Patient engagement · AI agent platform for patient access
Syllable
Syllable is a platform for building AI voice, text and chat agents, used by health systems to answer patient calls and schedule visits through Epic.
Strong fit
- Health system call centers that schedule through Epic
- IT teams that want to build and tune their own agents
- Buyers who want usage pricing with a free trial
Weak fit
- Small practices that want a ready-made front-desk agent
- Buyers who want a vendor focused only on healthcare
- Teams without staff to build and manage agents
Bottom line
Syllable is a solid choice for health systems that want to build their own AI phone and text agents, with Epic scheduling and usage pricing. It suits organizations with staff to design and manage agents. Small practices that want a ready-made front-desk agent should look at healthcare-only vendors.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Kobus Jooste, a former Google engineering director, co-founded Syllable in 2017 with Guilherme Bastos and leads it as chief executive. The company is based in San Jose, California.
Syllable began with voice and text assistants for health system call centers. Its agents answer patient calls, help patients find doctors, book appointments through Epic, and pass harder calls to staff.
It now sells a general platform for building AI agents across industries, with no-code tools for business teams and APIs for engineers. Customers can pick AI models from OpenAI, Anthropic or Google and run agents on several clouds.
Houston Methodist used Syllable for its COVID-19 vaccine hotline in early 2021, with a 91% automation rate across patient requests in the first month. Syllable says MemorialCare now routes every call to its navigation center to AI agents, and Virtua Health went live in three weeks.
TCV led a $40 million Series C in April 2022, with Oak HC/FT, Section 32 and Verily also investing. Syllable holds SOC 2 Type II and HITRUST e1 certifications.
Hyro fits when a health system wants a healthcare-only vendor for calls, chats and texts. Assort Health and Parakeet Health fit specialty groups, Hello Patient fits practices that want the agents run as a managed service, and Keona Health fits call centers that need nurse triage.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| Assort Health | 8.2 | 7.6 |
| Hyro | 7.9 | 6.4 |
| Keona Health | 7.4 | 7.7 |
| Hello Patient | 7.2 | 8.4 |
| Syllable | 7.2 | 7.3 |
| Parakeet Health | 6.9 | 7.0 |
Pricing
| Item | Detail |
|---|---|
| Model | Syllable charges metered usage for platform hosting, with third-party AI services passed through at cost, or custom enterprise pricing. |
| What usually drives cost | For Syllable, cost depends on call minutes, text volume, the AI models and voices chosen, and the support plan. |
| What to ask before you buy | Ask Syllable what a typical patient call costs with your chosen models, and what enterprise support and Epic integration add. |
| Note | Syllable's free trial caps each call at three minutes. |
| Published pricing | Syllable's pay-as-you-go plan starts with a $150 prepayment, and enterprise voice runs $0.06 a minute before AI model costs. Enterprise pricing is set by contract. |
Leadership
Methodology
| Weight | Factor | What it measures |
|---|---|---|
| 35% | Customer outcomes | Whether buyers get measurable operational or clinical-workflow results after go-live |
| 30% | Product | Capability depth, reliability, and fit for the job the category buys |
| 20% | Implementation | How hard it is to stand up, integrate, train, and stabilize |
| 15% | Pricing clarity | Whether a buyer can model total cost without a mystery quote |
| Label | Meaning |
|---|---|
| Highly recommend | Strong outcomes and product with manageable caveats |
| Recommend | Solid fit for the right buyer; know the tradeoffs |
| Conditional | Only with a specific use case or heavy caveats |
| Not recommended | Avoid for most buyers in this category |
Read our full methodology for how we weight scores and assign recommend labels.