About
Applied AI that integrates into real systems.
G|AI Works is an owner-operated applied AI studio, led by Oliver Gruenig. Strategy, engineering, integration, and operations delivered end-to-end — production-grade from the first sprint, with audit trails, measurable outcomes, and no proprietary platform or runtime dependency.
- ✓ 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.
Owner-operated by
Oliver Gruenig
Strategy · Engineering · Integration · Operations — end-to-end, under one accountable owner.
Why G|AI Works exists
Most AI projects reach the same dead end: a demo that works, and a production path that doesn't exist. The gap is not the model — it's the absence of feasibility thinking before the build begins.
What data is actually available? What governance will hold under real operational load? What does "done" mean against your specific stack? Without clear answers, even technically solid systems fail at the integration layer.
G|AI Works starts there. One accountable owner, fixed scope, production-grade from sprint one — with a structured feasibility assessment before any architecture decisions are made.
Working beliefs
Where I come from on this
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Feasibility before architecture.
Before any model work begins, available data, system boundaries, and governance constraints are mapped. What your stack can actually support determines what we build — not the model's theoretical ceiling.
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Most AI projects stall on integration, not on models.
The interesting work lives where an LLM meets an ERP, a data warehouse, an internal tool, or a content system. That interface is where my attention sits.
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Evaluation before design decisions.
An eval harness with a defined baseline runs from sprint one. Prompt, model, and structure choices are made against that baseline — not by taste.
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A deliverable you cannot run without me is not a deliverable.
Every engagement ends with a handover package — runbook, prompt registry, eval suite, documentation — so the system is operable on day 181 without me in the room.
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Production-grade is a default, not an upgrade.
Versioned prompts, structured output validation, rollback paths, and cost instrumentation are built in from the first commit — not bolted on before go-live.
Technical profile
Stack and shapes of work
A working profile, not a shopping list. These are the tools and project shapes used repeatedly in real engagements — chosen because they hold up under production load, not because they are trending.
// Languages
- · TypeScript
- · Python
- · SQL
- · Shell
// AI & LLM
- · Claude (Sonnet / Opus)
- · OpenAI models
- · Multi-agent orchestration
- · Structured output + schema validation
- · Retrieval over owned data
- · Eval harnesses
// Platforms & data
- · Astro · Node · Tailwind
- · Postgres · SQLite
- · Data warehouses
- · ERP / CRM connectors
- · REST & RPC APIs
// Operational
- · Prompt registries
- · Versioned output schemas (Pydantic / Zod)
- · Observability & cost instrumentation
- · CI eval gates
- · Rollback tooling
// Typical project shapes
- — AI Readiness Audit before moving a system into production
- — Prototype-to-production sprint for a well-scoped workflow
- — Hardening an existing AI system — eval gates, observability, cost control, security
- — Internal copilots and multi-agent workflows for real business jobs
- — Integrating LLMs into ERPs, CMSs, and data warehouses
- — Designing company memory and knowledge retrieval systems
Meta-proof
How this site is actually produced
This website is content-managed by the same class of system I build for clients: a multi-agent editorial pipeline where each role is a separate agent with a versioned prompt, explicit inputs and outputs, and a quality gate. EN is authored first, DE is produced in aligned variants, and every draft passes four gates before it ships — SEO, Security, Sources, Clarity.
// Pipeline
- 01 Trend Scout sonnet-4-6 surfaces topics, validates sources
- 02 Pillar Writers sonnet-4-6 drafts per domain — tech, marketing, finance
- 03 Fact & Source Checker sonnet-4-6 verifies claims, flags weak sourcing
- 04 SEO Architect sonnet-4-6 structures headings, metadata, internal links
- 05 Security Reviewer sonnet-4-6 removes exploit paths, enforces safe-defaults
- 06 Lead Editor opus-4-6 final gate — routes approved drafts to collection
The same architectural pattern is applied to client engagements — different subject, same operational discipline: versioned prompts, explicit contracts between roles, and an audit trail for every decision.
Two real cases are documented at different disclosure levels: the gruenig.com content pipeline (internal, fully open) and a vertical B2B comparison portal (client engagement, redacted).
Capabilities
Types of systems I build
- — LLM pipeline design and evaluation
- — Custom AI applications — web, internal tooling, CLI
- — Orchestration APIs with structured error handling
- — Human-in-the-loop review interfaces
- — Workflow automation with explicit state management
- — Financial reporting and number-grounding systems
- — Marketing intelligence and segmentation pipelines
Process
Fixed scope. Defined outcomes.
Every engagement starts with a data audit and ends with a handover package: runbook, prompt registry, evaluation suite, and full documentation. Deliverables, timeline, and the success metric are agreed in writing before any code is written. No open retainers. No scope creep. You own the entire system at handover.
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