07Service

AI Integrations

Seamlessly embed LLMs and ML models into your existing stack via clean APIs.

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Overview

You don't need to rebuild your product to add AI. We integrate intelligence directly into your existing systems through clean, well-documented APIs. Our model-agnostic approach means you're never locked into OpenAI, Anthropic, or any single provider.

We handle the hard parts — inference infrastructure, auto-scaling, rate limiting, fallback routing between providers, and monitoring — so your engineering team can focus on building great products.

Key Capabilities

Model-agnostic design — OpenAI, Anthropic, open-source, or custom
Clean REST and streaming APIs with rate limiting and auth
Managed inference infrastructure with auto-scaling
Fallback and routing logic across multiple model providers

Why choose us

What you get

Provider Freedom

Switch between OpenAI, Anthropic, open-source models, or custom fine-tunes without code changes.

Production APIs

Clean REST and streaming endpoints with authentication, rate limiting, and versioning.

Auto-Scaling Infra

Managed infrastructure that scales automatically with your traffic — zero DevOps needed.

Smart Routing

Automatic fallback and load balancing across multiple model providers for reliability.

How it works

Our process

01

Use Case Scoping & Model Selection

2–3 days

Define the specific AI capability being added to your product — generation, classification, search, summarization, or conversation — and select the optimal model (OpenAI, Anthropic, Google Gemini, or open-source) based on cost, latency, accuracy, and data privacy requirements. API contract and streaming strategy designed before any code.

02

API Design & Infrastructure Architecture

3–5 days

Design the API surface your product will integrate with — REST or streaming endpoints, authentication, rate limiting, versioning, and fallback routing between providers. Infrastructure architecture: model serving layer, caching strategy (for deterministic queries), and scaling approach.

03

Build & Integration Testing

7–14 days

Implement the inference layer with provider abstraction (swappable backend without code changes), streaming response handling, structured output parsing, and error handling. Integration tested against your product's actual use patterns with real production data volumes.

04

Production Hardening & Handover

3–5 days

Load testing to verify scaling behavior. Latency monitoring and cost tracking dashboards configured. Fallback routing between providers tested for reliability. Complete API documentation and SDK delivered to your engineering team for self-service integration.

Measurable impact

Typical outcomes

Model-agnostic architecture — swap providers without code changes

99.9%+ uptime via multi-provider fallback routing

Average response latency under 800ms for production chat endpoints

Technology stack

OpenAI API (GPT-4o, o3)Anthropic API (Claude 3.7)Google Gemini APIMistral APIMeta Llama (self-hosted)FastAPIPythonStreaming (SSE)Redis (caching)DockerAWS / GCP

FAQ

Common questions

Ready to get started?

Let's discuss how ai integrations can transform your business.

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