Synthpop

Synthpop automates healthcare voice QA agentically with Hamming

The proactive discovery is probably the number one most useful thing. We want issues to be raised either internally with our existing analytics and fallback mechanisms or through Hamming.

Jyoti Rani, AI Engineer at Synthpop

Synthpop logo
Company Logo
Location
Cambridge, MA
Industry
Healthcare AI
Funding
Series A ($23M total)

Use Cases:

  • Self-sustaining test suites grounded in real call data
  • Agentic MCP-driven test infrastructure setup
  • Cross-account insurance, patient, and clinic flow validation

Meet Synthpop

Synthpop is a patient journey orchestration platform that automates referral intake, insurance verification, eligibility, and patient communication for healthcare organizations. The company unifies document intelligence, payer-aware reasoning, and conversational voice agents into one coordinated system that automates large portions of healthcare back-office processes.

www.synthpop.ai

Synthpop runs voice agents across the full patient journey: inbound and outbound, patient and clinic, and payer. The team has automated agent provisioning, account onboarding, and call flow extension and built an internal layer of analytics, fallback mechanisms, and self-healing systems that remediate call flow issues automatically. As volume ramped across accounts, Synthpop chose to extend that automation philosophy to QA, with Hamming as the proactive-discovery and agentic test infrastructure layer.

With Hamming, Synthpop was able to

Proactive discovery of failure modes before they become customer-visible incidents
A self-sustaining test suite grounded in real production failure data
Agentic test infrastructure setup driven by Hamming's MCP server
Cross-account learning so new flows inherit institutional knowledge

The Challenge: Extending Synthpop's Automation Philosophy to QA

Synthpop's voice agents handle some of the hardest call flows in healthcare: insurance verification with multi-party transfers, eligibility checks, referral intake, and patient communication that spans many touchpoints over weeks. A single failure mode buried in a payer interaction could mean a missed order, a delayed appointment, or a patient who disengages.

On the agent side, Synthpop had already pushed automation hard. New accounts come online programmatically. Call flow types are extended without weeks of manual setup. Internal analytics and fallback mechanisms catch a class of issues automatically, and Synthpop's self-healing systems remediate call-flow problems on the fly. Customer success teams read through call analytics for the patterns those systems can't yet see.

QA was the next layer to bring up to that standard. The question wasn't whether existing analytics worked. It was whether Synthpop could add a proactive-discovery layer that surfaced systemic patterns earlier, ground its test suite in real data, and let coding agents stand up new test infrastructure as fast as new accounts came online.

Synthpop healthcare voice agent QA workflow showing patient journey orchestration across insurance, eligibility, and patient communication flows

Before and After Hamming

Failure mode discovery
Internal analytics plus call-by-call review
Layered on with proactive Hamming patterns
Test suite source
Curated scenarios per flow
Self-sustaining suite
Test infrastructure setup
Configured per account by engineers
Stood up agentically via Hamming's MCP
Cross-flow knowledge transfer
Coverage rediscovered for each new flow
Designing skill files and playbooks so institutional knowledge transfers across flows
Customer success QA load
Manual analytics review during volume ramps
Hamming surfaces clusters of recurring issues automatically

How Hamming Helps Synthpop Run Voice QA at Scale

01

Proactive Failure-Mode Discovery

Synthpop already runs internal analytics, fallback mechanisms, and self-healing systems that catch and remediate call flow issues automatically. Hamming layers proactive systemic discovery on top: it surfaces patterns in calls that point engineering directly at the highest-impact issue before customer success has to spot it call by call.

The most useful discoveries are the ones that name the failure mode in business terms. As Jyoti put it: “X percent of calls are failing in insurance because we're getting stuck at this piece of the call, either because both parties didn't have a particular context that needs to be provided early going in.” That phrasing is actionable for both engineering and customer success.

Learn more: Guide to AI Voice Agent Quality Assurance →

Proactive failure-mode discovery dashboard
02

Agentic Test Infrastructure via MCP

Synthpop is an aggressively agent-native team, and Hamming's MCP server extends that same automation curve to QA - coding agents drive the setup of mirror agents, evals, and test profiles per account instead of engineers wiring them up by hand.

As Jyoti described it: “How do we stand up the mirror Hamming agents and all of the infrastructure so that not only are we saving time for our customer success folks, but this config is not scaling linearly with engineering as well?” The MCP integration is the answer.

Learn more: HIPAA & Clinical Workflow Testing Checklist →

Agentic MCP-driven test infrastructure setup

Why Synthpop Chose Hamming

Synthpop is an agent-native team. Agent provisioning, account onboarding, call flow extension, and self-healing remediation already run programmatically. The bar for any QA platform was whether it could match that pattern instead of becoming the one manual workstream that scaled with engineering headcount instead of accounts. Hamming was the only platform that did.

The relationship reinforced the choice. Direct engagement with the Hamming team, including DM-level access during onboarding, meant feedback turned into shipped features quickly. As Synthpop pushes for OpenTelemetry-backed root cause analysis and cross-market flow expansion, having a partner that moves at that speed has been as important as the product itself.

The Results

The clearest results show up in two places: how Synthpop's customer success teams operate day-to-day and what happens when an account ramps volume. Both are moments where manual QA used to strain. Both now have Hamming as the load-bearing layer.

01

Customer Success Behavior Shifted

Jyoti credits Hamming with driving some behavior change on Synthpop's customer success teams. Where teams previously had to comb through analytics and individual calls to spot recurring patterns, Hamming surfaces some of those patterns directly. Less time hunting, more time on remediation and client communication.

02

Discoveries Land in Business Terms

The most useful Hamming reports land in business terms that customer success can act on directly. “X percent of calls are failing because we're getting stuck at this piece of the call” is the framing that pays off. Specific enough to fix. Clear enough for customer success to walk through with a client. Bringing that same clarity to engineering via OpenTelemetry forwarding is the next milestone.

“The highest utility comes when you're increasing test call volume on an account - that's when it becomes unsustainable. We want one source that proactively raises issues, and Hamming is one of those sources. With the MCP, we can stand up mirror Hamming agents and all of the infrastructure, so this config isn't scaling linearly with engineering as well.”

Jyoti Rani

AI Engineer at Synthpop

What's Next

Synthpop is wiring OpenTelemetry data through to Hamming so that traces, tool calls, and reads and writes flow alongside failure-mode reports. The goal is to close the loop between “something is wrong” and “here is exactly which function and which downstream system caused it,” so engineering can move directly from a Hamming failure-mode pattern to the underlying root cause without stitching together five different dashboards.

The team is also extending the agentic playbook across Synthpop's growing footprint. As new flows pilot in non-related markets, the test suite is structured so institutional knowledge about payer behavior, IVR shape, and patient journey patterns transfers across flows. Each new rollout inherits the testing posture of mature ones, instead of restarting from scratch.

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