engomez

Both work - Agents can be triggered via API just like any normal process, so they will go do the work async and post the result via e.g. an MCP of your choice to Slack, backend forms, etc. We don't have a built-in human-in-loop orchestration layer just yet, but since each execution has a conversation thread, you could orchestrate a way for your human-in-loop process to simply submit a new message with whatever was sent. Basically using the messaging as the event queue system.

That said, yes, we deal with a lot of customer support use cases so conversational experiences were a top priority for us. It's nice to be able to interact with an agent as a workflow or conversationally.

We measure the opposite now - % of questions where the bot was not able to find documentation to answer and it doesn't exist. This powers our insights reporting. We generally don't hear complaints about false positives here (i.e., cases where there were docs), so anecdotally, I'd guess 95%+ else our reporting wouldn't be very useful.

appreciate it!

best way to frame the customer-facing AI: guided tutorials on-demand that can translate between user terminology and product lingo.

appreciate it!

Ah understood. I can see how the term is a little fuzzy. Glad it highlighted how the product works though.

this one is designed for support agents - not to be customer facing. Whole goal is to make it more scalable to provide high-touch human-based support for teams who are keen on that.

Agree customer-facing AI has to be done in a tasteful and mindful way.

that's the goal. And now with Keep the goal is to help the support team answer those questions that come through faster too. Minimizing "time to answer" across the funnel is the goal.

glad to hear!

Have been a Cursor user for ~1yr. I think they just nailed the key workflows people wanted - i.e. the fully conversational side bar with "apply" buttons and easily being able to attach the right files or code snippets you want.

VS Code may have caught up now, but haven't looked back.

give the playground a shot and let me know what you think.

we answer 250k+ customer-facing questions/mo today for teams who really care about quality (Anthropic, Clerk, Pinecone, Postman) - we're brining that same care and high bar to our copilot for support teams.

the generative UI and conversational aspect is quite different than other copilots we've seen.

ty!

nothing like closing the content loop! try to make that direct.

My understanding is that Q is a general purpose internal ai/search service - similar to Glean or Microsoft's equivalents.

Our tool focuses on support use cases (customer-facing or internal-facing), which means we can go deep with workflows like detecting gaps in your documentation and focusing our efforts on quality around these scenarios. Generally our support copilot intro'd here also generates dynamic UIs so goes beyond a normal chat interface.

Take a look at https://docs.inkeep.com/ai-api/openai-chat-completion-endpoi...

tl;dr we define a JSON schema with a few semantic labels that represent a gradient in confidence. On our end we prompt it with certain examples and guidance for when to use each label. This is generally a better approach than e.g. asking an LLM to give a numeric score.

We also have trained embedding-based classifiers as non-LLM heuristics.

was this in the support copilot or our public-facing bot on our landing page? for my fyi what did you mean by 'failures' of the service, can look to create some content relating to that.

Not too bad - $3k.