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
> core_services
Turn your internal knowledge into a private AI platform for teams.
We implement a private corporate AI environment so your company can query documentation, processes and internal knowledge with specialised assistants, access control and GDPR-ready architecture.
Your teams already use AI or soon will. The question isn't whether they'll use it, but with what data, under what permissions and with what level of control. Private AI Knowledge Hub allows deploying a private platform where each department can work with their own knowledge area, their own documents and their own AI assistants. By default, infrastructure deploys on European servers and can evolve to local models, private cloud or hybrid architecture depending on data sensitivity.
Start with a controlled PoC. One department, real documentation, real users and usage metrics. No "total transformation" before knowing what works.
> service_signal
> private_ai > decision_layer > controlled_orbit
> problem
The problem isn't that your company lacks knowledge. It's that it can't use it well.
Most companies already have the information they need to operate better: manuals, processes, policies, reports, historical data, technical documentation, pricing, proposals and expert knowledge. The problem is that knowledge is scattered across folders, PDFs, emails, drives, spreadsheets and key people. Meanwhile, teams start using public AI tools to resolve internal questions.
AI use without control
When teams don't have a private alternative, they use public tools to resolve internal questions. Convenient, yes. Reassuring for IT, not so much.
Generic AI doesn't know your company
Public models can reason, summarise and write. But they don't know your processes, pricing, policies, products or previous decisions.
Each department needs different answers
Management, sales, HR, operations or technical support shouldn't query the same knowledge or receive the same type of response.
> Sensitive information leakage
> Responses without real company context
> Knowledge loss when someone leaves
> Duplication of questions and tasks
> Decisions made with incomplete information
> solution
We create a private AI environment organised by knowledge areas.
Private AI Knowledge Hub turns internal documentation into a private query and assistance platform for teams. The company can create knowledge areas by department, role or function. Each area can have its own documents, specialised assistants, access permissions, usage limits, controlled sources and models suited to the sensitivity level. LowOrbits doesn't just configure the platform. We design the architecture, prepare documentation, organise knowledge, configure assistants and accompany adoption.
> Reduce dependence on scattered knowledge
> Protect sensitive documentation
> Give each team access to the information they really need
> Improve internal query speed
> Avoid invented or evidence-free responses
> Prepare the company for more mature AI use
> Create a foundation for future workflows and automations
You don't need an AI that knows everything. You need an AI that knows the right thing, for the right person, under the right permission.
> architecture
From scattered documents to private corporate intelligence
01
Document preparation
We review, clean, structure and organise documentation so the system can retrieve it usefully. Uploading files isn't enough.
02
Knowledge Zones
We create knowledge areas by department, function or access level: Management, Sales, Technical Support, HR, Operations, Marketing, Product, Legal/Compliance.
03
Specialised assistants
We configure assistants with specific instructions, sources and limits. A sales assistant is not the same as one for HR or management.
04
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.
05
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.
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.
> Private environment per client
> European servers
> Knowledge areas
> Users and permissions
> Controlled cloud models
> RAG-ready documentation
> Managed support
PoCs, B2B companies, startups, management teams, internal training, commercial documentation.
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.
> Hardware or dedicated server
> Local models
> Local knowledge base
> Department areas
> Access inside the client network
> Maintenance and support
Industry, engineering, advanced manufacturing, technical documentation, intellectual property, environments with strong privacy requirements.
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.
> Private RAG
> Local models for sensitive documentation
> European models for corporate use
> Limited global models
> Internal permissions
> Differentiated query routes
Management teams, growing companies, mixed departments, companies that need to balance privacy and advanced capability.
Use cases by department
Executive / Strategy Hub
Assistant for management that summarises reports, connects previous decisions, analyses risks and helps prepare strategic meetings.
> result Summarise the main risks from the last quarter and propose three topics for the next management meeting.
Sales Knowledge Hub
Sales assistant querying pricing, arguments, technical sheets, commercial conditions, objections and previous proposals.
> result An industrial client asks for a discount and has maintenance issues. Which product should we recommend and with what argument?
Technical Support Hub
Support assistant querying manuals, error codes, procedures, compatibilities and technical documentation.
> result The machine shows error E-42 during startup. What does it mean and what steps should the technician follow?
HR & Onboarding Hub
Internal assistant for onboarding questions, internal policies, travel, expenses, procedures and corporate culture.
> result A new employee starts on Monday. What steps should they complete during their first two weeks?
Operations Knowledge Hub
Assistant for querying procedures, checklists, incidents, maintenance, quality or internal instructions.
> result What procedure should we follow if a quality control deviation is detected?
Marketing & Market Intelligence Hub
Assistant for querying campaigns, reports, positioning, competition, customer insights and brand documentation.
> result Summarise the learnings from the latest campaigns and what we should test next.
> orbit_hub_method
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.
01
Observe
We analyse the company context: departments, documentation, tools, users, data sensitivity and priority use cases.
Initial knowledge map and pilot scope.
02
Refine
We prepare documentation for AI: cleaning, classification, structure, metadata, versions and separation by areas.
Documentary base ready to become queryable knowledge.
03
Build
We configure the private environment: knowledge areas, specialised assistants, users, permissions, models and deployment architecture.
Functional Private AI Knowledge Hub.
04
Integrate
We validate the system with real users, test questions, sources, limits and use cases by department.
Operational pilot with feedback and initial metrics.
05
Tune
We adjust assistants, documents, models, prompts, permissions and flows to improve precision, utility and adoption.
Optimised system and evolution roadmap.
A useful RAG doesn't come from uploading PDFs. It comes from designing how a company turns documentation into operational knowledge.
Designed for companies that need private AI, organised knowledge and internal control
For CEOs
You want your company to use AI without losing control over documentation, decisions, processes and critical knowledge.
Faster queries, less dependence on key people and better decision-making.
For CTOs
You need a private, manageable architecture ready for permissions, models, security and technical evolution.
Corporate AI without improvisation or data leakage.
For industrial companies
You have manuals, technical sheets, processes and sensitive industrial property.
Private AI to query technical knowledge without exposing critical information.
For commercial teams
You want sales to query correct, updated and consistent information without always depending on management.
Faster responses and more consistent arguments.
For HR and training
You want to facilitate onboarding, internal policies, procedures and frequent answers without saturating the people team.
Better informed employees and fewer repetitive queries.
For innovation teams
You want to validate corporate AI with a real, controlled and measurable case before scaling to the entire company.
Serious, defensible PoC ready to grow.
What we deliver
Each implementation adapts to the technical context, privacy level and documentary maturity of the company.
✓ Private AI Environment — Private AI environment deployed on European cloud, private server, local appliance or hybrid architecture.
✓ Knowledge Zones — Knowledge areas by department, team or function, with differentiated documentation and access.
✓ Document Preparation Pipeline — Preparation, cleaning and structuring of documentation for precise system queries.
✓ Specialized AI Assistants — Assistants configured for specific roles: management, sales, support, HR, operations, marketing.
✓ Model Strategy — Model selection by sensitivity, cost, performance and task type: local, European or global with limited access.
✓ Access & Usage Rules — User configuration, permissions, usage limits and recommendations to avoid errors or data exposure.
✓ Pilot Validation — Tests with real questions, selected users and initial utility, adoption and precision metrics.
✓ Training & Onboarding — Training session for key users and basic guide on use, limits and best practices.
Private AI yes. Black box without control, no.
Private AI Knowledge Hub is designed as an assistance and query system, not as a substitute for human judgement or an automatic engine for critical decisions.
GDPR-first infrastructure
By default, infrastructure on European servers under privacy, access control and data minimisation criteria.
Role-based access
Each user accesses only the knowledge they need. Sales doesn't need to see sensitive HR information.
Local or hybrid models
Local models can be used for sensitive data. Global models can be limited to non-sensitive tasks.
Human-in-the-loop
The system helps query, summarise and recommend. Important decisions are still reviewed by people.
Evidence-first answers
The system prioritises responses based on available documentation. If there isn't sufficient evidence, it indicates this rather than inventing.
Controlled evolution
First documentary query. Then system integration. Later automations. Not everything on day one.
Privacy, permissions and evidence before spectacle.
> 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.
> privacy_compliant Form powered by Tally. GDPR compliant · Hosted in Europe · No cookie tracking
Frequently asked questions
A private corporate AI platform to query documentation and internal knowledge via specialised assistants, organised by knowledge areas and permissions.
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.
By default, on European infrastructure. It can also deploy on a private server, local appliance or hybrid architecture depending on sensitivity and requirements.
It's designed with a GDPR-first approach: European infrastructure by default, access control, exposure minimisation and separation by areas.
Yes. For sensitive information or industrial environments, local models can be used. For non-sensitive tasks, cloud models can be enabled with specific permissions.
Yes. That's one of the foundations of the product. Each department can have its own Knowledge Zone, documentation, assistant, permissions and limits.
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.
Yes, but normally not in the first step. We recommend starting with documentary query and then evolving towards connections with internal systems.
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.
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.