Promptra Team's two-step API combines Gemini 3.1 Pro for enhanced transcription

This review examines Promptra Team's API for audio transcription, which combines Gemini 3.1 Pro for ASR with an LLM for post-processing, focusing on its architecture and ruble pricing.

The Answer Up Front

Promptra Team's API is a compelling option for developers and businesses in the Russian market seeking a structured approach to audio transcription. It is particularly well-suited for those who need more than raw speech-to-text, valuing an integrated post-processing step for cleaning, summarization, and meeting protocol generation. The transparent, ruble-denominated pricing, directly mirroring provider costs, removes currency friction for local users. However, if your workflow demands direct control over the specific LLM used for post-processing or requires independent performance benchmarks before integration, you should proceed with caution. The bottom line: Promptra Team offers a thoughtfully engineered transcription pipeline, but its performance claims are currently unverified.

Methodology

This v0 review draws on the founder's published claims at dev.to, specifically the article "Нейросеть для транскрибации: расшифровка аудио в текст" (AI for Transcription: Audio to Text), published on 2026-05-29. The review covers Promptra Team's described two-step transcription architecture, its use of Google's Gemini 3.1 Pro Preview for Automatic Speech Recognition (ASR), the subsequent LLM-based post-processing, and the stated OpenAI-compatible API with ruble pricing. This review does not include independent performance benchmarks, long-term workflow integration analysis, or edge-case testing. Update cadence: re-tested when claims diverge from observed behavior or when independent benchmarks become available.

What It Does

Promptra Team offers an API designed for comprehensive audio-to-text transcription, moving beyond simple speech recognition. The service is structured around a two-step pipeline, accessible through a single, OpenAI-compatible API endpoint.

Two-step transcription pipeline

The core of Promptra Team's offering is a two-stage process. The first step, speech recognition (ASR), converts audio input into raw text. As of 2026-05-29, this is handled by Google's Gemini 3.1 Pro Preview, a multimodal model capable of directly accepting audio. The founder reports that this stage focuses on accurately transcribing spoken words and basic punctuation. The second step, text post-processing, takes the raw transcription and refines it using a large language model. This stage aims to remove filler words, correct repetitions, segment text into paragraphs and speaker turns, summarize content, extract key theses, and even generate meeting protocols with identified tasks and decisions. This separation allows for specialized models at each stage, optimizing for both accurate speech recognition and intelligent text refinement.

OpenAI-compatible API

The entire transcription and post-processing workflow is exposed via an OpenAI-compatible API. This design choice significantly lowers the barrier to entry for developers already familiar with OpenAI's API structure, allowing for quick integration into existing applications and workflows. The founder emphasizes that the API handles the orchestration between the ASR model and the post-processing LLM, presenting a unified interface to the user.

Ruble-denominated pricing

A key feature for its target market is the pricing model. Promptra Team states that its services are billed in rubles, directly reflecting the underlying provider costs at the Central Bank of Russia (ЦБ) exchange rate. The founder claims there is no markup on token usage, aiming for transparent and predictable costs for users operating within the Russian economic sphere. On 2026-05-27, the stated exchange rate was 71.668 ₽/$. This localized billing strategy addresses a common pain point for developers and businesses in regions with specific currency and payment infrastructure.

What's Interesting / What's Not

The explicit two-step architecture is a sound engineering decision. Separating ASR from LLM post-processing allows for specialized optimization at each stage. Using Gemini 3.1 Pro Preview for the initial ASR is a notable choice, indicating a commitment to leveraging a high-fidelity, multimodal model for foundational accuracy. Many transcription services offer raw output or basic cleaning; Promptra Team's integrated summarization and meeting protocol generation, all through a single API, is a valuable differentiator for specific use cases like corporate meetings or podcast production. The OpenAI API compatibility is a pragmatic move, reducing developer friction and accelerating adoption for teams already using similar tooling.

What is less clear, however, is the specific LLM used for the post-processing step. The founder states

The investor read

Promptra Team's approach signals a growing demand for specialized AI wrappers that abstract away complexity and localize billing for specific markets. The use of a high-end foundational model like Gemini 3.1 Pro for ASR, combined with a generic LLM for post-processing, suggests a focus on convenience and integration over proprietary model development. This strategy can be capital-efficient, but it also means the core IP is in the orchestration and market access, not the underlying AI. Comparable tools include direct API access to Google's or OpenAI's transcription services, potentially combined with custom LLM prompts. For Promptra Team to be investable, it would need to demonstrate significant market penetration in the CIS region, robust customer acquisition, and a clear path to expanding beyond a simple wrapper, perhaps through proprietary post-processing enhancements or specialized domain expertise. Otherwise, it functions as a deliberate small/bootstrapped play focused on a niche market need.