Unpredictable costs
Usage-based pricing means a bug, a loop or a traffic spike can run up a large bill without anyone noticing.
AI & Automation · API Integration
AI API integration services built for production traffic
Calling an AI API in a demo takes minutes. Running it inside a real product means handling keys, quotas, timeouts, retries, costs, provider outages and the data you send. That surrounding engineering is what we build.
What is AI API Integration?
AI API integration services connect your applications and business systems to external AI capabilities delivered over APIs, including language models and specialized services for OCR, speech-to-text, text-to-speech, image analysis and translation. The work covers authentication, request design, rate limits, error handling, cost monitoring, fallbacks between providers and data-handling controls. It turns a single API call into a dependable part of your software.
AI providers change models, deprecate versions, enforce rate limits and occasionally go down, and every call costs money. An integration that hard-codes one provider's API key into an app, retries blindly on failure and logs full customer data can work in testing and still cause trouble in production. A small abstraction layer with sensible limits, monitoring and fallbacks makes the difference.
What breaks when AI APIs meet real traffic
Rate limits and timeouts
Providers cap requests and tokens per minute, and long requests can exceed your app's timeout under load.
Provider and model changes
Models are updated or retired, and responses can change between versions, breaking integrations that assumed fixed behavior.
Data sent without review
Integrations often pass entire records to third-party APIs when only a few fields are needed.
Our AI API Integration service
Provider and service selection
Comparing candidate APIs for the task on your sample data, considering quality, language support, latency, pricing and data terms.
Integration layer
A server-side service that wraps provider APIs behind your own interface, so applications don't depend directly on one vendor's API.
Key and secret management
API keys kept server-side in a secrets store, scoped per environment and rotated, never shipped inside web or mobile apps.
Reliability engineering
Timeouts, retries with backoff, queues for long-running jobs, request rate control and fallback to an alternate provider or graceful degradation.
Cost and usage controls
Per-feature and per-customer usage tracking, budgets and alerts, caching of repeated requests and limits on input size.
Monitoring and versioning
Logs of latency, errors and usage, pinned model versions where providers allow it, and tests that catch behavior changes on upgrade.
Included capabilities
- Language model API integration
- OCR and document vision APIs
- Speech-to-text and text-to-speech
- Image analysis and moderation APIs
- Machine translation APIs
- Server-side key management
- Retries, queues and timeouts
- Multi-provider fallback
- Usage budgets and alerts
- Response caching
What AI API Integration changes for your business
Fewer production incidents
Timeouts, retries and fallbacks are designed to absorb provider problems so they don't break your app.
Spend you can see
Usage tracking and alerts reveal cost by feature or customer before the invoice arrives.
Freedom to switch providers
An internal interface lets you change or add providers without rewriting applications.
Controlled data exposure
Only the fields each task needs are sent to external services.
Where it makes the biggest difference
Voice notes to structured records
Field staff record a voice note in a mobile app, which is transcribed and summarized into a visit report for review.
Multilingual customer messages
Incoming messages translated for agents, and replies translated back, with the original text kept alongside.
Image checks in apps
Uploaded photos checked for quality or inappropriate content before they reach a listing or a reviewer.
Adding language features to a SaaS product
A product team adds summarization or drafting through a provider-agnostic layer, with per-customer usage tracking for billing.
Six steps from requirement to monitored integration
We load-test and estimate cost before launch, so rate limits and monthly spend are known quantities.
Define the capability
Specify the task, input and output formats, expected volume and acceptable latency and cost.
Compare providers
Test candidate APIs on real samples and review their pricing, limits and data-retention terms.
Design the layer
Agree the internal API, data minimization rules, error handling and fallback strategy.
Build and test
Implement the integration with automated tests, including failure scenarios such as timeouts and rate-limit errors.
Load and cost check
Simulate expected volume to confirm rate limits, latency and monthly cost estimates.
Deploy and monitor
Release with dashboards and alerts for errors, latency and spend, and document how to switch providers.
What it connects to
AI API Integration
- Language model APIs from major providers
- Cloud OCR and document AI services
- Speech recognition and synthesis APIs
- Image and video analysis APIs
- Translation APIs
- Odoo and ERP systems
- Web and mobile applications
- Secrets managers and cloud key vaults
Built with
Specialized AI service or general language model
| Criterion | Specialized API (OCR, speech, translation) | General language model API |
|---|---|---|
| Strength | Optimized for one task | Flexible across many text tasks |
| Output | Predictable, structured | Varies; needs validation |
| Cost pattern | Per page, minute or character | Per token of input and output |
| Typical use | Transcription, text recognition, translation at volume | Summarizing, classifying, drafting, reasoning over text |
Security, quality and scale
Secrets never in clients
Keys stay on the server, so they can't be extracted from browser code or mobile app bundles.
Field-level minimization
Personal and confidential data is removed or masked before requests leave your systems where possible.
Provider terms review
Data retention, training use and processing location are reviewed for each provider before go-live.
Abuse protection
Authentication and per-user limits on AI endpoints help prevent your account being used to run up costs.
What working with us looks like
Integration specialists
We build APIs and third-party integrations across ERP, web and mobile, and treat AI services with the same discipline.
Provider-neutral
Provider recommendations come from testing on your data, and the integration layer keeps you free to change providers later.
Costs designed in
Usage tracking and budgets are part of the first release, not added after a surprising bill.
Works with your stack
We integrate with Node.js, Python and PHP applications, Flutter apps and Odoo.
What it costs
Fixed-price packages with the scope written down, paid stage by stage. Your project goes live once it's fully paid.
Related services, technologies and industries
Sources and further reading
Official documentation and guidance if you want to read more about this topic.
AI API Integration: questions we hear most
Which AI providers can you integrate with?
We integrate with any provider that offers a documented API, including language model providers such as OpenAI, Anthropic and Google, and cloud services for OCR, speech, vision and translation. We recommend providers after testing on your data rather than by default.
How do you control AI API costs?
We track usage by feature and customer, set budgets and alerts, cache repeated requests, limit input size and choose smaller models where they perform well enough. Load testing before launch gives a realistic monthly estimate.
What happens if the AI provider goes down?
The integration is designed to fail gracefully: requests time out, retry with backoff, and either fall back to an alternate provider or show users a clear message. Long-running work goes through a queue so it can resume.
Can we call AI APIs directly from our mobile app?
We advise against it, because the API key would be embedded in the app and could be extracted and misused. Requests should go through your own server, which holds the key, applies limits and logs usage.
How is this different from LLM integration?
AI API integration focuses on the connection: authentication, reliability, cost and fallbacks across many kinds of AI services. LLM integration focuses on how a language model behaves inside your product, including prompts, retrieval, guardrails and evaluation.
Next step
Tell us which AI service you want to connect
Describe the capability, the system it should plug into and your expected volume. We'll recommend providers to test and outline how the integration should handle failure and cost.