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// Industry

Real-time AI for telemedicine

Video visits that connect the first time and stay up, and clinical answers that cite their source — the two places a telehealth product actually fails.

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// The problem

Why this is hard

A consult that fails to connect is not a bug report. It is a missed appointment with a clinician's time already spent, and the failures cluster in specific, findable places: a codec profile a desktop browser accepts and an iPhone renders as a black rectangle, audio that degrades past intelligibility long before the video looks bad, a reconnect that never fires.

Alongside it sits the other risk — an answer that sounds right and is not supported by anything. Both problems have the same discipline behind them: measure it end to end, and prove the result instead of assuming it.

// What matters here

The capabilities that move the needle

Real-Time AI Audit

Where a visit actually fails — connection setup, codec negotiation, the reconnect path — measured end to end on your stack, with a costed fix list you can act on with or without us.

RAG & Knowledge Systems

Answers composed only from your clinical corpus, with citations back to the passage and an eval harness that scores faithfulness on every prompt and model change.

Voice Agents in Production

Intake and follow-up calls where the latency budget counts the phone leg and turn-taking does not talk over a patient, with replay evals over a golden set before a regression reaches anyone.

Compliance-Grade Recording

Consent captured and provable, redaction on the live pipeline, retention that expires on schedule, and an audit log that reconstructs who heard what.

// Deep dives

Going deeper for telemedicine

// Proof

Representative outcome

Healthtech — Clinical RAG

Illustrative — an anonymised, representative engagement; figures are indicative, not a verified client metric.

hallucinated citations in eval

0

on the citation eval set

source-linked answers

100%

every shipped answer

Read the case study

// FAQ

Common questions

Almost always a codec profile or level the iOS decoder will not accept. Safari is strict where Chrome is permissive, so a stream that plays everywhere else renders as a black rectangle on an iPhone with no error anywhere to explain it. It is diagnosable from the negotiated SDP and the bitstream itself, and it is fixable at the encoder rather than in the app.

Answers are composed only from retrieved sources with citations, and an eval suite scores faithfulness on every change — an unsupported answer fails the gate rather than reaching a clinician. Ambiguous cases escalate to a person with the retrieved context attached.

That is a separate engagement from the video itself, and it is the right way to think about it: consent captured and provable, redaction applied on the live pipeline rather than in a batch job afterwards, retention schedules that actually expire, and an immutable log of who accessed what. Storage is the easy part; the obligations are the work.

// Related

All industries

// Let's build

Building AI for telemedicine?

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