AI API integration: add intelligence to your software
Gjethe adds AI features to software you already have (summarising documents, classifying and routing requests, extracting data from emails and PDFs, generating drafts, transcribing speech and analysing images) using APIs from OpenAI, Anthropic, Google, Mistral and open-source models, with guardrails, logging and cost controls built in.
- Free first consultation
- Written quote for your project
- Flexible payment milestones
At a glance
- Providers
- OpenAI, Anthropic, Google, Mistral, open models
- Capabilities
- Text, extraction, speech, vision
- Built in
- Guardrails, logging, cost limits
- Fits into
- Web apps, mobile apps, back-office tools
Definition
What is AI API integration?
AI API integration is connecting an existing application to an AI model provider's API so the application can use capabilities such as text generation, classification, extraction, speech recognition or image understanding as part of its normal features.
You don't need to train a model to use AI. The most capable models are a few API calls away. The work is in using them well: choosing the right model for the task and budget, designing prompts and structured outputs your code can rely on, handling failures and limits, protecting personal data and measuring whether the feature actually helps users.
Who is it for?
SaaS products adding AI features
Summaries, smart search, drafting assistants and automatic categorisation that make your product more useful.
Back offices drowning in documents
Invoices, forms, emails and contracts where data must be read and entered by hand.
Support and operations teams
Incoming requests that need sorting, prioritising and routing before anyone can act.
Products with audio or images
Transcription, captioning, image tagging or visual inspection added to existing workflows.
What's included, and what you keep
What's included
- Use-case review and feasibility test
- Model and provider selection with cost estimates
- Prompt and structured-output design
- Integration into your codebase or workflows
- Validation of AI output before it's used
- Fallbacks when the AI is unsure or unavailable
- Personal data redaction where needed
- Caching and cost controls
- Rate-limit and retry handling
- Logging and quality monitoring
- Evaluation on real examples
- Documentation for your developers
What you receive
Handed over at the end. Yours to keep.
- AI feature integrated and deployed
- Prompts and schemas in version control
- Evaluation results on your data
- Cost and usage dashboard
- Developer documentation
Common AI features we integrate
Extraction
Turn invoices, forms and emails into structured data your systems can use.
Classification and routing
Sort tickets, leads and documents by topic, urgency or sentiment.
Drafting
First drafts of replies, descriptions or reports for a person to review.
Semantic search
Search by meaning, not just keywords, across your product or content.
How do we make AI output reliable enough for production?
We ask models for structured output, JSON that matches a schema, rather than free text, and validate it before it touches your database. Fields that fail validation trigger a retry or go to a person.
For decisions, we use confidence signals and thresholds: high-confidence results flow through automatically, uncertain ones are flagged for review. That way the AI handles the bulk of routine cases and people handle the exceptions.
Finally, we keep a set of real examples with known correct answers and run it whenever prompts or models change, so quality is measured instead of assumed.
Choosing a model
| Consideration | Why it matters |
|---|---|
| Task difficulty | Simple classification runs well on small, cheap models; complex reasoning needs larger ones |
| Language | Some models handle Albanian or German noticeably better than others |
| Latency | Interactive features need fast responses; background jobs can use slower, cheaper options |
| Data requirements | Business terms, regions or self-hosting may be required for sensitive data |
| Cost at volume | Per-request cost multiplied by your volume decides what's sustainable |
How AI API integration works, step by step
Every step has a clear output you can review. Timing is agreed in your quote.
- 1
Feasibility test
We try the task on a sample of your real data with a few models before committing.
- 2
Design
Prompts, output schemas, validation and fallback rules.
- 3
Integrate
AI calls added to your application with error handling, caching and limits.
- 4
Evaluate
Accuracy, speed and cost measured on a test set.
- 5
Launch
Release behind a feature flag or to a subset of users first.
- 6
Monitor
Quality, usage and cost tracked, with alerts for anomalies.
Tools, and what shapes your quote
Pricing is based on your project and agreed in your quote. These are the things that move it.
| Factor | Why it matters |
|---|---|
| Task complexity | Extraction from clean forms is simpler than reasoning across long, messy documents. |
| Accuracy requirements | High-stakes uses need more evaluation and human review steps. |
| Data sensitivity | Redaction, regional hosting or self-hosted models add work. |
| Volume | High volumes justify optimisation: caching, batching and smaller models. |
| Integration points | Each place in your product or workflow the feature appears adds development. |
| Area | Tools |
|---|---|
| Providers | OpenAI, Anthropic, Google Gemini / Vertex AI, Mistral, Cohere |
| Open models | Llama, Mistral open weights, Whisper, Hugging Face |
| SDKs | Vercel AI SDK, provider SDKs, Instructor / Zod schemas |
| Speech & vision | Whisper, ElevenLabs, vision-capable models |
| Monitoring | Langfuse, Sentry, cost dashboards |
Common mistakes to avoid
What we see go wrong most often, and how a good project avoids it.
Trusting free-text output in code
Parse structured, validated output instead of hoping the model's text has the right format.
Using the biggest model for everything
Many tasks run perfectly on smaller models at a fraction of the cost.
Sending personal data unnecessarily
Redact or minimise data before it leaves your system, and use providers' business terms.
No fallback
Providers have outages and rate limits. Features must degrade gracefully.
Frequently asked questions
Which AI model should we use?
It depends on the task, language, latency, cost and privacy requirements. We often prototype the same feature on two or three models and choose based on results with your real data.
How do you keep AI costs under control?
We cache repeated requests, use smaller models where they're good enough, cap usage per user and track spending so there are no surprises.
What about privacy and GDPR?
We use providers' business terms where data isn't used for training, EU regions where available, and redact personal data before it leaves your system when needed.
Can AI read our invoices and forms?
Yes. Modern models extract fields from PDFs, scans and emails into structured data, with validation and human review for uncertain cases.
Can we switch AI providers later?
Yes. We keep provider-specific code behind a small abstraction, so changing models is a configuration and testing task rather than a rewrite.
Can you run models on our own servers?
Yes, with open-weight models, when data must not leave your infrastructure or when volume makes self-hosting cheaper.
How do we know the feature is good enough?
By measuring it on a test set of your real examples before launch and monitoring quality in production.
Do you add AI to mobile apps too?
Yes, usually through your backend so API keys stay secure and usage can be controlled.
Can AI transcribe calls or meetings?
Yes, with speech-to-text models, plus summaries and action items when useful.
Who owns the prompts and integration?
You do. Prompts, schemas and code live in your repository.
Can AI features work in Albanian or German?
Yes. Leading models handle both well for most tasks. We test with your real Albanian or German content, because quality varies by task and model.
How quickly do AI features respond?
Small models can respond almost instantly; larger models take longer. For interactive features we stream responses so users see text appear immediately, and run heavier tasks in the background.
What if the AI provider changes or retires a model?
Providers announce retirements in advance. Because prompts and evaluation sets live in your repository, we can test a replacement model and switch with confidence.
Can you improve an AI feature we already built?
Yes. We review prompts, model choice, validation and costs, build an evaluation set from real usage and improve accuracy and spend step by step.
Related services
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AI Automation
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Web Applications & SaaS
Portals, dashboards and subscription products.
Computer Vision
Image and video recognition, inspection and OCR.
Machine Learning
Prediction, classification and recommendation models.
AI API Integration by industry
Add AI to your product.
Tell us what you need. You get an honest assessment, a clear plan and a written quote. The first consultation is always free.
- What happens next
- 1You tell us what you need
- 2We aim to reply within 24 hours with questions
- 3You get a written quote for your project