The AI behind support: three models, one automatic fallback

Three models, a single configuration

Claude, GPT and Gemini are implemented as interchangeable options inside SDRBOT.ai — each with its own integration, but all answering to the same set of instructions, products and qualification rules your organization configured. Switching providers does not mean reconfiguring the AI SDR from scratch.

The default provider is chosen right in the account settings, among Anthropic, OpenAI, Gemini or the system default — no technical support required to change it. That lets you, for instance, test which model does better in your business's tone of voice and switch without losing any configuration you already made.

When one provider fails, another takes over

If the call to the chosen AI provider fails — API down, limit reached, malformed response — SDRBOT.ai detects the failure on the spot and re-sends the same conversation to a second provider automatically, within the same message, without the lead noticing anything went wrong on the other side. There is no need to restart the conversation or wait for someone on your team to spot the failure and act.

Which provider it falls to depends on which one failed: if it was the organization's default, the next attempt goes to the fallback provider; if the fallback itself failed, the attempt goes back to the default. And if the provider that failed is neither one, the system still tries any other available provider before giving up — the conversation only goes unanswered if every configured provider is down at the same time, a rare scenario.

Each attempt is recorded in the organization's AI usage logs, with the provider that actually answered and whether a fallback happened — data that helps the manager understand whether a specific provider is unstable, without hunting for it in any external tool.

A dedicated model for reading the tone

Beyond generating conversation replies, SDRBOT.ai runs a sentiment analysis on every incoming message, using a model configured specifically for fast tasks of that kind, with the same fallback mechanism if that particular model fails. The result feeds the conversation history and helps identify unhappy leads before the situation gets worse.

That same AI layer takes part in detecting opt-out requests as a second check, alongside the explicit text rule — lowering the chance of continuing to message someone who already asked you to stop.

A conversation summary generated on demand

When an agent needs to get oriented fast in a long conversation, one click generates a summary written by the AI with the main points discussed, the lead's level of interest and suggested next steps — using the same AI engine with fallback, so it keeps working even if the main provider is unstable at that moment.

That summary is kept for a while after being generated, to avoid reprocessing the same conversation over and over, and it has a daily generation cap per organization — enough for everyday use without running up unnecessary AI cost. If the cached summary no longer reflects where the conversation stands, because several new messages arrived after it was generated, a refresh button forces a new one, discarding the previous version instead of leaving the agent reading an outdated picture of the deal.