Which meeting tools can you connect to?

Zoom, Google Meet, and Microsoft Teams each provide recordings or transcripts through their APIs, and we also accept uploaded audio and subtitle files for calls on other platforms. Most projects start with one platform and add the others once the summaries and search results look right to the people who attend those meetings.

Do we have to record every meeting?

No, and most teams should not. A workable starting point is the recurring calls where obligations are created: client calls, project reviews, sales discovery, and planning sessions. Internal standups usually produce little that is worth indexing, and skipping them keeps the corpus clean and the running cost low.

Where do the transcripts live, and who can read them?

We agree that in writing before development starts. The document names the people and roles who can read each set of transcripts, the cloud account that holds the audio, transcripts, and search index, how long each is kept, and how a deletion request is handled. Where a board meeting or an HR conversation has stricter rules than a client call, the permissions and retention are set separately.

How accurate are the summaries and the answers?

Accuracy follows the recording. Clear audio with named speakers produces good transcripts, and a transcript with product names, acronyms, and jargon spelled correctly produces good summaries. Once the terms are wrong, every answer that quotes them is wrong too, which is why we build a glossary with you and show the source passage next to each answer.

How long does a first version take, and what does it cost?

A working pipeline over an initial set of meetings usually takes two to four weeks: transcription, summaries, decisions and action items, and search with source links. Permissions, several meeting sources, and self-hosted models extend that. Work runs at our flat rate of $39 per hour or as a fixed price agreed after the scoping session.

AI Meeting Knowledge Base

We turn Zoom, Google Meet, and uploaded transcripts into a searchable internal knowledge base with summaries, decisions, action items, and cited answers.

See our work:

Summ.ee→smrtAI→SmartAIChats→

Every company runs on decisions that were made out loud. A price gets agreed on a sales call, a deadline moves on a project call, a client asks for something different in week three. The record exists somewhere: a Zoom recording, a transcript, a notebook, or a channel message one person remembers writing. Six months later, the answer to "what did we agree?" sits across four systems.

A recording is not a knowledge base. An hour of audio is slow to search, a raw auto-transcript is hard to read, and neither says what was decided, who owns the follow-up, or which sentence a claim came from. We build the knowledge base that answers those questions from the calls you already run: transcripts come in from Zoom, Google Meet, Microsoft Teams, or a manual upload, and a structured, permissioned, searchable record comes out, with the passage and timestamp behind every answer so a reader can check the source before acting on it.

What goes wrong without one

The failures repeat. A commitment made in a kickoff call never reaches the project plan. A client refers to the number discussed in a meeting, and two people remember it differently. Someone spends a morning answering a question that was settled a year ago.

Who this is for

The value is highest where calls carry obligations:

Professional services firms where the file, not a partner's memory, has to hold the record
Agencies running several client accounts on recurring calls
Product and engineering teams with planning, review, and specification calls
Sales teams that want discovery calls to feed a CRM with the buyer's own words

What we deliver

Getting the transcripts in

Three capture routes, chosen per team. Platform transcripts from Zoom, Meet, or Teams are cheapest, because the audio stays in that platform's storage. A recording bot that joins the call gives more control over speaker separation and language handling. Manual upload covers phone calls, in-person recordings, and files with subtitles. Participants are told when a meeting is recorded, and where the audio must stay inside your network we run speech recognition on your own infrastructure.

Summaries, decisions, and action items

Speakers are separated, the transcript is cleaned, and each meeting is processed into structured fields: a short summary, the decisions taken, action items with an owner and a date, and questions left open. Extraction follows a schema we agree with you, so a decision stays a decision and a suggestion stays a suggestion. Owners get a confirmation step, because a model that guesses an owner creates work nobody agreed to do.

Search that cites the passage

Retrieval runs across the whole archive and returns the meetings where the topic appears, a short answer, and the exact passage behind it with a timestamp. Search combines meaning-based matching with keyword matching, because an invoice dispute question needs both the conversations about pricing and the calls that mention the invoice number. When nothing relevant exists, the system says so instead of assembling an answer from unrelated calls.

The AI calls run through controlled server endpoints with keys held out of the browser, which we cover under secure AI backend and API key protection. Records and search indexes sit in PostgreSQL or a managed vector store, using the same discipline we apply in database design and migration.

Who can read what, and how long you keep it

Transcripts are among the most sensitive documents a company holds, so access and storage are agreed in writing before the build starts. That document names the roles allowed to read each set of meetings, the account that owns the audio, transcripts, and index, the retention period for each, and how a deletion request is handled. Access is enforced per workspace and per meeting series, so an agency keeps each client's calls inside that client's space.

Storage is your cloud account by default (Google Cloud, Azure, or AWS), with the pipeline running under your billing and your audit logs. Where policy requires it, speech recognition and language models run on your hardware and no transcript leaves it. We also list which model vendors receive what, and whether they train on customer data.

Why the answers are only as good as the source

A knowledge base inherits the quality of its input. Room microphones pick up crosstalk, a laptop on speakerphone drops the far end, and every unrecognised product name is mangled and then repeated through the summaries. We build a glossary with you and test it against real recordings.

Coverage matters as much as quality. Meetings nobody recorded are invisible to search, and a corpus that starts last month cannot answer a question about last year.

How an engagement runs

The first step is a scoping session: which meeting series to include, where the recordings live, who may read them, and which decisions the team keeps losing. It ends with the data handling agreement and a fixed proposal for the first version.

Then a pilot over twenty to fifty real recordings, covering transcription, summarisation, action item extraction, and search with source links. You read the output against your own memory of those calls, which is the fastest way to find where extraction is wrong.

After the pilot we add what the team asked for once they saw real results: permissions, retention rules, and handoff into the tools people already use, through the connections covered under API development and integration. Documents and email threads can join the same index later, which connects this work to AI trend and content automation.

What it costs

Work runs at our flat rate of $39 per hour, or as a fixed price once scoping has defined the source material and the permission model.

The published order that fits most closely is the AI assistant and workflow automation budget on our pricing page, quoted at $1,800 to $3,900 across one to three weeks. That band covers a first working pipeline: transcription, summaries, decisions and action items, and searchable answers over an initial set of calls. Scope grows with the number of meeting sources, the history to import, the complexity of the access rules, retention requirements, integrations with a CRM or task tool, and whether the models run on your own infrastructure.

A usable first version is usually two to four weeks for one team and one platform. A company-wide base with per-client permissions and several sources is a project measured in phases, and we will say which one after the pilot.

Proof

Summ.ee is a WordPress and WooCommerce gifting storefront we delivered, with Next.js, Node, Express, MongoDB, and OpenAI in the project stack. What carries over is interface discipline: browsing works when the catalogue is structured and the path to a decision is short, and a meeting index lives under the same constraint.

smrtAI is an AI LiveChat product built on OpenAI APIs, with configurable bot scripts, custom terminology per business, and email, Messenger, Instagram, WhatsApp, and Telegram channels. It shows we run AI features as supported products rather than demos, including the tuning that keeps a model useful after launch.

SmartAIChats is a conversational SaaS platform on React, Next.js, Node, Express, MongoDB, and OpenAI, designed so non-technical teams can deploy it and get consistent answers from one system. Grounding answers in an approved knowledge set and keeping the data model able to grow are the habits a meeting knowledge base depends on most.

Related services

AI development for the model integration and automation work behind this page
Custom web application development for the application, permissions, and interfaces that hold the archive
Database design and migration for the schema, indexing, and query work that makes search fast
API development and integration for pulling recordings in and pushing action items out to your CRM or task tool
AI trend and content automation when the same pipeline has to produce reviewable drafts instead of internal answers

Next step

Tell us which meeting series loses information today, where the recordings live, and who may read them. We will come back with a data handling agreement and a scope for a pilot over real recordings.

Contact us with the platform you use and the decision trail you are missing, or put the requirement through the Vasilkoff.info estimator for a first scope and cost range.