LLM Integration
Language-model capabilities built into the products you already operate, with dependable behavior and predictable cost.
Overview
Most organizations do not need a new AI product. They need the software they already run to draft, summarize, classify, extract and answer. Our LLM integration service adds those capabilities to existing applications without requiring the application to be rebuilt around a single vendor.
A language model is one component in a larger system. We engineer the components around it: prompt design, structured and validated output, fallback behavior, caching, rate limiting and usage controls. The result is a feature that behaves consistently for your users and a cost profile that finance can forecast.
What we deliver
Feature integration
Conversational assistance, summarization, drafting, translation and classification added to existing web and mobile applications.
Model selection and routing
Comparative testing of models on your own data, and routing logic that sends each request to the model offering the best balance of quality, speed and cost.
Hosted model providers
Production integrations with OpenAI, Claude, Gemini, DeepSeek and Groq, behind a single internal interface.
Self-hosted models
Deployment of open models with Ollama and comparable runtimes where data must remain inside your own infrastructure.
Structured output and validation
Responses returned in defined schemas and validated before they reach your application, so that downstream systems receive reliable data.
Cost and usage governance
Usage dashboards, per-team quotas, rate limits and spend alerts, with caching to reduce repeated calls.
Our approach
- 1Assess the use case
We define what the feature must do, the quality level required and the data it may use.
- 2Benchmark the models
Candidate models are tested with your prompts and data, and compared on quality, latency and cost.
- 3Integrate behind your API
The model is placed behind an internal service with input checks, output validation and a fallback path.
- 4Measure and refine
Every request is logged, quality is sampled and prompts are refined as real usage accumulates.
What you receive
- An integrated, tested feature in your application
- A model comparison report with a recommendation
- A provider-neutral integration layer
- Usage, quality and cost dashboards
- Prompt and configuration documentation for your team
When this service fits
This service is appropriate when you have a working product and want to add AI capabilities quickly, safely and without long-term dependence on one provider. It is also the right starting point for teams that need to move a prototype into dependable production use.
Questions and answers
Is our data used to train the provider's models?
We configure integrations to use the provider terms that exclude customer data from training, and where that is not sufficient we recommend a self-hosted model.
What happens if a provider is unavailable?
The integration layer includes time-outs, retries and a fallback model, so that a provider incident degrades the feature rather than disabling it.
Can we change models later?
Yes. The application communicates with an internal interface, so the model behind it can be replaced without changes to the product.