AI, Data & Intelligence
LLM Integration
Adding large language model capabilities to existing applications through provider APIs, handling streaming, structured output, rate limits, retries, data redaction and switching between providers.
Capability overview
What llm integration involves
LLM integration brings language model features into software that already exists, such as a summary button in a case management tool, smart autocomplete in a form or document drafting in a CRM. The integration layer decides how requests are formed, what data leaves your environment and how the application behaves when a provider is slow or down.
We implement a gateway between your applications and providers such as OpenAI, Anthropic, Google Gemini, Azure OpenAI or AWS Bedrock. It handles authentication, personal data redaction, rate limiting, retries with backoff, provider fallback, caching and logging, so each application team does not reinvent these controls.
What is included
Integration layer features
Provider abstraction
A consistent internal API over multiple providers, making model changes a configuration decision tested against evaluation sets.
Data redaction
Personal identifiers such as names, phone numbers, Aadhaar or PAN numbers masked before prompts leave your environment where policy requires.
Resilience controls
Timeouts, retries, circuit breakers and fallback models so a provider outage degrades features gracefully.
Streaming and structured output
Token streaming for responsive interfaces and schema-validated JSON for features that feed other code.
Usage governance
Per-application keys, quotas, cost attribution and request logging with configurable retention.
How we work
How we deliver llm integration
Feature scoping
Target features, data involved and acceptable latency agreed with product owners.
Data policy check
Data classification, provider terms and residency requirements reviewed with security and legal teams.
Gateway setup
Integration layer deployed with provider accounts, secrets management and monitoring.
Application changes
Features built into the existing codebase with loading states, error messages and feature flags.
Rollout and observation
Features released gradually while latency, errors, user feedback and cost are monitored.
Related capabilities
Related capabilities in Generative AI & LLM Engineering
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Questions & answers
Questions about LLM Integration
Cannot find what you need? Our team responds to technical and commercial questions within one business day.
Ask a questionEnterprise API terms from the major providers state that business data sent through their APIs is not used for training by default. We confirm the current terms for your chosen provider and plan during design.
Yes. Open source gateways such as LiteLLM, or cloud API management services, can provide the abstraction layer. We choose based on your hosting preferences and required controls.
Short tasks with smaller models can complete in about a second, while long generations take several seconds. Streaming, caching and asynchronous processing keep the user experience responsive.
The gateway setup is a fixed-price project. Each application feature is then quoted separately based on its complexity and the changes needed in the existing codebase.
Next step
Discuss llm integration with Acmez
Share what you need to change, build, integrate or support. We will map the practical next step.