OpenAI and Claude API integration
We build OpenAI and Anthropic language models into your product so that answers can be checked and API spend stays predictable.
What's included
- Assistants and chatbots connected to your systems
- RAG: answers grounded in company documents and knowledge bases
- Control over answer quality and token spend
- Switching to new models and API versions, with answer quality checked on your own scenarios
Technologies
Related services
How we work
- 01
Brief and estimate
We look at the task, your current system and constraints, then name the timeline and budget and suggest how to work together.
- 02
Specification
We capture requirements and acceptance criteria in a specification — it is what we build against and how the result is accepted.
- 03
Iterative development
You see the result after every iteration; we test and fix before anything goes into a release.
- 04
Launch and support
We ship with no downtime, monitor the product after launch and stay on for support if you need it.
Cost and timeline
Hourly Rates: from 1 800 ₽ to 3 500 ₽ / hour — depending on specialist qualification.
We give an exact timeline and budget after a brief: we break down the task, fix the scope and suggest a format — a fixed plan or hourly work.
See plansFAQ
Which model should we choose: OpenAI or Claude?
It depends on the task: models differ in quality on specific scenarios, context length, speed and price. We compare them on your real examples and often build in the option to switch providers so you are not tied to one of them.
What happens to our data when it is sent to the API?
We send the model only what it needs to answer, mask personal data where possible and take the provider's data processing terms into account. The assistant's access to internal systems is limited by user permissions, and requests and responses are logged for audit.
How do you keep answer quality and cost under control?
We build a set of test examples and run it on every change to prompts or the model. Costs come down through caching, matching the model to the complexity of the request and trimming context. The cost of the integration itself depends on scope and is estimated after a short brief.