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
Prompt Injection Defense & Tool Authorization
- — Tool boundaries tightened under a least-privilege model
- — Reduced unauthorized action paths via strict allowlisting and input validation
- — Governance controls documented and reproducible for future agents
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Cross-industry
Secrets & PII Leakage Prevention
- — Data boundaries defined and enforced across logs, prompts, and retrieval
- — Sensitive fields redacted at source — not suppressed at display layer
- — Designed to pass a data-boundary review on first submission
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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engineering · 20 Feb 2026
Structuring LLM Pipelines for Production: A Practical Engineering Framework
A step-by-step breakdown of how to move an LLM prototype into a reliable, observable production pipeline — covering prompt versioning, evaluation harnesses, and latency budgets.
Read → -
finance · 15 Feb 2026
LLM-Driven Financial Reporting: From Raw Data to Auditable Summaries
How large language models can automate the generation of structured financial narratives while maintaining audit trails and data integrity.
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.