AI & ML

OpenAI

The most widely adopted large language model API. GPT-4o, o1 reasoning models, function calling, and the Assistants API form a broad AI development platform.

Trusted by leading organisations

United NationsSwiss GovernmentProspaIAGQantasEYANZ
The landscape

The most adopted AI platform

OpenAI's API spans text generation, vision, audio transcription, image generation, and embeddings. The Assistants API adds persistent threads, file search, and code interpretation.

Structured outputs and function calling let GPT models return validated JSON matching a schema. This turns LLM responses into reliable API contracts that downstream systems can parse directly.

Technology snapshot

Market demand 5/5

Current industry demand for this technology

Adoption 4/5

How widely used by development teams worldwide

Scalability 4/5

How well it handles growth in load and complexity

At a glance

Models GPT-4o, o1, GPT-4o-mini
Key features Function calling, Assistants, fine-tuning
Enterprise Azure OpenAI for data residency
Typical pattern Chat, extraction, code generation, agents
Common use cases
Chat ApplicationsContent GenerationData ExtractionCode Generation
What we deliver

Our OpenAI capabilities

01

Function calling & structured outputs

Validated JSON matching provided schemas. Type-safe, parseable responses directly actionable by systems.

Function callingJSON modeSchema validation
02

Assistants API & threads

Persistent conversation state, file retrieval, and code execution. Multi-turn workflows without client state.

Assistants APIThreadsCode interpreter
03

Fine-tuning for specificity

Specialise GPT models for classification, extraction, or tone matching with JSONL training data.

Fine-tuningJSONLEvaluation
Why Adaca

Why Adaca for OpenAI?

Enterprise GPT deployment

Azure OpenAI for data residency, private endpoints, and APRA compliance.

Production-grade integration

Retry logic, token budgets, rate limits, and graceful degradation for production systems.

Fine-tuning & evaluation

Structured pipelines with held-out evaluation sets and A/B comparison against base models.

Observability & cost control

Token counts, latency, and model version logged per call. Per-feature budgets prevent runaway spend.

Multi-model architecture

Routing across GPT-4o, o1, and mini based on complexity to balance cost and quality.

Realistic AI evaluation

Domain-specific test cases, human preference scoring, and automated regression checks.

Deploying GPT in production?

Talk to our AI team about GPT integration, fine-tuning strategy, or Azure OpenAI deployment.

Talk to Our Experts