AI API Integration (GPT, Claude, Gemini)
“The right model for the right task, integrated cleanly.”
We integrate OpenAI, Anthropic, Google, and open-source models into your existing product.
Whether you need a summarization feature, a writing assistant, a classification pipeline, or a content generator - we pick the right model, build the integration, and handle the edge cases.
Start with a focused first build.
Send the workflow, the tools involved, and where the handoff breaks. We will map the smallest build that can prove value before you commit to a larger system.
The useful parts of this build.
These are the pieces buyers usually need when the workflow has to run inside a real product, CRM, dashboard, or internal operation.
Right model routed to each task based on quality, speed, privacy, and cost
Prompt and schema design for outputs your product can trust
Streaming UI states so users see progress instead of waiting blindly
Usage tracking and cost controls before volume grows
Fallbacks, retries, and validation when a model or provider fails
How this moves from audit to production.
The first version stays narrow enough to ship, but includes the architecture, integrations, model layer, review path, and observability needed by a real team.
Identify product actions that need generation, classification, summarization, translation, extraction, or tool calling.
Design provider routing across OpenAI, Claude, Gemini, Mistral, or local models based on latency, cost, context size, and risk.
Build streaming UI states, cancellation, structured output validation, retry policies, and prompt version control.
Add token accounting, model fallback, request logging, rate-limit handling, and user-level usage controls.
Evaluate outputs through fixtures, red-team prompts, and production traces before expanding the AI feature surface.
Questions before building this workflow.
Can you integrate multiple AI providers in one product?
Yes. We build a routing layer that can select OpenAI, Claude, Gemini, Mistral, or local models per task while keeping logs and fallbacks consistent.
How do you control AI API costs?
We use prompt trimming, model routing, caching, token budgets, batch jobs, streaming limits, and usage dashboards so costs are visible before they become a surprise.
Can the AI call product tools safely?
Yes. Tool calls are validated through schemas, permissions, dry-run states, and confirmation steps for actions that change records or trigger external systems.
Related services in this category.
Send one workflow.
Send the workflow. We will show what to build first.