How We Work— From Friction to Automation


We deploy on your infrastructure, extract knowledge from your systems, and deliver measurable ROI — in weeks, not years.

AI Workforce Augmentation – Moving Beyond AI Assistants to Intelligent Execution

Clarity First. Automation Next.

Your teams don’t need another tool—they need results. That’s why every engagement with Arti follows the same proven model: start small, deliver measurable impact fast, then scale.

Friction Audit

Diagnostic Pilot

Scale-Up

Step 1 – Friction Audit (2 Weeks)

We identify the hidden bottlenecks in your Jira, Confluence, and service data.

  • Pinpoint wasted effort and rework.
  • Quantify the cost of friction.
  • Map high-ROI automation opportunities.

Outcome: Clear baseline of where your expertise is trapped and what it costs.

Monitoring API for Application adaptation
tailored ai solutions

Step 2 – Diagnostic Pilot (8 Weeks)

A structured pilot focused on one critical process (e.g. routing, onboarding, service).

  • Refine chaotic data into a structured knowledge asset.
  • Deliver working AI models with human-in-loop pipelines.

  • Deployed inside your existing tools — Teams, Slack, Jira, Confluence. No new interface for your team.

Outcome: Prove automation readiness with measurable outcomes, e.g. 85%+ routing accuracy, 4× faster onboarding, 65% faster resolutions.

Step 3 – Scale-Up

Expand automation across teams and processes.

  • Extend the refined knowledge base.
  • Deploy AI agents for triage, self-service, and documentation.
  • Integrate with existing IT systems.

Outcome: Scalable automation, reduced expert overload, faster growth without linear headcount.

ai powered ticketing systems
chat-based knowledge retrival

Continuous Refinement (Ongoing)

Ongoing improvement and KPI driven expansion: train models on refined knowledge base, expand integrations, onboard additional teams.

  • Arti learns continuously from the decisions your team makes daily.

  • Documentation gaps are detected automatically and surfaced for closure.

  • The intelligence layer improves from operational reality.

Outcome: Continuous retraining, compliance assurance, and scaling advisory.

Built on the IntakeOps Architecture

• Intake Layer — captures and normalizes human requests
• Operational Intelligence Layer — structures historical and live context
• Orchestration Layer — routes, executes, and governs agents and workflows

Every engagement contributes to a living, self-updating intelligence layer. Arti connects Jira, Confluence, and communication tools to systematically ingest, link, and refine operational intelligence.
→ Explore the IntakeOps Architecture

We went from 85% dark data to 72% structured coverage in 2 weeks. It’s now powering automated routing, smarter onboarding, and freeing up experts to focus on solving — not sorting — problems.

VP Operations, Automation Technology Provider

Arti surfaced recurring SAP export errors tied to a misconfigured integration. Fixing that single root cause eliminated hundreds of monthly incidents.

IT Service Manager, Machinery Manufacturer

We thought our bottleneck was process complexity — turns out one engineer was resolving 88% of all module issues. The audit exposed a single-point-of-failure risk we didn’t see.

Head of Engineering, Industrial Software Company

Our request labels were chaos — dozens of inconsistent components and tags. Artiquare helped us rebuild a clean taxonomy, which now drives smarter routing, clearer ownership, and faster onboarding.

Service Operations Lead, Automation Technology Company

🇪🇺 EU Hosted | 🔒 GDPR Ready | 🧩 On-Premise Available | 🧠 Open Source Models | ⚖️ AI Act Ready

Licensing Model

  • Pilot License: Fixed 8 week engagement, capped users/scope.

  • Production License: Outcome-based (volume of tickets/knowledge nodes automated).

  • Enterprise Support: Continuous retraining, compliance assurance, and scaling advisory.

sigmund AQTA5E6mCNU unsplash ai-powered documentation,ai-driven documentation,AI-Enhanced Documentation,Document Retrieval with AI How We Work— From Friction to Automation

Deployment & Architecture Overview

Deployment Options

  • On-Premise: Full deployment in your infrastructure or private cloud (air-gapped possible).

  • Private Cloud (EU): Managed instance hosted under GDPR and AI Act compliance.

  • Hybrid: Cloud-based orchestration, local data connectors.

Core Components

  • Data Adapters – Jira, Confluence, Slack, Teams, GitHub, custom APIs.

  • Context Layer – Transforms raw data into structured, searchable context.

  • Knowledge Graph Engine – Links entities, patterns, and relationships dynamically.

  • Automation Agents – Power routing, triage, and knowledge reuse.

  • Human-in-Loop Oversight – Ensures quality, traceability, and feedback-driven improvement.

Supported Environments

  • Docker | On-Prem
  • Azure | AWS | GCP | Private Cloud
  • GDPR Ready
  • ISO/IEC 27001-aligned
  • AI Act Compliant

For details on our privacy, security, and compliance practices, see our Trust & Compliance page.

FAQs

The Friction Audit is a low-risk entry point designed to show ROI quickly. Pricing depends on the size and complexity of your data environment—but it’s less than the cost of a single FTE per month.

The Friction Audit delivers insights within 2 weeks. Our 8-week Diagnostic Pilot produces measurable automation outcomes—typically 70–75% routing accuracy, 4× faster onboarding, or 50% inquiry deflection.

You decide. Some customers stop after the pilot with a roadmap in hand. Others scale the solution across multiple processes with our support.

No. Our approach works with your existing Jira, Confluence, and IT systems. We refine your knowledge first, then layer automation on top.

Yes. All processing is done within environments that meet enterprise-grade security and compliance standards. Data never leaves your control.

Multiple layers: PII detection and masking, anonymization, and EU-private cloud or on-prem deployment. Your data never leaves your control boundary—the AI comes to your data.

We don’t rely on prompt engineering tricks. Our architecture uses deterministic controllers with smaller, specialized models for specific tasks. The intelligence is in orchestration, not brittle prompts.

We use a sovereign stack of open-source models, tailored to your deployment and data sensitivity. No dependency on a single large vendor’s API.

We combine automated pattern discovery with expert input to identify hidden inefficiencies and business-critical pain points. Prioritization starts with high-frequency, low-complexity workflows for fastest ROI, then expands systematically with clear KPIs.

Yes, but we follow a “Crawl, Walk, Run” approach. We start with structured data (like Jira) to prove ROI and trust, then expand to unstructured sources.

Ready to see where your friction is costing you most?