Congrats on the launch. One complaint: RPA this, non-RPA that, but you never explain what it means. I would write down the acronym fully once at the first mention on the landing page.
Legacy system users are also the one who pays the most for tools and services. We sell to enterprise, I can attest to that. If it is relevant usecase and positioning for the market, it should be fine.
yeah it’s been interesting to watch, we were surprised initially at how much legacy users actually wanted to adopt AI - I think it’s because of how awful the old software can be to interact with
100% right - we support the AI companies who are selling to the legacy end users - for ex: we don’t sell directly to hospitals, but an AI scribe for doctors that already has a hospital as a customer, we help them integrate to the hospital’s EMR
Yeah that’s true - in those cases we’ve either worked with their outsourced IT provider to spin up VMs for us or have had to spin up our own VM and connect through a VPN - IT can be very fun…
Small website nitpick: I feel like the "In production with" section's companies logos should be a bit darker, I could barely tell there was something there.
54% of Americans read below a 6th grade level. People word-mash "alot", "atleast", and now "eachother" all the time. Sports commentators use verse when they mean versus.
I'm not suggesting that you correct your customers, but there's no reason to sink to the lowest common denominator when writing.
i'm curious: how does the steady state error rate of a stochastic automated system like this compare with the downtime and errors that come from a (brittle) deterministic bridge that can fail with upgrades? what does the observability look like? (i'm guessing one feature is that the execution log including images/screenshots for each transaction gets saved, which is probably a huge improvement.)
it’s a good q - we experimented a lot with computer use / agentic automation and found that at scale a hybrid solution where the automations run as deterministic code with agents for recovery is the best - running automations as code is faster & cheaper & when you’re doing critical tasks (like updating patient records) you don’t want an agent to potentially mess something up.
previously writing RPA code used to take a long time - using AI (and its infinite patience) we can write more durable code that covers more edge cases
And since they’re code based it’s pretty straightforward to an agents monitor them and update their code when upgrades to the underlying system happen etc…
for observability - we have workflow execution logs that store text, videos and screenshots so an agent or a human can debug them - lots and lots of webhooks when things break ! (:
Does this only revert back to LLM Vision when it catches an error? I.e once the RPA / workflow is built once, it’s efficient for running multiple times (until it catches an error state)?
yes effectively, but we use LLM vision in multiple places - for context, there are multiple ways an RPA can fail:
1. RPA code breaks (ex: throws an exception if a window does not exist)
2. RPA reports success but was clicking / typing in the wrong place
3. Underlying system breaks (virtual machine / legacy software)
the skill we have in our MCP is to build the RPA code to throw exceptions where possible so an LLM can understand the context and recover
to avoid false success states we add LLM vision steps in the workflow itself to error out if it sees that the system is in the wrong state
and for the underlying system breaking it can be as simple as having a CRON job that checks the status of the process / the health of the VM and running a script to reboot the system
it depends on the system but the pattern we've seen with RPAs is you can catch maybe 80% of the edge cases in the first week it's been rolled out
We think CyberDesk is great - our main difference is I believe the primary driver for their automations is computer use / agent based - whereas our automations use agents to create/maintain/support python code
Is the cloud LLM the judge based on screenshots with patient/customer data included ? That seems like a no-go for many countries given privacy concerns ?
No patient/customer data included in the screenshots - in production we’d basically find some “region” of the screen to screenshot that would help an LLM say yes/no to - ex: “is the nav bar green and does it say Insert Note”
Biggest question is how much of this can be stored / processed on our own infra and with our own lifecycle rules? For example, this can touch a lot of PHI. Screenshots, videos, JSON inputs/outputs etc.
mingabunga | 3 hours ago
[OP] fchishtie | 2 hours ago
Boxxed | 3 hours ago
[OP] fchishtie | 2 hours ago
saheed_laminar | 2 hours ago
polonbike | 2 hours ago
[OP] fchishtie | 2 hours ago
theaniketmaurya | an hour ago
debarshri | an hour ago
[OP] fchishtie | an hour ago
[OP] fchishtie | an hour ago
throw03172019 | an hour ago
[OP] fchishtie | an hour ago
throw03172019 | an hour ago
[OP] fchishtie | an hour ago
throw03172019 | an hour ago
[OP] fchishtie | an hour ago
dragonsenseiguy | an hour ago
[OP] fchishtie | an hour ago
snozolli | 57 minutes ago
I think you meant premises.
https://brians.wsu.edu/2016/05/30/premise-premises/
[OP] fchishtie | 54 minutes ago
throw03172019 | 52 minutes ago
snozolli | 28 minutes ago
I'm not suggesting that you correct your customers, but there's no reason to sink to the lowest common denominator when writing.
a-dub | 56 minutes ago
[OP] fchishtie | 35 minutes ago
previously writing RPA code used to take a long time - using AI (and its infinite patience) we can write more durable code that covers more edge cases
And since they’re code based it’s pretty straightforward to an agents monitor them and update their code when upgrades to the underlying system happen etc…
for observability - we have workflow execution logs that store text, videos and screenshots so an agent or a human can debug them - lots and lots of webhooks when things break ! (:
throw03172019 | 41 minutes ago
[OP] fchishtie | 25 minutes ago
1. RPA code breaks (ex: throws an exception if a window does not exist) 2. RPA reports success but was clicking / typing in the wrong place 3. Underlying system breaks (virtual machine / legacy software)
the skill we have in our MCP is to build the RPA code to throw exceptions where possible so an LLM can understand the context and recover
to avoid false success states we add LLM vision steps in the workflow itself to error out if it sees that the system is in the wrong state
and for the underlying system breaking it can be as simple as having a CRON job that checks the status of the process / the health of the VM and running a script to reboot the system
it depends on the system but the pattern we've seen with RPAs is you can catch maybe 80% of the edge cases in the first week it's been rolled out
throw03172019 | 21 minutes ago
[OP] fchishtie | 14 minutes ago
ilundin | 18 minutes ago
[OP] fchishtie | 11 minutes ago
throw03172019 | 8 minutes ago