Tokenizing human interaction

Your agents’ most important decisions need reliable context.

Agents can’t access most real-world knowledge. We capture it where it’s said, in meetings, calls, rooms and screens, with consent, and hand it to your agents.

Agents need what people know.

Most of it is said, not written: in calls, meetings and rooms.

Capture

_æfer listens where work happens.

Meetings, calls, rooms and pocket recorders, with consent.

Transcribe

Precisely captured.

Accurate transcripts, each in the language it was spoken.

Identify

Who said what.

Every line named by voice, even offline.

Hand over

Ready for your agents.

As structured context, through API or MCP.

_æfer is built on the transcription company Netflix and ElevenLabs trust for its accuracy.

7Mhours transcribed
1Mspeakers identified
50,000customers

Company knowledge,
said in meetings.calls.rooms.screens.the hallway.pocket recorders.

Connectors read what’s written down: mail, docs, tickets. _æfer brings in what’s said, which is where most decisions happen.

Context to scale every team.

Capture everything the company says once, and every team’s agents build on it. A few of the jobs it makes possible:

Sales coaching

Compares every demo with the ones that closed and tells each rep what to change.

SalesHead of Sales

Client follow-ups

Every promise made to a client goes out as a follow-up, with who made it.

SalesAccount executive

Decision log

Why X was decided, who decided it, and the meeting where it happened.

ProductProduct manager

Customer voice

Every feature request said on a call, de-duplicated, with the clip.

ProductProduct manager

Incident timeline

The postmortem timeline, written from the incident call as it happened.

EngineeringOn-call engineer

Escalation context

The whole account history, including what sales promised, on every escalated ticket.

SupportSupport lead

Action items that chase

Every action item from every meeting, with an owner and a date, nudged until done.

OperationsProject manager

Audit trail

Who told the client what, and when, for every regulated conversation.

OperationsCompliance officer

Handover when someone leaves

What Pau knew, from two years of his calls, for whoever takes over.

PeopleTeam lead

Weekly exec digest

What every team decided, promised and worried about this week, on one page.

LeadershipCEO

Garbage in,
garbage out.

A bad transcript or a missing name makes a bad agent. Same four voices, two transcripts, and what an agent does with each.

?Speaker 1Legal needs the deep pay signed by Friday.en
PRPau RibasLegal needs the DPA signed by Friday.en
?Speaker 2I’ll send it to Kenny what an a bay at north wind.en
MVMarta VidalI’ll send it to Kenji Watanabe at Northwind.en
?Speaker 1The cord, who posed a contract of email.en
LSLaia SolerD’acord, ho poso al contracte avui mateix.ca
?Speaker 3Con dos años bajamos al veinte siete.en
JMJoan MartíCon dos años bajamos al veintisiete.es
your agent, on the generic transcriptSomeone will send the deep pay to Kenny.
your agent, on _æferMarta sends the DPA to Kenji at Northwind tonight; Legal needs it signed by Friday.
The accuracy engine

The best model.
Every time.

You never pick a model. We run our own fine-tuned models and third-party ones, score them on real meeting audio, and send each language to the one that writes it best. When a better model ships, it takes over.

130+languages
10+speech engines
Livescores on real meeting audio
0model choices for you to make
scored live
Scored against the linguist workforce’s corrections
Speaker identification

Four voices, one room.
Know who said what.

Three in the room, one on the call, and nobody logs in. Voice prints put a name on each voice, even offline.

SDKs

Ship it in 5 minutes.

Headless, in your name, inside your own app. A company brain built its whole Meetings feature with it in 5 minutes.

acme/notetaker.ts
APIMCPUI ComponentsWhite labelHeadless

Trust is the core.

Your context. Your rules.

Your agents get the work. Nobody gets watched. A work filter keeps personal moments out, and nobody can change that, not even an admin.

_æfer always_æfer never
Sticks to meetings and workReads private chats or direct messages
Tells everyone in the call that notes are onListens in the background without people knowing
Lets each person say no, pause, review or remove what they shareKeeps what someone asked it not to keep
Keeps user rules on, whatever the admin setsLets anyone switch them off
Follows your company’s rules for who is recorded, who sees it and where it’s storedShares with anyone your rules leave out
LSLaia’s Tuesday
Product review
Call with her mum
Call with Globex
Direct message to Joan
Pricing doc, with her agent
Booking a dentist
work filter
to Acme’s brain
stays with Laia

Not the company brain.
The input layer to it.

Brains, agents and apps are built on top. _æfer provides the context.

Your notetaker in every call

Joins Zoom, Meet and Teams as you.

Bot-less capture

Your desktop app records meetings and calls on the computer.

Capture screen context

What happens on screen, as events: app switched, slide shown, doc opened.

Record the room

In-person meetings, in your app.

Connect recorders

Plaud and Pocket, via Connect SDK.

Meetings feature for your app

Sidebar, meeting list and summary, as components.

Structured output

Decisions, commitments and risks, from every call.

→your brain

Clean, attributed input.

Who said what, in which language, with the consent record. Garbage in, garbage out.

→your agents

The capture you don’t want to maintain.

Bots, desktop, mobile and devices, as SDKs. Accuracy per language included.

→your app

Inside your app, in your name.

White label, in your own client app, B2C included. The rules your company sets, with consent handled.

Start with what was said.

Connect your agent to the meetings, calls and rooms your rules allow. Ship in 5 minutes.

Connect your agent