Legion Algo Labs

    Local AI integration

    Local AI Integration for Modern Businesses

    Deploy intelligent AI on your own infrastructure, connect it to the business systems you already use, and keep greater control over how your information is processed.

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    • On-premises or private cloud
    • Open-weight models
    • Connected to your systems
    • Implementation and support

    How it fits together

    1. Your documents and systemsPolicies, manuals, CRM, helpdesk
    2. Private knowledge baseIndexed on infrastructure you control
    3. Local language modelOpen-weight model, local inference
    4. Answers and workflowsCited answers, drafts, hand-offs

    What local AI is

    AI that runs where you decide

    Local AI means running language models, and the services around them, on infrastructure you control: a machine in your office, a dedicated server or a private cloud server. Requests are not sent to a public AI service by default.

    Open-weight models, run with tools such as Ollama, can answer questions, summarise documents, draft text and sort information. Connected to your own documents and systems, they become practical business tools rather than a separate chat window.

    Benefits

    Why businesses choose local AI

    • More control over information

      Prompts and documents are processed on infrastructure you choose. That makes it easier to meet your own policies and your clients' expectations about where information goes.

    • Costs you can plan

      You pay for hardware or hosting and support rather than per request, which suits steady internal use. Whether it costs less than a cloud service depends on your usage, and we show you the comparison.

    • Built into your workflows

      The assistant works inside the tools your team already uses, through APIs and workflows, instead of asking people to copy and paste between systems.

    • Works on restricted networks

      Once installed, a local model can run where internet access is limited or where policy keeps certain systems offline.

    On-premises deployment

    Installed, configured and documented

    We assess your workload, recommend hardware or a private server, then install and configure the model runtime, the knowledge base and the integrations. Access is limited to the people and systems you approve.

    • Model runtime set up and tuned for your hardware
    • Sign-in and access control for staff
    • Logging so you can see how the system is used
    • Documentation your team can follow

    Business system integration

    Connected to the systems you already run

    Local AI is most useful when it can read from and hand work to your existing tools. We connect it through APIs and n8n workflows to systems such as document stores, CRMs, helpdesks and email, limited to the data and actions you approve.

    Private knowledge assistants

    Answers from your own documents

    Retrieval-augmented generation (RAG) lets an assistant search your approved documents, such as policies, manuals and procedures, and answer with a reference to the source, rather than relying on what a model happened to learn in training.

    Documents are indexed in a vector database you host. When a document changes, the index is updated, so answers follow the current version.

    Local versus cloud AI

    Which fits your situation

    Neither option is right for everything. Many businesses use both.

    ConsiderationLocal AICloud AI service
    Where requests are processedLocal AI: On infrastructure you controlCloud AI service: On the provider's infrastructure, under its terms
    Model capabilityLocal AI: Strong for focused tasks; the largest models need substantial hardwareCloud AI service: Access to the most capable hosted models
    Running costLocal AI: Hardware or hosting, plus maintenanceCloud AI service: Usage-based fees
    MaintenanceLocal AI: Maintained by you, with our supportCloud AI service: Model maintained by the provider
    Internet dependencyLocal AI: Can run offline once installedCloud AI service: Needs a connection
    Often suitsLocal AI: Sensitive documents, steady internal use, restricted networksCloud AI service: Occasional use, tasks that need the most capable models

    Hybrid deployment

    Local by default, cloud where you allow it

    Hybrid model routing sends each request to the right place. Sensitive or routine requests stay with a local model; selected tasks can go to a cloud model where your policy allows it. The rules are agreed with you, and routing can be logged and reviewed.

    Hardware and infrastructure

    What you need to run it

    Requirements depend on the model, the number of users and the work it does. We size this during discovery, before you buy anything.

    • Smaller open-weight models can run on a well-specified workstation or an Apple silicon Mac
    • Larger models and several users at once generally need a server with one or more GPUs with enough memory for the model
    • Storage for the models, document indexes and backups
    • Secure network access to the systems the assistant connects to
    • Or a private cloud server, if you would rather not run hardware on site

    Security and maintenance

    Local reduces exposure. It does not replace good practice.

    Running AI locally keeps processing away from public AI services, but security still depends on configuration, access and upkeep. No system can be guaranteed secure, so we document the risks and the controls in place with you.

    • Least-privilege connections to each business system
    • Updates to models, runtimes and dependencies
    • Backups of configuration and document indexes
    • Monitoring, with alerts when something stops working

    Technologies we work with

    • Ollama
    • Open-weight language models
    • Local inference
    • Retrieval-augmented generation (RAG)
    • Vector databases
    • Private knowledge bases
    • n8n workflow automation
    • API integrations
    • Hybrid model routing

    Working with Legion

    Consultation and implementation

    Every project starts with the problem, not the technology.

    1. 01

      Discovery

      We learn what the assistant should do, which information it may use and who will use it.

    2. 02

      Assessment

      We recommend models, hardware or hosting and integrations, with the trade-offs explained.

    3. 03

      Pilot

      A working version on a sample of your documents, so you can judge the answers before going further.

    4. 04

      Implementation

      Full deployment, integrations, access control, documentation and staff handover.

    5. 05

      Ongoing support

      Optional monthly maintenance: updates, monitoring, index refreshes and help when you need it.

    Projects are priced as an initial implementation fee, with an optional monthly maintenance and support agreement. Scope and cost are confirmed after discovery.

    Questions

    Common questions

    Is local AI completely private?
    It keeps processing on infrastructure you control, which gives you more control than a public AI service. Privacy still depends on access, configuration and how the system is used, so we document these with you. No system can be guaranteed secure.
    Do we need our own servers?
    No. Options include a machine in your office, a dedicated server or a private cloud server. We recommend what fits your workload and policies.
    Can a local model match cloud AI?
    For focused tasks such as answering questions from your documents, summarising and drafting, current open-weight models can perform well. The largest hosted models are still stronger at some complex tasks, which is why a hybrid setup can make sense.
    Can it connect to our existing systems?
    Yes, through APIs and n8n workflows, with tools such as document stores, CRMs and helpdesks. It only reaches the systems and data you approve.
    How is a local AI project priced?
    An initial implementation fee covers discovery, setup and integration. An optional monthly agreement covers maintenance, updates and support. We confirm scope and cost after discovery.
    Do you only work in Christchurch?
    Legion Algo Labs is based in Canterbury, New Zealand. We work with businesses in Christchurch, across New Zealand and internationally, mostly remotely.

    Related services

    Local AI integration is an engineering service for client businesses. It is separate from Legion's own products, such as Legion AI inside Legion HQ.

    Start with the problem

    Tell us what you want to improve. We will tell you plainly whether this service is the right fit.

    Book a discovery callSend your brief