
Yes. The system ingests private documents — PDFs, Word, spreadsheets, databases, internal portals, APIs — into a controlled index that only your users and your assistant can query.
Can answers include source references?
Yes. Every answer can carry inline citations linking back to the source document, page, or row, so users can verify and trust what the assistant returned.
Can different users see different knowledge?
Yes. Role-based retrieval, source-level ACLs, and tenant isolation ensure each user only sees results they are permitted to access — enforced at retrieval, not just in the UI.
Can this run on-prem or privately?
Yes. Deployments support private cloud, on-prem servers, and air-gapped environments — with your data, your keys, and a clearly defined boundary that nothing crosses.