AI & Machine Learning

A stage-by-stage voice AI latency budget: endpointing, STT, LLM, TTS, transport and telephony, where the extra second hides, and how to claw it back.

Looking for a Vapi or Retell alternative? Signals it’s time to leave, what owning the voice stack means layer by layer, and how to migrate safely.

A CTO’s plan for moving AI from pilots to production: readiness checks per workflow, wave-based sequencing with exit gates, legacy options and governance.

A decision guide for teams that already run RPA: which bots to migrate, which to keep, the real maintenance tradeoff, and how to sequence it.

What an AI agent costs to build and run in 2026: effort by stage, monthly model and tooling costs, a worked 10,000-task example and the costs estimates miss.

Multi-agent LLM systems fail for predictable reasons — shared state, runaway loops, cost blowups. What actually breaks, and how to design around it.

Voice AI agent development companies compared for 2026: platforms (Vapi, Retell, LiveKit) vs integrators, telephony, compliance and named proof.

How to set up a WebRTC data channel, choose a reliability mode, keep it in sync with video, and handle backpressure — spec-grounded, not vendor blogs.

Pipecat vs LiveKit Agents: the real architectural difference — transport-agnostic pipeline vs bundled LiveKit stack — and how to choose.

How to add live captions and real-time transcription to a video platform: streaming vs batch, vendor patterns, accuracy trade-offs, and accessibility.

“AI video enhancement” in a WebRTC product almost always means one of three things: noise suppression (cleaning up what a participant’s microphone picks up), background removal or replacement (blurring or swapping what’s behind them), and super-resolution (upscaling a low-bitrate or low-resolution video track). They get sold as one feature set. They are not one engineering decision. Noise suppression and basic background […]

Adding real-time speech-to-speech translation to a live video platform comes down to one architecture decision: cascaded — speech-to-text (STT) → machine translation (MT) → text-to-speech (TTS), three separate models chained together — or end-to-end speech-to-speech (S2S), a single model that takes audio in one language and emits translated audio, with no intermediate text step. Get it wrong and […]

A real-time AI development company builds AI systems that run inside a live, low-latency media stream — a voice agent that holds a phone call, AI analysis of a live video feed, on-the-fly translation of a video consultation — as opposed to AI that processes data asynchronously, like a chatbot answering typed text, an offline document pipeline, […]

AI document processing for FinTech is the use of custom OCR, natural language processing, and machine learning to turn unstructured financial documents — bank statements, leasing contracts, loan applications, KYC files — into structured, decision-grade data that underwriting, risk, and compliance teams can act on with confidence. It goes further than generic OCR, which reads […]

Running a voice AI platform in production means paying for five things every call touches: the WebRTC/SFU transport layer that carries the audio, speech-to-text (STT), the LLM that decides what to say, text-to-speech (TTS), and the hosting, bandwidth, and observability underneath all of it. Most public cost breakdowns only price the AI components and quietly […]

By Stan Reshetnyk, CTO, Trembit — HIPAA/KBV-compliant telemedicine architecture and real-time AI integration · Last updated 15 July 2026 An AI clinical scribe is ambient software that listens to a clinician–patient conversation during a visit and drafts a structured clinical note — a SOAP note, an assessment, a problem list — instead of just a […]

A voice AI agent on WebRTC runs inside one live, two-way media session. The user’s microphone streams up as an audio track, the agent’s synthesized speech streams back as another, and control events travel alongside. Between the two tracks sits either a cascaded STT → LLM → TTS chain or a native speech-to-speech model. Whether […]

An honest look at what AI agents reliably automate for businesses today — and where the hype outruns reality. No vendor spin.

A step-by-step playbook for building an AI admin co-worker: automate back-office tasks with human-in-the-loop checkpoints, guardrails, and audit trails.

By the Trembit Engineering Team · Last updated: 2026-07-08 An AI orchestration engine is the production layer that sits between your application and its models. It routes each request to the right model, handles fallback and retries when one fails, manages the RAG and context pipeline, coordinates multi-step and multi-agent workflows, enforces guardrails, and gives […]