Solutions

From the first analysis to AI running in production.

Every engagement starts with the Ruolus analysis. Its findings decide which of the solutions below you actually need, so you only pay for what moves your business forward.

01 · Where every project starts

AI Readiness Analysis

The Ruolus engine studies your company from the inside: the processes your teams run, the data you already collect, the systems you use and the goals you're measured on. It then scores every possible AI use case on business impact and feasibility, and tells you which ones are worth doing first and which ones to skip.

Best for: companies that know they need AI but aren't sure where to start, or that want a second opinion before a large investment.

  • Guided intake with your stakeholders and team leads
  • Secure scan of data sources, documents and existing tools
  • Process mapping and bottleneck detection
  • Use-case scoring by impact, feasibility and risk
  • Compliance check for KVKK, GDPR and industry rules

Deliverables

AI Readiness ScoreReport
Use-case priority mapReport
System architecture blueprintBlueprint
Cost & ROI forecastModel
90-day implementation roadmapPlan
Typical timeline2–3 weeks
02 · Models that speak your language

Custom Model Fine-Tuning

Off-the-shelf models don't know your products, your terminology or your internal rules. We adapt language and vision models to your domain using your own data, so they give answers your team can trust and run at a fraction of the cost of the largest general-purpose models.

Best for: companies with domain-specific language, documents or images, and those that need models running in their own environment.

  • Dataset curation, cleaning and labeling
  • Base model selection (open-source or commercial)
  • Supervised fine-tuning and parameter-efficient methods (LoRA)
  • Custom evaluation suite built around your real tasks
  • Safety testing and guardrails before go-live

Deliverables

Fine-tuned model or API endpointModel
Evaluation report vs. baselineReport
Model card & documentationDocs
Deployment packageCode
Typical timeline4–8 weeks
03 · From notebook to production

ML Pipelines & MLOps

A model that works in a notebook is only the beginning. We build the pipelines that train, test, deploy and monitor your models automatically, so updates are safe, repeatable and fast, and problems are caught before your customers notice them.

Best for: teams that already have models or prototypes and need them to run reliably at scale.

  • Automated training and evaluation pipelines
  • CI/CD for models with staged rollouts
  • Model registry, versioning and experiment tracking
  • Monitoring for accuracy, drift, latency and cost
  • Infrastructure on your cloud or on-premise

Deliverables

Production pipeline (infrastructure as code)Code
Model registry & versioningPlatform
Monitoring dashboards & alertsPlatform
Runbook & team handoverDocs
Typical timeline4–10 weeks
04 · Give your team their time back

Intelligent Automation

We build AI assistants and agents that take over repetitive, rule-heavy work: answering customer questions, reading and routing documents, preparing reports and searching internal knowledge. People stay in control of every decision that matters.

Best for: support, operations, finance and HR teams handling high volumes of repetitive requests or documents.

  • Customer support assistants connected to your knowledge base
  • Document processing: invoices, contracts, forms and emails
  • Internal knowledge assistants with retrieval (RAG)
  • Multi-step agents integrated with your CRM, ERP and ticketing tools
  • Human-in-the-loop approval for sensitive actions

Deliverables

Production assistant or agentProduct
Integrations with your systemsCode
Human review workflowsProcess
Usage & quality analyticsDashboard
Typical timeline3–8 weeks
05 · The foundation of every AI system

Data Strategy

AI is only as good as the data behind it. We assess how your data is collected, stored, labeled and governed, then design a practical plan to make it AI-ready without stopping your business to rebuild everything.

Best for: companies with scattered data across many systems, or with data quality that blocks AI projects.

  • Data maturity and quality audit
  • Target architecture: warehouse, lakehouse and vector stores
  • Labeling and annotation pipelines
  • Governance, access control and data retention policies
  • KVKK and GDPR alignment

Deliverables

Data maturity reportReport
Target data architectureBlueprint
Governance policyDocs
Labeling & collection planPlan
Typical timeline2–4 weeks
06 · Same results, smaller bill

AI Cost Optimization

Many companies run their AI on the biggest, most expensive models for every request. Ruolus analyzes how your system is really used and finds where a smaller model, a cache or a smarter architecture delivers the same quality for less.

Best for: companies with AI already in production and growing inference or GPU bills.

  • Full audit of model, API and infrastructure spend
  • Model routing: the right model size for each request
  • Distillation, quantization and caching strategies
  • GPU and infrastructure right-sizing
  • Ongoing cost monitoring with alerts

Deliverables

AI spend auditReport
Prioritized savings planPlan
Implemented optimizationsCode
Cost monitoring dashboardDashboard
Typical timeline2–6 weeks

Not sure which solution you need?

That's exactly what the Ruolus analysis is for. Start there, and we'll tell you what to build and what to skip.