Kyaxa is knowledge infrastructure for enterprise teams. It captures every conversation across an organisation and structures it into a queryable knowledge layer — preserving decisions, commitments, context, and reasoning over time. Unlike note-taking or transcription tools, Kyaxa is built as institutional knowledge that compounds with use.

What is knowledge infrastructure?

Knowledge infrastructure is a persistent, queryable layer of organisational knowledge built from the conversations, decisions, and commitments that already happen inside a company. It differs from documentation systems (which require manual entry) and from meeting transcription tools (which produce static records). Kyaxa captures knowledge at the moment it is created and makes it continuously retrievable with permission-aware, temporal context.

How is Kyaxa different from an AI meeting assistant or note-taker?

Meeting assistants and note-takers produce transcripts and summaries — static artefacts of individual meetings. Kyaxa is knowledge infrastructure: it builds a structured, evolving knowledge layer across all conversations, with temporal reasoning, permission-aware retrieval, and provenance. The output is institutional knowledge, not meeting notes.

How does Kyaxa compare to enterprise AI products like Microsoft Copilot or Google Gemini?

Microsoft Copilot and Google Gemini are horizontal AI assistants embedded in productivity suites. Kyaxa is a dedicated memory infrastructure layer. It is purpose-built to preserve and retrieve organisational decisions, commitments, and reasoning over time — and is deployable on-premises or in private infrastructure where horizontal AI assistants typically are not.

Where is organisational data stored?

The organisation chooses: managed cloud, the organisation's own cloud infrastructure, or fully on-premises. Conversation data, transcripts, and the memory graph never leave the chosen environment. Every organisation's data is isolated at the database level — by design, not by policy.

Is Kyaxa enterprise-ready?

Yes. Kyaxa is designed with SOC 2 Type II controls from day one, supports on-premises deployment, provides tamper-proof audit logging, granular access controls down to the individual meeting, and a strictly confidential mode that is locked even from administrators.

Kyaxa - What Is Meeting Intelligence?

Meetings are your company's most underused data source

Every organisation runs on meetings. Strategy is discussed in meetings. Decisions are made in meetings. Action items are assigned in meetings. Yet almost none of this information is captured in a way that's useful after the meeting ends.

The average professional spends 15 hours per week in meetings. That's nearly 40% of working time. Multiply that across a team of 20, and you have 300 hours of conversations every week: conversations full of decisions, context, and commitments that mostly vanish the moment the call ends.

This isn't a new problem. What's new is that AI can now solve it.

Meeting intelligence defined

Meeting intelligence is the practice of automatically capturing, transcribing, and structuring meeting content so it becomes searchable, actionable knowledge.

It's not just transcription. A transcript is a wall of text: useful for reference but impractical for daily use. Meeting intelligence goes further:

Transcription: converting speech to text with speaker identification
Structuring: extracting decisions, action items, key discussion points into organised minutes
Attribution: identifying who said what, who owns which action item
Search: making months of meeting history queryable in seconds
Q&A: asking natural-language questions and getting answers sourced from your meetings (e.g. "What did we decide about pricing last quarter?")
Meeting prep: automatically generating a brief before your next meeting based on everything discussed previously with those participants

The difference between a transcript and meeting intelligence is the difference between a filing cabinet full of unsorted paper and a searchable knowledge base.

Why now?

Three technologies matured simultaneously to make this practical:

1. Speech-to-text accuracy crossed the usability threshold

Modern speech models like Whisper achieve word error rates below 5% for English, better than most human note-takers. Five years ago, automated transcription was a novelty. Today, it's more accurate than the average person typing notes while trying to participate in the discussion.

2. Speaker diarization became reliable

Knowing what was said is only half the problem. Knowing who said it matters just as much. Speaker diarization (the AI task of segmenting audio by speaker) has improved dramatically. Modern models can distinguish speakers in real-time with high accuracy, even in group conversations with overlapping speech.

3. Large language models can structure unstructured content

Transcripts are unstructured by nature. People interrupt, go on tangents, circle back. LLMs can take a raw transcript and extract the signal: what was decided, what needs to happen next, what the key discussion points were. The output is structured minutes that read like a well-organised document, not a raw dump of everything that was said.

What good meeting intelligence looks like

A meeting intelligence system should produce three things:

Structured minutes

Not a transcript. Not bullet points someone typed during the call. Structured minutes with:

Decisions: what was agreed, with enough context to understand why
Action items: who does what, by when
Key discussion points: the substance of the conversation, attributed to speakers
Participants: who was in the room

These should arrive in your inbox within minutes of the meeting ending, without anyone taking notes.

Searchable history and Q&A

After six months of documented meetings, your team has a knowledge base. But it's only useful if you can access it. Good meeting intelligence goes beyond keyword search: it lets you ask questions in natural language and get sourced answers:

"What did we decide about the pricing model?" You get the answer with a citation to the exact meeting and date
"What action items does Sarah have from the last month?" Results are pulled from every meeting she attended
"What's been discussed about the Q3 launch?" The answer is synthesised across multiple conversations

This is fundamentally different from searching a folder of documents. You're querying your team's collective memory, and the AI finds the answer across hundreds of meetings in seconds. Every answer is traceable back to the source meeting, so you can verify it.

Meeting prep briefs

The most undervalued use of meeting intelligence is preparation. When you have structured records of every past meeting, AI can generate a brief before your next meeting, automatically, based on the participants and topic:

Last discussed: what was covered in previous meetings with these people
Open action items: what's still outstanding from last time
Key decisions: relevant decisions already made that the group should reference
Talking points: suggested items to raise based on the meeting history

Instead of walking into a meeting cold (or spending 20 minutes re-reading old notes), you get a two-minute brief that puts you fully in context. The AI does the preparation work that nobody has time to do manually, drawn from every relevant meeting in your history.

This changes meeting quality dramatically. When everyone walks in knowing what was decided last time and what's still open, the conversation starts at a higher level.

The compound effect of documented meetings

The real value of meeting intelligence isn't any single meeting. It's the accumulation.

Week 1: Your team has minutes from 10 meetings. Useful for reference.

Month 3: You have minutes from 120 meetings. Patterns emerge. You can search for decisions across projects. New team members can read the history of any initiative.

Month 6: You have 250+ meetings documented. The system can answer questions like "What has been our approach to enterprise pricing over the past quarter?" by synthesising across dozens of conversations. Your meeting history becomes institutional memory.

This is what "knowledge compounds" means. Each meeting adds to the base. The more you have, the more valuable each new meeting becomes, because it connects to everything that came before.

Who benefits most

Meeting intelligence isn't a nice-to-have for every team. It's transformative for specific use cases:

Leadership teams: where decisions have the highest stakes and the worst documentation. Board meetings, executive standups, and strategy sessions are exactly the conversations that should be captured with precision.

Client-facing teams: sales calls, client reviews, project kickoffs. Every promise made, every requirement discussed, every timeline agreed. Searchable and attributable.

Cross-functional teams: where context gets lost between departments. When engineering, product, and design are all in a meeting, the decisions made need to be accessible to everyone who wasn't in the room.

Regulated industries: healthcare, legal, finance. Where documentation isn't optional and the cost of a forgotten decision can be material.

Meeting intelligence vs meeting recording

Recording meetings is not the same as meeting intelligence. A recording is a 45-minute file that nobody will watch. It's the meeting equivalent of "I'll just save this file on my desktop": technically preserved, practically inaccessible.

Meeting intelligence transforms the recording into something useful: structured text that can be read in 2 minutes, searched in seconds, and synthesised across your entire history. The recording is the raw material. The intelligence is the product.

The bottom line

Meetings aren't going away. The question is whether the knowledge generated in those meetings compounds or evaporates.

Meeting intelligence ensures it compounds. Every decision documented. Every action item tracked. Every conversation searchable. The teams that adopt this early build an information advantage that grows with every meeting they have.

The rest are still asking "what did we decide last week?" in Slack.

What Is Meeting Intelligence?