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// AI STRATEGY & DELIVERY

The right AI for your business.

We assess the possible routes against your real operations, pick the one we can prove, and only then deploy it.

Request a paid AI assessment Explore practical uses
  1. 1 Assess
  2. 2 Prove
  3. 3 Deploy

A paid independent study. You own the output and can implement it with us, internally or with another supplier.

// REPRESENTATIVE PROVIDERS

We compare capabilities, not logos.

OpenAI models/API, Anthropic Claude, Google Gemini, Mistral AI, Kimi and other suitable options can be researched and tested against your work.

  • OpenAI
  • Anthropic / Claude
  • Google Gemini
  • Mistral AI
  • Kimi / Moonshot AI

Examples we can evaluate and integrate – not a fixed catalogue, endorsement or partnership claim.

// PRACTICAL USES

Start with work worth improving.

The useful question is not where to add AI. It is where a measured capability can remove friction, reveal information or support a better decision.

01 / EVERYDAY OPERATIONS

Turn repetitive information work into a controlled flow.

AI can help transform unstructured material into reviewed, structured outputs connected to the systems your team already uses.

  • Invoice and receipt parsing
  • Document extraction and classification
  • E-mail triage
  • Structured data entry
  • Internal knowledge assistants
  • Summaries
  • Workflow automation
Incoming invoice / PDF
Verified fields / ERP queue Human review required Source page 2 / visible
02 / CUSTOMER-FACING SERVICES

Help customers while keeping escalation visible.

A service can answer, advise or draft in several languages while a person remains available for ambiguity and high-impact decisions.

  • Support assistants
  • Product advisers
  • Multilingual self-service
  • Draft responses
  • Escalation to a person
ESCALATION TRACE ASSISTANT → PERSON
  1. AI ASSISTANT

    Draft answer assembled from approved help content.

  2. ESCALATION TRIGGER

    Account change requires identity verification.

  3. HUMAN SUPPORT

    Context and source links transferred for review.

03 / SPECIALIST & CREATIVE WORK

Give specialists better tools for complex material.

Research and creative systems can organise evidence, work across large collections and generate controlled starting points without hiding uncertainty.

  • Research
  • Investigations
  • Large document sets
  • Domain-specific assistants
  • Image generation
CONTROLLED IMAGE WORKFLOW BRIEF → GENERATE → REVIEW
  1. APPROVED BRIEF & SOURCES

    Brand guide / product reference

  2. GENERATED IMAGE

    Version 03 / inside the agreed workflow

  3. SPECIALIST REVIEW

    Composition, rights and output use checked

These are possible applications, not guaranteed outcomes. High-impact workflows include evaluation, source visibility and human review where risk requires them.

// PAID AI OPPORTUNITY STUDY

A decision-ready runbook – not a sales teaser.

We research the opportunity, test assumptions and turn the result into a standalone implementation plan before a larger investment is made.

The completed study belongs to you. Use it with ARODAX, your internal team or another supplier.

CLIENT-OWNED RUNBOOK Research / compare / recommend
  1. Opportunity map and prioritised use cases
  2. Data, privacy and operational risk assessment
  3. Researched and compared solution options
  4. Recommended architecture and integration map
  5. Implementation and operating cost model
  6. Phased runbook with responsibilities and next steps
COST & EFFORT DRIVERS
  • Workflow scope
  • Data readiness
  • Quality and risk threshold
  • Usage and infrastructure
// TWO DELIVERY PATHS

Choose the architecture after discovery and testing.

API Integration and Private AI solve different constraints. A hybrid system is also possible; the right boundary follows evidence rather than fashion.

FAST PATH

API Integration

Connect selected commercial capabilities to your workflow with low infrastructure burden and usage-based cost.

  • Fast onboarding
  • Access to leading capabilities
  • Provider evaluation and fallback planning
  • Usage and budget controls
Time to value
Usually faster to pilot
Infrastructure and model validation first
Data location
Depends on service, contract, region and configuration
Inside the agreed private boundary
Control
Provider service boundary
Client-defined model and infrastructure boundary
Customisation
Prompting, tools, retrieval and provider features
Open model, retrieval, tools and selected fine-tuning
Operating responsibility
Provider platform plus integration operations
Dedicated infrastructure and model operations
Cost shape
Usage-based
Capacity and operations; predictable at suitable volume

The correct choice depends on representative data, security requirements, quality testing and realistic operating economics.

// API INTEGRATION

A provider API becomes useful through a controlled pipeline.

We evaluate a suitable model and connect it to the business workflow with explicit permissions, validation, observability and a provider-change plan.

  1. Client system
  2. Data and permission boundary
  3. Selected provider API
  4. Validation / human review
  5. Business workflow
  • Model and provider evaluation
  • API implementation
  • Structured outputs
  • Permissions and privacy controls
  • Observability
  • Evaluation sets
  • Usage and budget controls
  • Fallbacks
  • Provider-change planning

Data handling depends on the selected service, contract, region and configuration. We define and verify that boundary for the chosen use case.

// PRIVATE AI INFRASTRUCTURE

Privacy and control, from a compact lab to enterprise infrastructure.

Hardware ownership, location, hosting and ongoing management are agreed per project. We can operate the system continuously or document and hand it over.

PRIVATE AI LAB

Compact systems for controlled work.

Suitable for offices, labs or data centres where a focused team needs private inference, prototyping or specialist generation.

  • High-end desktop NVIDIA GPUs suited to professional use
  • Specialised compact devices such as NVIDIA DGX Spark
  • Controlled inference and prototyping
  • Specialist assistants, image generation and selected fine-tuning workloads
ENTERPRISE AI INFRASTRUCTURE

Dedicated platforms for sustained critical use.

Professional data centre systems selected according to workload, availability, budget and lifecycle – never a fixed hardware package.

  • Suitable NVIDIA Blackwell, Hopper or Ampere architecture
  • Multi-GPU and redundant designs where justified
  • Monitoring, security and incident handling
  • Configuration and applicable business-data backups, upgrades and lifecycle management
CONTROLLED AI TOPOLOGY
  1. Approved data
  2. Retrieval / knowledge
  3. Model serving
  4. Business applications
  5. Observability
  6. Human oversight
// OPEN MODELS & CUSTOMISATION

Swap the model without surrendering the system.

The surrounding application, permissions, knowledge connections, evaluation and ownership boundary stay explicit while the model layer can evolve.

Client application
Permissions & tools

Open-weight text model

Retrieval connects approved company knowledge with source visibility and access rules.

Evaluation & observability
Documented ownership boundary
  • Select suitable open-source or open-weight text, vision and image models
  • Evaluate quality with client-relevant examples
  • Connect only approved company knowledge
  • Implement retrieval, tools, permissions and workflows
  • Fine-tune only when measured benefit justifies cost and maintenance
  • Keep a documented replacement and upgrade path
// DELIVERY & OWNERSHIP

Evidence at every gate. A clear owner at every stage.

We do not promise production deployment before data, security and quality have been evaluated.

  1. Consult and research

    Define the work, constraints, data boundary and commercial question.

    GATE / PRIORITISED OPPORTUNITY

  2. Prove with representative data

    Compare options and measure quality, risk and operating economics.

    GATE / VERIFIED EVIDENCE

  3. Integrate or deploy

    Build the approved API pipeline or private infrastructure with documentation.

    GATE / PRODUCTION READINESS

  4. Manage or hand over

    ARODAX can operate the result continuously or transfer it to an agreed owner.

    GATE / ACCEPTED OWNERSHIP

You receive documented architecture, operating responsibilities and a replacement or handover path.

// PRACTICAL ANSWERS

What decision-makers usually ask.

Will our company data be used to train third-party models?
That depends on the selected service, contract, region and configuration. We define the data boundary, verify the relevant provider terms and configure the integration for the agreed use. Where this boundary is unacceptable, Private AI may be the better route.
When is an API better than Private AI?
An API often wins when fast onboarding, access to leading capabilities and low infrastructure burden matter most. The decision still depends on data handling, measured quality, volume and the provider boundary.
Where can private infrastructure be located?
It may run in an office, lab or professional data centre according to security, power, connectivity, availability and operating requirements. Hardware ownership, hosting and management are agreed per project.
Can you work with our current systems and data?
Yes. The study maps existing applications, interfaces, permissions and data quality before recommending an integration. We do not require a complete platform replacement.
What is the difference between retrieval and fine-tuning?
Retrieval supplies approved current knowledge at request time and can preserve source visibility. Fine-tuning changes model behaviour through additional training. We fine-tune only when evaluation shows a benefit that justifies its cost and maintenance.
How do you handle quality, hallucinations and human oversight?
AI output can be wrong. We use representative evaluation sets, structured outputs, source visibility, guardrails, monitoring and human review where the consequence of an error requires it.
What determines implementation and operating cost?
Cost follows workflow scope, integration complexity, data preparation, quality and security requirements, usage volume, infrastructure, licences and the selected operating model. We confirm scope and price before work begins.
What exactly does the paid opportunity study deliver?
You receive a prioritised opportunity map, risk assessment, researched options, recommended architecture, cost model and a phased runbook with responsibilities and next steps.
Can we implement the runbook elsewhere?
Yes. The study is a standalone client-owned deliverable. Your internal team, another supplier or ARODAX can implement it.
Can ARODAX manage or hand over the finished system?
Yes. We can provide ongoing infrastructure and model operations, monitoring, security, upgrades and incident handling, or prepare documented handover to the agreed owner.
// PAID AI ASSESSMENT

Start with an independent AI assessment.

Tell us what you want to improve, which systems and data are involved, and where risk matters most.

We confirm the scope and price before work begins.

ARODAX SERVIS s.r.o. is the data controller. We process the submitted details to answer your inquiry or take steps at your request before entering into a contract. Read our privacy policy for more information.

You will hear from a specific person who can scope the study – not an automated sales sequence.