Applied AI · Owner-operated
AI, integrated where your work already happens.
G|AI Works builds AI into the systems you already run — ERPs, data warehouses, content stacks, internal tools. Strategy, engineering, integration, and operations from one owner-operated studio.
G|AI Works is led by Oliver Gruenig from Karlsruhe, working with teams across DACH and Europe.
- ✓ Integrates into your stack — ERP, data warehouse, CMS, internal APIs, existing data stores.
- ✓ Production-grade from sprint one — versioned prompts, validated outputs, rollback paths.
- ✓ Measurable outcomes — every engagement defines a success metric before work starts.
OpenAI and Anthropic certifications complement production experience.
Approach
From guessing to governed execution.
Most AI projects stall not because the model is wrong — but because no one assessed what was actually buildable in the available stack before work began.
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Assess before you build
Data quality, system boundaries, and governance constraints mapped before any architecture decisions are made.
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Govern by design
Controls, audit trails, eval gates, and cost policies built from sprint one — not retrofitted before go-live.
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Transfer complete ownership
Every engagement ends with a system your team can run, audit, and extend independently.
Integration focus
Where AI actually lands
Building AI into the systems, workflows, and knowledge your teams already depend on.
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System integration
AI embedded into ERP, CRM, data warehouses, and internal APIs. Clean contracts, no brittle glue.
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Knowledge systems
Company memory, retrieval over owned data, versioned knowledge bases — answers with sources, not guesses.
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Agents & internal tools
Multi-agent workflows and internal copilots that do specific jobs inside real processes — not chat demos.
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Content & editorial pipelines
AI-assisted research, writing, and review with clear gates, multilingual output, and a human-in-the-loop.
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Workflow automation
End-to-end automation with explicit state, structured outputs, audit trails, and safe failure modes.
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LLMOps & observability
Eval harnesses, cost instrumentation, prompt registries — so systems stay stable and owned after go-live.
Services
Six delivery tracks
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Engineering
→From prototype to production pipeline
Production-ready AI systems — designed for reliability, observability, and long-term maintainability.
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RAG & Company Memory for Enterprise Teams
→Retrieval-Augmented Generation & knowledge systems
RAG, company memory and governed AI knowledge systems for enterprise teams: make documents, workflows and internal knowledge usable with access control, audit trails and LLMOps.
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Marketing
→Intelligent systems for pipeline and content
AI-augmented marketing systems that increase pipeline quality and reduce manual work — with measurable outcomes at each stage.
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Finance
→Audit-ready AI for financial operations
LLM pipelines for financial reporting, variance analysis, and audit-ready narratives — with number-grounding validation and regulatory guardrails built in.
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Programming
→Bespoke software around your AI systems
Custom AI-powered applications, internal tooling, and APIs — built to production standards with documented interfaces, test coverage, and no vendor lock-in.
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Security
→Security-first AI systems: threat modeling, guardrails, and hardening for real-world inputs.
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LLMOps & Observability
→From metrics to maintainability
Monitoring, evals, cost control, and reliability tooling for AI systems in production.
Reference engagements
What these engagements delivered
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Cross-industry
AI Attack Surface & Threat Modeling
- — Attack surface mapped with prioritised controls — designed for rapid remediation
- — Audit-ready threat model documentation delivered at engagement close
- — Typically clears an internal security review in one cycle
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Cross-industry
Evaluation Harness & Regression Gates
- — No regressions shipped to production after eval gates were introduced
- — Golden test suite covers all critical workflows with automated scoring
- — Prompt and model changes typically deployable safely in under 30 minutes
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Cross-industry
LLM Cost Tracking & Budget Policies
- — Full per-request cost visibility surfaced in operational dashboards from day one
- — Budget gates and routing rules designed to eliminate unplanned spend spikes
- — Predictable cost-quality tradeoffs with documented fallback behaviour
Selected work
Two real cases, two disclosure levels
The reference engagements above describe representative patterns. The two cases below are different: specific engagements, disclosed at the level the context allows — one internal case with architecture open, one client case with names redacted.
// Internal · openly documented
An in-house wiki for production AI systems
A human-governed knowledge system built on Claude Code. Decisions, patterns, and operational knowledge are documented, reviewed, and structured so that AI systems in production can reliably build on them.
// Client · redacted
Vertical B2B comparison portal
A real client engagement where the operator of a comparison portal is also one of its listed providers. Scoring architecture, confidence taxonomy, and validation harness disclosed — names and numbers redacted.
Engagement formats
Clear ways to start
Fixed scope, fixed duration, one success metric agreed in writing. Pick the shape that fits your moment.
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Readiness Audit
2–10 daysStructured review of your AI systems, data boundaries, and controls — with a prioritised action plan.
Scope this format → -
Prototype Sprint
2–4 weeksFrom idea to working prototype with an eval harness and evidence it beats a defined baseline.
Scope this format → -
Production Hardening
2–6 weeksObservability, security controls, eval gates, and cost instrumentation added to an existing AI system.
Scope this format → -
Enablement & Ops
OngoingQuality reviews, monitoring, and operational continuity for teams running AI in production.
Scope this format →
Signature deliverables
What ships with every engagement
Six concrete artefacts land in your repo by go-live — working infrastructure you own, operate, and extend.
// Handover package
- 01
Prompt registry
versioned · diffable · auditable
Every prompt committed, diffable, rollback-ready. No silent edits in a console.
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Eval suite
golden set · CI gates · regressions caught
A golden test set gates every prompt and model change before it reaches production.
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Runbook
incident · rollback · on-call
Operational documentation so the next engineer can run the system without me in the room.
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Audit log
input hash · prompt version · model version
Every output reconstructable from logs — compliance-ready, reviewer-ready.
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Observability dashboard
latency · errors · cost per request
Live dashboards for latency distributions, schema pass rates, and cost curves.
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Security baseline
least privilege · pinned versions · no default telemetry
Credentials, tool access, and third-party egress scoped from the first commit.
Insights
From the studio
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agentic systems · 18 Jun 2026
Multi-Agent Systems in Production: Contracts, Gates, and Human Escalation
A bounded production pattern for multi-agent workflows, covering orchestration, context, tools, evaluation, cost, and human escalation.
Read → -
governance · 12 May 2026
AI Governance That Survives an Audit: Controls, Trails, Ownership
How to turn AI governance into an operating model with explicit roles, controls, evaluation, logging, and incident handling.
Read → -
readiness · 8 Apr 2026
AI Readiness Audit: What to Check Before Production
A practical checklist for assessing data, identity, security, evaluation, operations, and ownership before an AI workflow moves beyond a prototype.
Read →
Get started
Ready to deploy?
Tell me what you're building. You'll get a clear first step — an audit, a prototype plan, or a delivery proposal. No slide decks, no vague roadmaps.