How speech-to-text works
How spoken words become text a machine can read, in real time and with the names spelled right.
Read the explainer →How the technology actually works.
A plain-language tour of the ideas behind a voice you can talk to, with a link to the source paper behind each one, so you can go as deep as you like.
Eight threads behind the work.
Each thread groups the explainers by the question it answers. Start with whichever one you are curious about.
Speech & audio
Hearing and speaking
Turning speech into text, and text back into a natural voice.
7 explainers → Retrieval & knowledgeFinding the right facts
Pulling the right facts across many sources, and mapping how they connect.
6 explainers → ConversationTaking turns
The timing and turn-taking that make a spoken exchange feel natural.
6 explainers → Latency & systemsAnswering back fast
The engineering that lets a voice reply without the wait.
6 explainers → Security & infrastructureKeeping work isolated
How sensitive work stays walled off while it runs.
10 explainers → Data & recordsOne clean record
Spotting duplicates, filling gaps, and turning messy records into one clean truth.
12 explainers → Pipelines & syncMoving data, kept in sync
Storing, processing, and two-way syncing data across email, calendar, CRM, and tasks.
10 explainers → Models & trainingHow the models work
The architectures and training ideas behind modern language and speech models.
10 explainers →Explore the research.
Find an idea by topic, or search for a question, technique, or tool. Each explainer includes diagrams and a reading list of the original research.
67 explainers
RAG over CRM, email, and multiple sources
How a model pulls the right facts from a CRM, an inbox, and other sources before it answers.
Read the explainer →Turn detection in voice agents
How a voice agent knows when you have finished speaking, and when it is the agent's turn to talk.
Read the explainer →Ultra-low-latency voice pipelines
What it takes for a voice to answer back fast enough to feel like a real conversation.
Read the explainer →Secure enclaves and confidential computing
How confidential computing keeps sensitive work isolated while it runs.
Read the explainer →Knowledge and connection graphs
How people, accounts, and conversations connect into a map a machine can follow.
Read the explainer →How text-to-speech works
How written words become a voice that sounds natural enough to listen to.
Read the explainer →Wake words and keyword spotting
How a device listens for one short phrase, all the time, on almost no power, and almost never gets it wrong.
Read the explainer →Speaker diarization: who spoke when
How a system tells voices apart and labels who said which line.
Read the explainer →Noise suppression and echo cancellation
How background noise and echo get stripped away so the words come through clean.
Read the explainer →Emotion and prosody in speech
How tone, pace, and stress in a voice carry meaning beyond the words.
Read the explainer →Real-time speech translation
How spoken words in one language become another, in near real time.
Read the explainer →How language models use tools and take actions
How a model decides to act, call a tool, and use what comes back.
Read the explainer →Structured and schema-constrained generation
How a model is held to a strict shape, so its output drops cleanly into fields.
Read the explainer →Grounding answers and reducing hallucination
How answers get tied back to real sources, so a model says less that is not so.
Read the explainer →Long-context memory and compression
How a model holds a long history in view and keeps what matters.
Read the explainer →Embeddings and vector search
How meaning becomes numbers, so the right passage is found by similarity, not keywords.
Read the explainer →Low-latency inference: speculative decoding and serving
The tricks that make a large model answer quickly, from speculative decoding to smarter serving.
Read the explainer →PII redaction and privacy-preserving ML
How personal details get found and masked, and how models learn without exposing them.
Read the explainer →Entity resolution: one customer, one record
How a system spots that two records are the same account or contact, and merges them into one.
Read the explainer →Schema matching across systems
How fields from different CRMs and tools get lined up so the data means the same thing everywhere.
Read the explainer →Filling in missing data
How blanks in a record get filled in well enough to act on, without making things up.
Read the explainer →Detecting and repairing dirty data
How errors, typos, and contradictions in a dataset get found and fixed at scale.
Read the explainer →Turning conversations into structured fields
How free text and talk become the named fields a CRM expects.
Read the explainer →Fuzzy matching and blocking at scale
How near-duplicate names and records get matched quickly across millions of rows.
Read the explainer →Truth discovery: resolving conflicting records
How a system decides which value is right when sources disagree.
Read the explainer →Weak supervision and data programming
How rules and noisy signals stand in for hand-labeled data to train a model.
Read the explainer →Normalizing names, addresses, and companies
How messy names, addresses, and companies get put into one consistent form.
Read the explainer →Cleaning data with minimal human effort
How a system asks a person only about the cases where their judgment matters most.
Read the explainer →Lead scoring on clean sales data
How a model ranks which leads are worth the rep's time, once the data is clean.
Read the explainer →Matching records without exposing PII
How two parties match records without either one revealing the underlying personal data.
Read the explainer →Data lakes and the lakehouse
How all the raw signals get stored cheaply in one place, ready to query later.
Read the explainer →Change data capture: keeping systems in sync
How a change in one system flows to the others within seconds, by watching its log.
Read the explainer →Stream processing: data as it happens
How data gets handled the moment it arrives, not in a nightly batch.
Read the explainer →Two-way sync and conflict resolution
How two systems update each other and settle on one answer when both change at once.
Read the explainer →Distributed data processing at scale
How work gets split across many machines to crunch data that will not fit on one.
Read the explainer →Event sourcing and the unified log
How an append-only log of events becomes the single source the rest is built from.
Read the explainer →Exactly-once and idempotent writes
How a record gets written once and only once, even when a sync retries.
Read the explainer →Eventual consistency and consensus
How copies on different machines end up agreeing, and when they are guaranteed to.
Read the explainer →Incremental sync: webhooks, polling, and sync tokens
How a system pulls only what changed since last time, instead of everything.
Read the explainer →One model across email, calendar, CRM, and tasks
How different tools get mapped to one shared shape so they work as a single picture.
Read the explainer →Transformers and attention
The architecture behind modern language and speech models, and why attention changed everything.
Read the explainer →Tokenization: how text becomes tokens
How words and sounds get chopped into the units a model actually reads.
Read the explainer →Self-supervised and contrastive learning
How models learn from raw, unlabeled data before anyone labels a thing.
Read the explainer →Few-shot and in-context learning
How a model picks up a new task from a few examples in the prompt, with no retraining.
Read the explainer →Chain-of-thought reasoning
How asking a model to show its working makes it reason more reliably.
Read the explainer →Fine-tuning and parameter-efficient adaptation
How a general model gets specialized to a domain without retraining the whole thing.
Read the explainer →RLHF and preference optimization
How human preferences shape a model's tone, helpfulness, and judgment.
Read the explainer →Knowledge distillation
How a big model teaches a small, fast one to behave almost the same.
Read the explainer →Mixture of experts
How a model routes each input to just the parts it needs, for scale without the cost.
Read the explainer →Continual learning without forgetting
How a model keeps learning new things without losing what it already knew.
Read the explainer →Multi-tenancy and tenant isolation
How one system serves many customers while keeping each one's data walled off from the rest.
Read the explainer →Access control: RBAC, ABAC, and least privilege
How a system decides who can see and do what, and why less access is safer.
Read the explainer →Encryption in transit and at rest
How data is scrambled on the wire and on disk, and how the keys are kept.
Read the explainer →Authentication, SSO, and OAuth
How you prove who you are once and move between systems safely.
Read the explainer →Audit logging and tamper-evident trails
How a system keeps an honest, unchangeable record of who did what, and when.
Read the explainer →Zero-trust architecture
Why modern security trusts nothing by default and verifies every request.
Read the explainer →Data residency and sovereignty
How data gets kept in the region a customer requires, and why it matters.
Read the explainer →Compliance: SOC 2, ISO 27001, and GDPR
What the big compliance frameworks actually ask for, in plain terms.
Read the explainer →Reading sentiment and intent in a conversation
How a system reads the mood and aim of a call as it happens, from both the words and the voice.
Read the explainer →Intent recognition and dialogue acts
How a system works out what a speaker actually wants: a question, an objection, a yes.
Read the explainer →Dialogue state tracking
How a system remembers what has been agreed and what is still open across a multi-turn talk.
Read the explainer →Coreference: tracking who and what across a conversation
How a system knows who "they" and what "it" refer to, turns later.
Read the explainer →Real-time updates: WebSockets, SSE, and push
How fresh data reaches the app the instant it changes, instead of waiting for a refresh.
Read the explainer →gRPC, RPC, and binary serialization
How services talk to each other fast, with compact binary messages over efficient channels.
Read the explainer →Caching, from CPU to CDN
How keeping a copy close cuts the wait, and the hard part: knowing when it has gone stale.
Read the explainer →Load balancing and autoscaling
How traffic gets spread across machines, and how the fleet grows and shrinks with demand.
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