Pretty Good AI vs Solv (Maya)

A competitor with a real voice agent

Several of the platforms an athenaOne practice evaluates against us do not answer the phone. Solv does. Maya is a genuine 24/7 voice agent that books into the EHR, takes payment on the line and speaks more than 80 languages, and Solv publishes production numbers most vendors would not: more than a million patient calls handled, an average of just over two minutes per call, an average of $86 collected by phone, and a 5x to 8x lift in call-to-booking conversion across three partners.

We are not going to pretend that is not real. It is more published voice volume than we claim. So the comparison has to be about something other than whose voice is better.

The difference is the setting, not the technology

Solv is built for one care setting. Its navigation offers two segments, urgent care and health systems, and the product, from the consumer marketplace to the case-study library, is tuned to walk-in, single-service-line, high-concurrency clinics. That is a coherent product, and if that is your business it is worth a serious look.

A multi-specialty athenaOne practice runs on different inputs: provider-specific scheduling rules, visit types driven by orders as well as walk-ins, referral workflows with acceptance criteria, a tickler queue and a payer-specific prior-auth backlog. How much of that shows up in an urgent-care call pattern varies by clinic; confirm with Solv which of these Maya handles before assuming.

Where Solv genuinely beats us

Demand. Solv operates a step we do not: it brings patients to you. It runs a consumer marketplace, an app with a large review base, pushes clinics into Google and Apple Maps booking, and in July 2026 published an integration making it the in-person urgent care booking service inside Amazon Health AI.

That is consumer-marketplace discovery, which is different from converting a practice’s own inbound demand and new referrals. Our scope is the practice you already have, and panel growth comes through referral capture and reactivation rather than through a consumer channel. For a walk-in clinic, Solv’s demand side may simply be worth more than any efficiency gain.

Where we go that Solv stops

Solv’s published scope stops at the clinic door: no claims, no denials, no A/R, no back office. Our product runs the full operational path, front to back:

• Patient discovery and acquisition. Self-referral web forms, a provider referral portal, fax and email referral intake, referral status tracking, new-patient registration.
• Getting them booked and into the room. Inbound voice 24/7, smart routing, scheduling and rescheduling, waitlist backfill, no-show recovery, lapsed-patient reactivation, tickler and order outreach, medication refills, and the full administrative call in 10 languages.
• Everything that decides whether you get paid. Eligibility and insurance verification, prior-auth status and preparation, denials and appeals, patient balance collection, payment links and plans.
• Proving it worked. Outcome calls, KPI dashboards, per-call QA scoring, patient surveys and reviews, a call review console.

That back half is where an athenaOne practice loses the most staff hours, and it is not on Solv’s roadmap because it is not their buyer’s problem.

The marketplace record, plainly

Solv joined the athenahealth Marketplace in February 2018 and has 135 practice connections and six lifetime reviews, the most recent from November 2024. Solv’s own site now leads with Maya, so the voice repositioning is real and recent, but athenahealth is one of eight EHRs on that listing.

We are an athenahealth Marketplace partner since 2025, with 45 practice connections and 22 ratings, all five-star, as of October 2026. Fewer connections, all of them athenaOne.

What we would tell you to check

Ask Solv what happens when Maya meets an athenaOne appointment type it does not recognize, and ask what percentage of their athenaOne customers are multi-specialty rather than urgent care. Ask us for a call that ends with athenaOne updated and no task left for staff, and ask what our failure path looks like when the AI is not sure. Both answers are more informative than any feature grid.