Applied AI Studio
AI that ships.
Not just demos.
G|AI Works designs and deploys AI systems for finance, marketing, and engineering teams — built to production standards, with audit trails and measurable outcomes.
- ✓ Production-grade from sprint one — versioned prompts, validated outputs, rollback paths.
- ✓ Security-first — no third-party tracking by default, audit-ready outputs, safe defaults throughout.
- ✓ Measurable outcomes — every engagement defines a success metric before work starts.
Production standards
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Audit trails
Every output logged with input payload hash, prompt version, and model version.
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Token cost control
Per-request cost instrumentation surfaced directly in your operational dashboards.
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Eval & regression gates
Every prompt and model change is tested against a golden set before reaching production. Regressions are caught in CI, not by users.
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Monitoring
Latency distributions, error rates, and schema validation pass rates tracked live in production.
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Security baseline
No third-party telemetry by default. Pinned model versions. Credential hygiene and least-privilege access enforced by default.
Services
Applied AI, by discipline
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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.
Use Cases
Outcomes, not assumptions
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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
Process
How we deliver
- 01
Data audit
We map your available signals, validate data quality, and establish a measurable baseline before any model work begins.
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Scope & contract
Fixed deliverables, timeline, and success metric agreed in writing before we start. No scope creep, no open-ended retainers.
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Build & validate
Iterative implementation with an eval harness running from sprint one. Every prompt or model change is measured against the baseline.
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Deploy & instrument
Production deployment with observability, alerting, output schema validation, and a documented rollback path — operational from day one.
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Hand-off
Full documentation, prompt registry, runbook, and eval suite delivered. You own the system entirely. No lock-in.
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 us what you're building. We'll scope a focused engagement and give you a clear first step — no slide decks, no vague roadmaps.