What Azure OpenAI enables for your business.
GPT-4o is Gilligan Tech's primary model for tasks that span multiple input types — reading scanned invoices, analysing product images, transcribing meeting recordings, and processing mixed PDF+image documents in a single pass.
- GPT-4o — Multimodal reasoning over text, images, and documents
- GPT-4o with Vision — Direct image understanding for invoice and form extraction
- Whisper — Speech-to-text for meeting transcription and call analysis
GPT-4o's vision capability reads any document format — scanned PDFs, photos, structured forms — and extracts structured data with high accuracy. No templates, no training, no configuration. Works out of the box on new document types.
- Invoice extraction — Vendor, amount, line items, dates — from any PDF format
- Contract analysis — Key clause extraction, date identification, party names
- Form OCR — Handwritten and typed form digitisation with structured output
GPT-4o mini is our workhorse for high-volume, lower-complexity tasks — ticket triage, content categorisation, intent classification, sentiment analysis. At a fraction of the cost of GPT-4o, it handles thousands of calls per hour without budget impact.
- GPT-4o mini — High-throughput classification at low cost-per-call
- Batch API — Async processing at 50% cost reduction for bulk workloads
- Structured outputs — JSON-schema-enforced responses for reliable downstream parsing
Azure OpenAI is the only path to GPT-4o inside Microsoft's enterprise compliance boundary. For clients in healthcare, legal, finance, or government, Azure OpenAI satisfies data governance requirements that the consumer OpenAI API cannot meet.
- Regional data residency — US or EU regions; data stays in-region
- Microsoft BAA — Business Associate Agreement available for HIPAA workloads
- Private endpoints — VNet integration keeps inference traffic off public internet
How Gilligan Tech deploys Azure OpenAI.
- Task routing: Tasks are classified by modality and complexity. Multimodal inputs (PDFs with images, audio) route to GPT-4o; text-only classification tasks route to GPT-4o mini to optimise cost.
- Prompt construction: Documents are pre-processed — PDFs converted to images or text, audio transcribed via Whisper — and assembled into a structured prompt with explicit output format requirements.
- Structured inference: GPT-4o is called with structured output mode (JSON schema) to ensure the response is machine-parseable. This eliminates brittle regex parsing of free-text responses.
- Validation layer: Extracted data is validated against business rules — required fields, numeric ranges, date formats. Anomalies are flagged for human review rather than silently passed downstream.
- Compliance logging: Every call is logged with Azure Monitor. Application-level logs add business context — document type, extracted fields, validation outcome — for full auditability.
Azure OpenAI models we deploy.
| Model | Context | Best for |
|---|---|---|
| GPT-4o | 128K tokens | Multimodal reasoning, document extraction, complex analysis |
| GPT-4o mini | 128K tokens | High-volume classification, triage, intent detection |
| GPT-4o with Vision | 128K tokens | Invoice/form OCR, image analysis, mixed-media documents |
| Whisper (Azure) | Audio | Meeting transcription, call-centre audio, voice notes |
| text-embedding-3-large | 8K tokens | Semantic embeddings for RAG when GPT-4o is the primary model |
| Azure AI Foundry | Managed | Custom fine-tuning, evaluation pipelines, prompt management |