What problem does Tippiti solve?

Domain knowledge instead of standard logic

The quality of a result depends on how well the AI understands the domain context: which phrasings are correct, which errors in the dictation must be fixed, how dictation commands are carried out, what belongs in the finished text and what does not.

Tippiti works with rules developed jointly with medico-legal experts and transcription services and continuously improved. This means the output already fits most use cases without further configuration. Anyone who has specific requirements on top of that can add their own rules.

Built-in quality control

Tippiti does not work with a single AI pass. In the agentic workflow, several AI models work together with their own roles for correction[TOOLTIP] Correction at Tippiti means resolving and fixing transcription and dictation errors. [/TOOLTIP], fact-check[TOOLTIP] Fact-check at Tippiti means verifying clearly checkable details in the dictation – proper names, medication names, ICD codes, case numbers, place and postcode combinations, etc. A clinical evaluation of diagnoses or findings is not part of the check. [/TOOLTIP] and plausibility-check[TOOLTIP] Plausibility-check means verifying clearly logical errors, to further reduce transcription and dictation errors. [/TOOLTIP]. An independent AI validator[TOOLTIP] A separate verification stage by another model that checks the result against defined error classes – spellings, place/postcode combinations, ICD plausibility, contradictions between sentence parts, etc.

Verified is the consistency of the text, to further reduce transcription errors, not the clinical correctness of diagnoses or assessments. [/TOOLTIP] compares the result with the original dictation and verifies that the applicable rules were followed and content was carried over correctly. If discrepancies are found, it triggers another correction round, until the result is right.

Part of this workflow is an active fact-check and plausibility-check of medication names, active ingredients, dosages, ICD codes, case numbers, proper names, place names, etc. Are spellings correct, are codes valid, do postcode and place name match, are dosages within a realistic range? If in doubt, a web search[TOOLTIP] If the model knowledge is incomplete – new medications, rare proper names, etc. – individual factual terms are checked against external references, without further dictation context. This way, clearly checkable details that the model knowledge alone does not reliably provide are also verified.

Searches use only isolated factual terms – patient names, diagnostic references, dictation content or other sensitive data are not transmitted (see FAQ). [/TOOLTIP] complements the check. Logical contradictions in the text are also detected. That goes beyond plain transcription correction.

If discrepancies are found, a targeted re-correction and re-check is run. This cycle of correction, validation and re-correction continues until the result meets the requirements – several AI models in their own roles, with their interplay improving the result step by step.

Unlike generative AI tools that author content of their own, Tippiti does not produce new medical findings, diagnoses or assessments from its own assumptions. Tippiti corrects, structures and validates what was dictated, and checks facts – the clinical statements come from you. The finished result is available as a Word document, in the integrated editor or via the API. How this is covered legally – DPA, German § 203 StGB, no training with your data – is described on the Privacy page.

Adaptable where it needs to be

The base rules cover most requirements. But medico-legal experts and transcription services often have specific conventions: particular abbreviations, date formats, ICD code notations, formatting rules, etc.

To cover every use case, Tippiti supports instruction sets and text replacements. The instruction set can be set individually for each dictation at upload time.

Quality comes from the details

Send an audio file to a model, and you get text. For a usable result, that is often not enough. To deliver a high-quality output automatically, many technical decisions are needed: audio optimisation, sensible segmentation at pauses, context control between sections, handling of long texts, error handling for problematic passages.

On top of that come the edge cases and the quirks of each AI model, which have to be handled accordingly. All of these details determine whether you end up with just a result, or with a result you can actually work with.