Critical information scattered
The company has useful documents, manuals, processes and data. But finding, understanding and applying them still depends on people, folders or internal memory.
> private_system
Private AI Knowledge Hub
Your knowledge.Your private AI.
Turn your company's documentation into useful answers for your team. With specialist assistants, visible sources and permission-based access.
Start with one department, your documents and real questions.
Design and implementation, directly with me.
What do we need to onboard someone?
Prepare role-based access, complete the onboarding checklist and review information-use policies with the person responsible. [01–03]
Onboarding guide · Preparing for day one.
Select a source to trace its origin.
The knowledge is there. Access falls short.
Manuals, processes, pricing and expertise: the knowledge already exists. But it is spread across files, tools and key people. While finding an answer remains difficult, teams turn to public AI tools to resolve internal questions.
Scroll to continue, or choose a problem above.
Your knowledge. Within your team's reach.
A private AI environment where each team consults the documentation it needs, with specialist assistants and access permissions defined by your company.
See how it works> solutionPRIVATE AI KNOWLEDGE HUBIllustrative demonstration
Explore the three layers of your Hub
Documentation organised by department, role or function. A shared foundation for finding answers without relying on scattered files or whoever knows where they are.
Less searching. More accessible knowledge.Specialist assistants that consult each area's sources. Your team can check the answer and review its origin; the AI has limits too.
Useful answers, with evidence to review.Users, permissions, usage limits and models chosen for the sensitivity of your information. Each team accesses the knowledge relevant to its role.
Available information, not unrestricted access.YOUR KNOWLEDGE. YOUR RULES.
The right information. For the right person. With the right permission.
Documents → query → sourced answer
Step through an example query to see how the system finds relevant context, builds a concise answer and keeps the origin of each claim visible.
01 / Prepared documents
Knowledge enters as structured context, not as an unexplained folder.
Documents are reviewed, split into useful fragments and organised with metadata and permissions before the team can query them.
PDF · DOCX · EMAIL · 24 fragments · access: support
02 / Relevant context
The question connects to the fragments that can answer it.
“What return window applies to opened equipment?”
The system retrieves specific passages based on content and permissions. It does not seek a generic answer outside the authorised knowledge base.
Return window: 14 days Support case required before shipping
03 / Verifiable sources
The answer keeps its sources in view.
Opened equipment can be returned within 14 days if all accessories are present. A support case with the order number must be logged before shipping.
Highlighted origin: Returns policy 2026, section 2.1, page 4.
Highlighted origin: Returns policy 2026, section 2.1, page 4.Highlighted origin: Technical support email, thread 18, order LO-2841.What gets built behind this flow
Document preparation
We review, clean, structure and organise documentation so the system can retrieve it usefully. Uploading files isn't enough.
Knowledge Zones
We create knowledge areas by department, function or access level: Management, Sales, Technical Support, HR, Operations, Marketing, Product, Legal/Compliance.
Specialised assistants
We configure assistants with specific instructions, sources and limits. A sales assistant is not the same as one for HR or management.
Model routing
We select which models to use depending on the case: local for sensitive information, European for private cloud environments, advanced global for non-sensitive tasks.
Evolution
The system can start as a PoC and evolve towards connections with CRM, ERP, databases, automations, reporting or APIs.
Three ways to deploy Private AI Knowledge Hub
Each company has a different level of sensitivity, budget and technical maturity. That's why we don't offer a single closed architecture.
Private instance · European infrastructure
Private instance · European infrastructureKnowledge stays inside the client perimeterSensitive data local · advanced capacity routed 01
Cloud EU Private Hub
Private managed instance on European infrastructure. Ideal for companies that want to validate quickly without installing their own hardware, maintaining private and controlled architecture.
Included in this architecture
- Private environment per client
- European servers
- Knowledge areas
- Users and permissions
- Controlled cloud models
- RAG-ready documentation
- Managed support
Best fit
PoCs, B2B companies, startups, management teams, internal training, commercial documentation.
02
On-Prem Knowledge Box
Local installation or dedicated appliance within the client's infrastructure. Ideal for companies with sensitive documentation, industrial property or maximum data sovereignty needs.
Included in this architecture
- Hardware or dedicated server
- Local models
- Local knowledge base
- Department areas
- Access inside the client network
- Maintenance and support
Best fit
Industry, engineering, advanced manufacturing, technical documentation, intellectual property, environments with strong privacy requirements.
03
Hybrid Intelligence Architecture
Hybrid architecture combining local models, European cloud and advanced global models with usage limits. Ideal for companies wanting privacy for sensitive data but also advanced capability.
Included in this architecture
- Private RAG
- Local models for sensitive documentation
- European models for corporate use
- Limited global models
- Internal permissions
- Differentiated query routes
Best fit
Management teams, growing companies, mixed departments, companies that need to balance privacy and advanced capability.
Use cases by department
Select an area to see what knowledge its assistant would query and a realistic question it could resolve.
Hub function
Executive / Strategy Hub
Assistant for management that summarises reports, connects previous decisions, analyses risks and helps prepare strategic meetings.
Example query
Summarise the main risks from the last quarter and propose three topics for the next management meeting.
Hub function
Sales Knowledge Hub
Sales assistant querying pricing, arguments, technical sheets, commercial conditions, objections and previous proposals.
Example query
An industrial client asks for a discount and has maintenance issues. Which product should we recommend and with what argument?
Hub function
Technical Support Hub
Support assistant querying manuals, error codes, procedures, compatibilities and technical documentation.
Example query
The machine shows error E-42 during startup. What does it mean and what steps should the technician follow?
Hub function
HR & Onboarding Hub
Internal assistant for onboarding questions, internal policies, travel, expenses, procedures and corporate culture.
Example query
A new employee starts on Monday. What steps should they complete during their first two weeks?
Hub function
Operations Knowledge Hub
Assistant for querying procedures, checklists, incidents, maintenance, quality or internal instructions.
Example query
What procedure should we follow if a quality control deviation is detected?
Hub function
Marketing & Market Intelligence Hub
Assistant for querying campaigns, reports, positioning, competition, customer insights and brand documentation.
Example query
Summarise the learnings from the latest campaigns and what we should test next.
O.R.B.I.T. Hub Method
From scattered documentation to private knowledge system.
We don't start by deploying technology. We start by understanding what knowledge should be available, who should query it and under what limits.
Phase 01
Observe
We analyse the company context: departments, documentation, tools, users, data sensitivity and priority use cases.
Phase outcome
Initial knowledge map and pilot scope.
Phase 02
Refine
We prepare documentation for AI: cleaning, classification, structure, metadata, versions and separation by areas.
Phase outcome
Documentary base ready to become queryable knowledge.
Phase 03
Build
We configure the private environment: knowledge areas, specialised assistants, users, permissions, models and deployment architecture.
Phase outcome
Functional Private AI Knowledge Hub.
Phase 04
Integrate
We validate the system with real users, test questions, sources, limits and use cases by department.
Phase outcome
Operational pilot with feedback and initial metrics.
Phase 05
Tune
We adjust assistants, documents, models, prompts, permissions and flows to improve precision, utility and adoption.
Phase outcome
Optimised system and evolution roadmap.
Designed for companies that need private AI, organised knowledge and internal control
Choose the profile that best describes your situation. You will immediately see where the Knowledge Hub fits and what changes for your team.
This sounds like you if
For CEOs
You want your company to use AI without losing control over documentation, decisions, processes and critical knowledge.
What changes
Faster queries, less dependence on key people and better decision-making.
This sounds like you if
For CTOs
You need a private, manageable architecture ready for permissions, models, security and technical evolution.
What changes
Corporate AI without improvisation or data leakage.
This sounds like you if
For industrial companies
You have manuals, technical sheets, processes and sensitive industrial property.
What changes
Private AI to query technical knowledge without exposing critical information.
This sounds like you if
For commercial teams
You want sales to query correct, updated and consistent information without always depending on management.
What changes
Faster responses and more consistent arguments.
This sounds like you if
For HR and training
You want to facilitate onboarding, internal policies, procedures and frequent answers without saturating the people team.
What changes
Better informed employees and fewer repetitive queries.
This sounds like you if
For innovation teams
You want to validate corporate AI with a real, controlled and measurable case before scaling to the entire company.
What changes
Serious, defensible PoC ready to grow.
What we deliver
Eight deliverables organised into three groups. The exact scope adapts to the company's technical context, privacy and documentary maturity.
Private foundation
Infrastructure, knowledge and documents ready to operate.
Private AI Environment
Private environment on European cloud, own server, local appliance or hybrid architecture.
Knowledge Zones
Areas by department or function, with separate documentation, permissions and access.
Document Preparation Pipeline
Clean, structured documentation prepared for precise retrieval.
Intelligence and control
Assistants, models and permissions configured with clear boundaries.
Specialized AI Assistants
Assistants configured for specific roles and sources.
Model Strategy
Model selection according to sensitivity, cost and performance.
Access & Usage Rules
Users, permissions and usage limits configured.
Validation and adoption
A pilot tested with users and prepared for adoption.
Pilot Validation
Tests with real questions, users and initial metrics.
Training & Onboarding
Training for key users and a practical usage guide.
Private AI yes. Black box without control, no.
It is designed to assist and retrieve. It does not replace human judgement or automate critical decisions without oversight.
Privacy and access Data stays where it belongs and each person sees only what is authorised.
GDPR-first infrastructure
European infrastructure by default, with access control and data minimisation.
Role-based access
Each person accesses only the knowledge required for their role.
Models and oversight Sensitivity determines which models intervene and which actions require human judgement.
Local or hybrid models
Sensitive data can remain local; cloud models are limited to authorised tasks.
Human-in-the-loop
The system assists; important decisions remain under human review.
Evidence and evolution Every answer can be traced and the system grows deliberately.
Evidence-first answers
Every answer shows its origin and acknowledges when evidence is insufficient.
Controlled evolution
Document retrieval first; integrations and automations follow when they add value.
> technical_partner
Development in technical collaboration
Private AI Knowledge Hub is being developed with technical collaboration from Worklab Argentina in areas of infrastructure, deployment and development.
LowOrbits leads the product vision, functional architecture and implementation strategy.
Worklab Argentina
Technical development partner
> request_pilot
Build your first private knowledge pilot.
Tell us what documentation you want to centralise and how many people would access the system.
Frequently asked questions
Select a question to read the answer.
What is Private AI Knowledge Hub?
A private corporate AI platform to query documentation and internal knowledge via specialised assistants, organised by knowledge areas and permissions.
Is it a chatbot?
Not exactly. The interface may be conversational, but the real product is the complete system: prepared documentation, knowledge areas, specialised assistants, private architecture and usage rules.
Where is the information hosted?
By default, on European infrastructure. It can also deploy on a private server, local appliance or hybrid architecture depending on sensitivity and requirements.
Is it GDPR compliant?
It's designed with a GDPR-first approach: European infrastructure by default, access control, exposure minimisation and separation by areas.
Can local models be used?
Yes. For sensitive information or industrial environments, local models can be used. For non-sensitive tasks, cloud models can be enabled with specific permissions.
Can it be organised by departments?
Yes. That's one of the foundations of the product. Each department can have its own Knowledge Zone, documentation, assistant, permissions and limits.
What happens if documentation is disorganised?
That's normal. Part of the service involves preparing, classifying and structuring documentation. If data is in complete chaos, we start with a documentary preparation phase.
Can the system connect to CRM, ERP or databases?
Yes, but normally not in the first step. We recommend starting with documentary query and then evolving towards connections with internal systems.
Do you recommend starting with Orbit Discovery Sprint?
If the company isn't clear on which department, documents or use case to prioritise, yes. If there's already a clear case, you can start directly with a PoC.
> next_route
Two sound ways to start.
If it's not sure which department or documentation to prioritise, we recommend starting with Discovery.
Start with one area. Demonstrate value. Scale with control.
Private AI Knowledge Hub doesn't require transforming the entire company from day one. Start with one department, a set of documents and real users. Validate utility, precision and adoption. Then decide whether to scale to more areas, local models, integrations or automations.