Enterprise / Delivered project

Local AI Meeting Assistant

A local-first assistant for transcription, action capture, and searchable meeting memory with enterprise retention controls.

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Private enterprise AI system structuring information on local compute — Enterprise · Data → Intelligence → ActionPhoto by Headway · Unsplash License ↗

01 / Problem statementDelivered project · Enterprise

The operating problem

Sensitive meetings generate decisions and commitments that are difficult to retrieve, while public processing may conflict with privacy or residency requirements.

Conditions the system must survive03 operating constraints

Meeting audio contains overlap, accents, jargon, and variable devices

Sensitive conversations may not be allowed to leave local infrastructure

Incorrect action items can create real coordination errors

02 / How we approached it

From operating uncertainty to testable evidence.

The work was decomposed into four engineering decisions. Each one produced an artifact the customer could inspect, test, and carry into deployment.

01Frame

Capture authorized audio through approved meeting integrations

Evidence produced

Privacy boundary and meeting-output taxonomy

02Evaluate

Transcribe and segment speech on controlled infrastructure

Evidence produced

Speech and diarization benchmark on representative rooms and accents

03Engineer

Generate cited notes, decisions, and actions

Evidence produced

Action-item extraction study with participant correction

04Operationalize

Apply access, retention, and deletion policies

Evidence produced

Local application package with retention and model controls

03 / Deployment record

Deployed around the workflow—not beside it.

The system boundary includes where inference runs, how evidence reaches existing tools, and how people handle uncertainty after launch.

Implementation statusDelivered project

Topology

Local workstation or private-network inference with optional organization-controlled synchronization.

Integration

Calendar context, approved meeting audio, local documents, and task tools connect through explicit permissions.

Operation

Users confirm actions and can edit or delete artifacts; administrators control retention, model versions, and data paths.

04 / Evaluation metrics

What must be measured before the system earns trust.

Evaluation covers model behavior, workflow burden, and production performance. The metric defines the gate; the customer baseline and acceptance threshold define the target.

01Evaluation gate

Word error rate

How it is measured

WER by room, device, accent, overlap, and domain vocabulary.

What it decides

Determines transcript usability and vocabulary adaptation needs.

02Evaluation gate

Speaker diarization error

How it is measured

Missed, false, and confused speaker time.

What it decides

Controls whether attribution can support decisions and actions.

03Evaluation gate

Action-item precision

How it is measured

Confirmed actions among extracted assignee, task, and due-date candidates.

What it decides

Sets the boundary between suggestions and task creation.

04Evaluation gate

Local response performance

How it is measured

P95 processing time, memory use, and percentage of data kept local.

What it decides

Validates the privacy and device-experience objective.

05 / Customer perspective

Value has to appear in the customer’s operating day.

What matters in practice

The assistant should distinguish transcript, inferred summary, and confirmed action—and make deletion and correction obvious.

01Observable value signalSearchable institutional meeting memory

02Observable value signalFaster follow-through on actions

03Observable value signalReduced exposure of raw audio

04Observable value signalClear retention ownership

06 / Technology context

Tools follow the system—not the other way around.

Final architecture depends on data quality, operating conditions, integrations, risk, and evaluation criteria established during discovery.

Speech-to-textLLMsRAGOn-premise inferenceAccess control

Test the operating assumption

Define the evidence required to move from possibility to production.

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Local AI Meeting Assistant

Sensitive meetings generate decisions and commitments that are difficult to retrieve, while public processing may conflict with privacy or residency requirements.

From operating uncertainty to testable evidence.

Capture authorized audio through approved meeting integrations

Transcribe and segment speech on controlled infrastructure

Generate cited notes, decisions, and actions

Apply access, retention, and deletion policies

Deployed around the workflow—not beside it.

What must be measured before the system earns trust.

Word error rate

Speaker diarization error

Action-item precision

Local response performance

Value has to appear in the customer’s operating day.

Tools follow the system—not the other way around.

Related operational systems.

Competitor Analysis AI System

Edge AI for predictive maintenance and leak detection

Define the evidence required to move from possibility to production.