A familiar API surface
Plan around familiar API conventions and reduce coupling between application code and model providers. Confirm supported protocols and models before integration.
Explore ↗AI INFRASTRUCTURE / FOR DEVELOPERS
Connect to AI.
Build what’s next.
AI API integration for businesses and developers. Bring model capabilities into your applications through familiar interfaces, and keep your team focused on the product.
Familiar SDK patternsTransparent pricing plansEnterprise enquiries
ONE INTERFACE. MORE POSSIBILITIES.
YOUR APP
UBTL API
OpenAI-style
Claude / Gemini
DeepSeek
integration.py
import os from openai import OpenAI client = OpenAI( api_key=os.environ["OPENAI_API_KEY"], base_url=os.environ["OPENAI_BASE_URL"], ) response = client.chat.completions.create( model=os.environ["OPENAI_MODEL"], messages=[{"role": "user", "content": "Build something useful."}], )
SDK-COMPATIBLE PATTERNWEBSITE UNDER MAINTENANCE
Explore model ecosystems
Availability to be confirmedOpenAI compatibleAnthropic ClaudeGoogle GeminiDeepSeek
Availability to be confirmedOpenAI compatibleAnthropic ClaudeGoogle GeminiDeepSeek
BUILD WITH CLARITY
Less integration work.
More room to build.
Bring model integration, usage costs and engineering practices into one clear plan, from a first prototype to a maintainable application.
Designed for model workflows
Build a consistent engineering workflow for chat, generation and tool use. Evaluate models against task quality, context needs and cost.
Explore ↗Understand your usage
Estimate costs using explicit rate multipliers and usage units. Agree on quotas, billing rules and failure handling before going live.
Explore ↗DEVELOPER FIRST
A familiar SDK.
A clearer path to integration.
Keep the tools you know. Start by confirming model access, then validate each integration step with a minimal request.
01
Define the requirements
Choose the use case, model, budget and data boundaries.
02
Configure the connection
Use server-side environment variables. Keep keys out of the browser.
03
Test, then scale carefully
Validate responses, error handling and usage before production.
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["OPENAI_API_KEY"],
base_url=os.environ["OPENAI_BASE_URL"],
)
result = client.chat.completions.create(
model=os.environ["OPENAI_MODEL"],
messages=[{"role": "user", "content": "Hello, AI."}],
)
print(result.choices[0].message.content)# Set these to values confirmed by your service contact.
curl "${OPENAI_BASE_URL}/chat/completions" \
-H "Authorization: Bearer ${OPENAI_API_KEY}" \
-H "Content-Type: application/json" \
-d "{\"model\":\"${OPENAI_MODEL}\",\"messages\":[{\"role\":\"user\",\"content\":\"Hello, AI.\"}]}"import OpenAI from "openai";
// Run on your server. Never expose a key in browser code.
const client = new OpenAI({
apiKey: process.env.OPENAI_API_KEY,
baseURL: process.env.OPENAI_BASE_URL,
});
const result = await client.chat.completions.create({
model: process.env.OPENAI_MODEL,
messages: [{ role: "user", content: "Hello, AI." }],
});
console.log(result.choices[0].message.content);Integration example, not a live API test. Confirm the base URL, model and access before use.
MADE FOR REAL WORK
Practical AI for the work you do.
From a first experiment to a carefully managed enterprise workload.
Commercial applications
Plan AI capabilities for support, search and content workflows, with clear quality and data boundaries.
Research & evaluation
Compare models on reproducible datasets. Record parameters, versions and evaluation results.
Prototypes & startups
Validate a product idea with a minimal integration before investing in scale.
Batch processing
Design queues, rate limits and retries to keep larger workflows manageable.
Agents & tool use
Separate model output from execution, and authorize and validate every tool call.
Developer workflows
Understand each editor or client’s protocol requirements and verify compatibility by version.
NOTES FROM THE BUILD
Useful ideas for building with AI.
Give reasoning agents tools, not unchecked authority
Design tool use around permissions, validation, and explicit human decisions.
Separate account access, API secrets, and spending responsibility
Reduce exposure through ownership, isolation, and a rehearsed response plan.
Choose a model with evidence, not a name
Build a repeatable selection process around tasks, data, and failure costs.
LET’S BUILD
Your next AI product starts here.
Tell us about your use case, model requirements and budget. Let’s work out a practical integration plan.