Virtual event platforms have evolved from basic video streams into interactive experiences, but the real differentiation now comes from intelligence woven into every session. Large language models can power real-time Q&A moderation, intelligent networking, automated summarization, and vision-enabled slide analysis. The challenge for engineering teams is not choosing whether to use LLMs, but how to integrate them without letting inference costs scale alongside audience size and context length.
Architecture Overview
A modern virtual event platform typically ingests video and audio streams, extracts transcripts, captures chat messages, and surfaces slides or screen shares. An LLM layer sits between these data streams and the end user, handling moderation, question answering, scheduling, and post-event summarization. Because event transcripts and chat histories can quickly reach tens of thousands of tokens, your inference backend needs to handle long contexts efficiently and predictably.
Real-Time Q&A and Moderation
During live sessions, chat volume can overwhelm human moderators. An LLM can filter spam, surface relevant questions, and even draft answers in real time. For global events, multilingual models reduce friction. Oxlo.ai hosts Qwen 3 32B, which handles multilingual reasoning and agent workflows, and Llama 3.3 70B as a general-purpose flagship for fast, accurate responses. Both support streaming responses, so moderators see suggestions appear token by token rather than waiting for a full generation.
import openai
client = openai.OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key="YOUR_OXLO_API_KEY"
)
response = client.chat.completions.create(
model="llama-3.3-70b",
messages=[
{"role": "system", "content": "You are a moderation assistant for a tech conference."},
{"role": "user", "content": "Flag any off-topic messages in this chat log and summarize the top 3 questions."}
],
stream=True
)
for chunk in response:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="")Agentic Networking Assistants
Networking is the main reason people attend virtual events. An LLM with function calling can act as a concierge, reading attendee profiles and scheduling meetings via calendar APIs. Oxlo.ai supports function calling and tool use across its chat models, including DeepSeek R1 671B MoE for deep reasoning and complex coding tasks. You can define tools for calendar lookups, profile searches, and CRM updates, then let the model decide which to call.
tools = [
{
"type": "function",
"function": {
"name": "schedule_meeting",
"description": "Schedule a 1:1 between two attendees",
"parameters": {
"type": "object",
"properties": {
"attendee_a": {"type": "string"},
"attendee_b": {"type": "string"},
"time_slot": {"type": "string"}
},
"required": ["attendee_a", "attendee_b", "time_slot"]
}
}
}
]
response = client.chat.completions.create(
model="deepseek-r1-671b",
messages=[{"role": "user", "content": "Find a time for Alice and Bob to meet tomorrow."}],
tools=tools,
tool_choice="auto"
)Transcription and Summarization
Post-event content is valuable, but only if attendees can search and summarize it. You can transcribe session recordings with Whisper Large v3 or Whisper Turbo via the Oxlo.ai audio/transcriptions endpoint, then pass the resulting text to a long-context model for summarization. Event transcripts are often lengthy, and summarizing multiple sessions multiplies your input token count. Unlike token-based providers, Oxlo.ai charges one flat cost per request regardless of prompt length. For long-context summarization, models like DeepSeek V4 Flash support a 1 million token context window, and Kimi K2.6 handles advanced reasoning across 131K contexts. This makes Oxlo.ai significantly cheaper for long-context and agentic workloads when you are processing bulk transcripts.
Vision-Enabled Slide Analysis
Speakers share slides, diagrams, and demos. A vision model can extract text from screenshots, explain diagrams, or index visual content for search. Oxlo.ai offers vision models including Gemma 3 27B and Kimi VL A3B. Because these support image input through the chat/completions endpoint, you can pass base64-encoded screenshots alongside text prompts using the same OpenAI SDK pattern.
response = client.chat.completions.create(
model="gemma-3-27b-it",
messages=[
{"role": "user", "content": [
{"type": "text", "text": "Extract the key takeaways from this slide."},
{"type": "image_url", "image_url": {"url": "data:image/png;base64,..."}}
]}
]
)Why Request-Based Pricing Matters for Events
Virtual events generate unpredictable context lengths. A single panel discussion transcript can exceed 50,000 tokens. If your platform performs multi-turn agentic workflows or maintains a global context of the entire event, token counts compound quickly. Oxlo.ai uses request-based pricing, meaning you pay one flat cost per API request regardless of prompt length. For long-context and agentic workloads, this can be 10-100x cheaper than token-based alternatives. There are no cold starts on popular models, so latency stays consistent when traffic spikes between sessions. You can explore the exact structure at https://oxlo.ai/pricing.
Implementation Example
Putting it together, here is a minimal example that uses JSON mode to return structured data from a messy event chat log. Oxlo.ai is fully OpenAI SDK compatible, so the only change is the base URL.
import openai
import json
client = openai.OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key="YOUR_OXLO_API_KEY"
)
response = client.chat.completions.create(
model="llama-3.3-70b",
messages=[
{"role": "system", "content": "Extract questions and their sentiment as JSON."},
{"role": "user", "content": "[Chat log with 12,000 tokens of attendee messages...]"}
],
response_format={"type": "json_object"}
)
data = json.loads(response.choices[0].message.content)
print(data)Conclusion
Building a virtual event platform with LLMs requires more than model access. You need multilingual chat, vision understanding, audio transcription, function calling, and long-context summarization, all at a cost that does not punish engagement. Oxlo.ai provides 45+ open-source and proprietary models across 7 categories, fully OpenAI SDK compatible, with request-based pricing that flattens costs for the long contexts typical of live events. Whether you are moderating real-time chat with Qwen 3 32B, transcribing sessions with Whisper, or analyzing slides with Gemma 3 27B, Oxlo.ai is designed to handle the workload without scaling surprises.
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