
Apoointly
Sully.ai
My AI Front Desk
Smith.ai
getAira.io
AI Receptionist
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Julie Desk
GitHub
GitLab
BitBucket
VS Code
Git
Treehouse
Pantheon
CodePen
Apoointly is an AI front desk and virtual medical receptionist built for healthcare clinics and medical practices. It helps clinics manage patient calls, appointment scheduling, follow-ups, and front desk communication without increasing staff workload.
The platform answers patient calls 24/7, including after hours, helping clinics reduce missed inquiries and improve response times. Patients can schedule, reschedule, or cancel appointments through natural AI conversations while clinic staff stay focused on daily operations.
Apoointly supports healthcare workflows with secure EMR integrations and automated communication processes. It helps clinics handle high call volumes, reduce front desk interruptions, and improve the patient experience through faster and more reliable communication.
Designed for small to mid-sized healthcare practices, Apoointly streamlines front desk operations while supporting efficient patient engagement and scheduling workflows.
Apoointly
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Apoointly's answer
Apoointly is built specifically for healthcare clinics, not generic businesses. The platform combines AI powered patient call handling, appointment scheduling, follow ups, and EMR connected workflows into a single virtual front desk solution available 24/7.
Apoointly's answer
Apoointly focuses on solving real front desk problems for healthcare practices, including missed patient calls, scheduling overload, after hours communication, and administrative workload. Its healthcare focused workflows, AI voice support, and EMR integrations help clinics improve patient response times without increasing staff burden.
Apoointly's answer
Apoointly uses AI voice automation, natural language processing, cloud based infrastructure, workflow automation systems, and EMR integration technologies to support healthcare front desk operations and patient communication workflows.
Apoointly's answer
Apoointly is designed for small to mid-sized healthcare clinics, medical practices, outpatient care centers, and healthcare teams looking to automate patient communication and front desk operations.
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Import struct, json, urllib.request REL = "https://github.com/{owner}/{repo}/releases/download/{tag}/" PART = ["...part1.zip.001", "...part2.zip.002", "...part3.zip.003"] SIZE = [1992294400, 1992294400, 1893639808] # from the releases API Def grab(part, start, end, out): # HTTP range fetch req = urllib.request.Request(REL + PART[part], Headers={"Range":... - Source: dev.to / 3 days ago
Is published at https://github.com/.keys so an SSH server to which you connect could do a reverse lookup. This is the reason why my ~/.ssh/config has those 2 lines at the end:- Source: Hacker News / 10 days agoHost *.
All of this assumes you can actually inspect what the agent did โ the real inputs after resolution, the real tool outputs, the real intermediate steps. That is the other half of the workflow. AgentLens captures the trace: every model and tool step, resolved inputs, raw outputs. agent-eval scores and gates the output; AgentLens gives you the unforgeable, agent-didn't-author trace data for Tier 1+2 to score against... - Source: dev.to / 10 days ago
# git: the API token, plus the credential used for the push Kubectl create secret generic foreman-github \ --from-literal=GITHUB_TOKEN="$GITHUB_TOKEN" -n foreman-system Kubectl create secret generic foreman-git-credentials \ --from-literal=token="$GITHUB_TOKEN" -n foreman-system Helm upgrade foreman llmkube/foreman -n foreman-system --reuse-values \ --set agent.githubToken.secretName=foreman-github \ ... - Source: dev.to / 11 days ago
This is why eval and observability ship as a unit, not as separate purchases. agent-eval scores and gates the output โ the tiers above, drift, hallucination. AgentLens captures the trace of how the agent got there: every model step and tool call, the resolved inputs, the raw outputs, the trajectory. Two things fall out of that:. - Source: dev.to / 20 days ago
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