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.
- ✓ 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.
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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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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finance · 14 Mar 2026
How to Build LLM Audit Trails for Regulated Workflows
In regulated environments, it is not enough that a model produces a plausible answer. This guide covers the architecture, design principles, and practical patterns for building LLM audit trails that can be reconstructed, reviewed, and defended.
Read → -
security · 14 Mar 2026
Prompt Injection Defense Beyond Basic Guardrails
Basic guardrails are not security architecture. This guide covers the structural reasons prompt injection persists, what effective defense actually requires, and how to build LLM systems where trust boundaries are enforced at the system level.
Read → -
security · 14 Mar 2026
RAG Access Control: Building Permission-Aware Retrieval
Retrieval quality alone is not enough in enterprise RAG systems. This guide covers why permissions must be enforced before generation, what permission-aware retrieval actually requires, and how to build a defensible retrieval boundary.
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.