APIs and backend services that bring AI into your software, apps, and operational systems. Classification, summarisation, extraction, generation. Exposed as clean, documented endpoints any application can call.
Build once, use everywhere. Model-agnostic. Centrally managed. Scalable from 10 users to 10,000.
Your first AI feature was embedded directly in one application. It works. Now three more apps need the same capability. And two more need different AI functions. Each one is being built from scratch: different prompts, different error handling, different guardrails, different cost management. The AI logic is spreading, and nobody has a central view.
This is the infrastructure gap. AI models are powerful. But using them well in production (with guardrails, monitoring, cost control, caching, rate limiting, and scalable architecture) requires a proper backend layer between your applications and the models.
HELLO PEOPLE builds AI API layers for Australian businesses. Clean REST endpoints your developers call. Complexity managed behind the API. Central control, consistent quality, and a foundation that scales as you add more AI use cases across your stack.
If your AI capabilities are scattered across apps with no central management, you need an API layer.
You built an AI feature inside one app. Now two other apps need the same capability: classification, summarisation, data extraction. But the logic is buried in the first application. Rebuilding it three times is not a plan. You need it as a service.
Different teams, different apps, different ways of calling the same AI model. One team uses the OpenAI SDK directly. Another calls through a Python wrapper. A third is using a no-code tool. No consistency, no shared guardrails, no central monitoring. Each integration is a one-off.
Every app calls the model directly. Some make too many calls. Some pass oversized prompts. Some retry failures without backoff. The monthly API bill keeps climbing and nobody can tell you which app or which feature is responsible.
The AI feature works for 10 users. You want to roll it out to 500. But the current approach (direct model calls from the frontend, no caching, no rate limiting, no queue) will not hold. You need backend infrastructure, not just a prompt.
AI capabilities spread across apps means security spread across apps. API keys stored in different places. Data flowing through different paths. No central audit log. No single place to apply guardrails. Compliance becomes a per-app problem instead of a solved-once problem.
Every new AI feature requires your developers to figure out prompt engineering, model selection, error handling, rate limiting, and response parsing from scratch. The same decisions, made fresh every time. A central AI API layer eliminates the repetition.
Tell us about your applications, your AI use cases, and your scale requirements. We will design an API layer that fits.
Five categories of AI backend services. Most businesses start with reusable microservices and add orchestration as complexity grows.
Self-contained AI services. Each one does one thing well. A classification service. A summarisation service. A data extraction service. An embedding service. A content generation service. Each with its own endpoint, documentation, and versioning.
Any application in your stack can call any service. Your CRM, your portal, your mobile app, your internal tools. They all get the same AI capabilities through the same standardised interfaces.
Each microservice includes guardrails, rate limiting, error handling, and logging. You build it once. Every app benefits.
Complex AI tasks rarely involve a single model call. A document processing pipeline might: receive the file, extract text, classify the document type, extract specific fields based on the classification, validate the data, and push it to the target system. That is six steps, not one.
We build orchestration layers that chain AI operations together. Step-by-step pipelines with error handling, retry logic, conditional routing, and monitoring at each stage.
The orchestration layer manages the complexity. Your applications just call one endpoint and get the result. What happens behind that endpoint (model calls, data lookups, validation, routing) is abstracted away.
A central gateway that sits between your applications and the AI models. Every AI request goes through the gateway. Authentication, rate limiting, usage tracking, request logging, cost attribution. All handled in one place.
Model routing handled centrally. GPT-4o for complex tasks, a lighter model for simple classification, a local model for sensitive data. The calling application does not need to know which model handles its request. The gateway decides based on your rules.
If a model provider has an outage, the gateway can failover to an alternative. If a new model performs better, you update the gateway, not every application. Central control, zero per-app changes.
AI endpoints that do not just process text. They query your data first. A "summarise customer" endpoint that reads the CRM, recent emails, and support tickets before generating a summary. An "answer question" endpoint that searches your knowledge base before responding.
The data connection logic lives in the API, not in the calling application. Your CRM does not need to know how to search your document repository. It calls the API. The API handles the retrieval, the context assembly, and the model call.
Data access scoped by the calling user's permissions. The API respects role-based access. A sales team member gets sales-relevant data. A support agent gets support-relevant data. Same API, different results based on who is asking.
AI that triggers automatically when things happen in your systems. A new support ticket created → AI classifies and routes it. An invoice uploaded → AI extracts the data. A contract signed → AI updates records across three systems.
Webhook receivers that listen for events from your CRM, helpdesk, document management, or any system. When the event fires, the AI pipeline runs. No user action required.
Asynchronous processing with status tracking. Large jobs queue and process in order. Your systems are not blocked waiting for AI to finish. Results pushed back when ready.
We built an enterprise knowledge management system powered by RAG. Unified search across 50,000+ documents delivers answers in seconds instead of hours.
Read the full case studyNot raw model wrappers. Production-grade backend services with documentation, monitoring, guardrails, and cost management built in.
Clean REST APIs with OpenAPI documentation. Your developers can integrate in hours, not weeks. SDKs and code examples for common languages.
Swap underlying models without changing the API contract. Your apps call the same endpoints regardless of whether GPT-4o, Claude, or Llama handles the request.
Input validation, output filtering, hallucination detection, and topic boundaries applied centrally. Every app gets the same safety layer without building its own.
Dashboard showing requests, latency, errors, token usage, and cost, broken down by app, by endpoint, and by user. Know exactly where your AI spend goes.
API key management, role-based access, and per-endpoint permissions. Control which apps can call which AI functions. Full audit trail.
Response caching for repeated queries. Rate limiting per app and per user. Queue management for high-volume processing. Cost-efficient by default.
The first AI feature takes weeks to build. Classification logic, prompt engineering, error handling, model integration, testing, guardrails. Then your second app needs the same capability. Without a shared API layer, you build it again. And again for the third app.
An AI API layer means you build the capability once and expose it as a service. Your CRM calls it. Your portal calls it. Your mobile app calls it. Your internal tools call it. Same logic, same guardrails, same quality. Zero duplication.
One company built a document classification service as an API. Within six months, five different internal applications were using it. The effort to add classification to each new app dropped from weeks to a single afternoon of API integration.
When AI logic is scattered across applications, managing it is a nightmare. Which apps are calling which models? How much is each costing? Are guardrails applied consistently? Is anyone sending data they should not?
A central AI API layer gives you one dashboard for all AI activity. Usage, cost, performance, errors, and security. Visible in one place. Guardrails configured once and applied everywhere. Model changes made centrally.
This is how AI governance works at scale. Not per-app policies and per-app monitoring. Central management with consistent controls.
Direct model calls from application frontends do not scale. No caching means repeated identical queries cost the same every time. No queuing means burst traffic overwhelms the model. No rate limiting means one runaway app burns through your budget.
A properly architected AI API layer includes caching (identical questions get instant cached answers), queuing (burst traffic processes in order), rate limiting (no single app monopolises capacity), and auto-scaling (infrastructure grows with demand).
The difference between 10 users and 10,000 users is infrastructure architecture, not AI model capability. Get the API layer right and scaling is a configuration change.
Without an AI API layer, every developer building an AI feature needs to learn prompt engineering, model APIs, error handling patterns, guardrail implementation, and cost management. That is a steep learning curve that slows delivery.
With an AI API layer, your developers call a documented REST endpoint. Send this input, get this output. The AI complexity is abstracted behind the API. A junior developer can add AI-powered classification to a new app in a day.
Your AI specialists focus on improving the API layer: better prompts, better models, better guardrails. Your application developers focus on building features. Everyone works where they add the most value.
Any business running multiple applications that need AI capabilities. These are the most common patterns.
One AI backend serving your CRM, portal, mobile app, and internal tools. Classification, summarisation, and generation available as reusable endpoints across your stack.
An API that receives documents, extracts data, classifies them, and pushes structured results to your systems. Called from any app that handles document uploads.
A search endpoint that understands natural language queries, searches your data sources, and returns contextual answers. Embedded in portals, apps, and support tools.
Building a SaaS product? Add AI features via API without coupling AI logic to your application code. Ship, iterate, and improve AI independently from your core product.
Webhook-triggered AI that responds to events in your CRM, helpdesk, or document management. New ticket → classify and route. New document → extract and file. New lead → score and assign.
A centralised content API for product descriptions, email drafts, report narratives, and marketing copy. Guardrailed for brand voice and accuracy. Used across your website, CRM, and marketing tools.
A structured approach. First service deployed early so your team can start integrating.
Model-agnostic, cloud-flexible, and built to integrate with your existing systems.
Model-agnostic by design. Route to different models based on task, cost, or data sensitivity. Swap without changing consuming apps.
Deployed in Australian data centres. Serverless, containerised, or dedicated, matched to your volume and latency requirements.
Data-connected APIs that query your databases for context before generating responses. Real-time data, not stale embeddings.
Webhook integration for event-driven AI. Listen for CRM/helpdesk events and trigger AI processing automatically.
RAG-connected APIs with vector search for knowledge retrieval. Semantic search over your documents and data.
Asynchronous AI processing for high-volume and long-running tasks. Queue, process, and deliver results without blocking.
We had AI logic scattered across four apps. Each one called OpenAI differently. Each one had different guardrails. Costs were invisible. HELLO PEOPLE built us a central AI API. Now all four apps call the same endpoints. We monitor everything from one dashboard, and adding AI to new apps takes days instead of months.
We write the code, design the interface, deploy the systems, and support them long-term. No subcontracting, no offshore handoffs.
You get a clear price before we start. No hourly billing that spirals, no surprise invoices at the end of the month.
We understand BAS, super, award rates, Australian privacy law, and the tools local businesses actually use: Xero, MYOB, ServiceM8, Tradify.
You talk to the people building your software. No account managers, no project managers relaying messages, no ticket queues.
You own everything: the code, the data, the hosting. No lock-in. No proprietary platforms you cannot leave.
An AI API is a backend service that exposes AI capabilities through standard web endpoints. Your applications send a request ("classify this text" or "summarise this document") and get a structured response back. The AI model, prompts, guardrails, and data connections are all managed behind the API. Your app never touches the model directly.
If you have one application and one AI use case, embedding AI directly in the app is fine. An API layer becomes valuable when you have multiple apps, multiple use cases, or plans to expand AI across your stack. It is also valuable when you need central control over cost, security, and guardrails.
Yes. We build standard REST APIs with full OpenAPI documentation. Any developer who can call a REST endpoint can use the AI services. We provide code examples for common languages: Python, JavaScript, C#, PHP. The complexity is behind the API, not in front of it.
The API contract stays the same. If we swap from GPT-4o to Claude for a particular endpoint, the calling applications do not change. Same request format, same response format. Model selection is a backend configuration, not an application-level decision.
We provide a fixed-price quote after scoping. A focused AI API layer with 2-3 core services sits at the lower end of project size. A comprehensive platform with orchestration, gateway, monitoring and multiple services is larger. Ongoing cloud hosting and AI model API fees are scoped during the build.
A core API layer with 2-3 services takes 6 to 8 weeks. A full platform with orchestration, gateway, and monitoring takes 10 to 14 weeks. We deploy the first service early so your team can start integrating while we build the rest.
Your cloud. Azure (Sydney), AWS (Sydney), or GCP. Serverless for low-volume or bursty workloads. Containerised for consistent high-volume. We recommend the architecture that matches your usage patterns and budget.
Yes. Ongoing monitoring, model updates, prompt optimisation, and new service development. We review usage data monthly and recommend improvements. When new models or capabilities emerge, we evaluate and deploy them through the existing API layer.
Tell us about your apps, your use cases, and your scale. We will design an AI API layer that your entire stack can use.
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What applications need AI capabilities? What functions do you need: classification, summarisation, extraction, generation? What scale? We will scope an API solution.
Prefer a quick chat? Call 0425 531 127. We answer the phone in Perth.
