Project Proposal

Project Title

Cognitive Communication Coach: AI-Powered Conversation Reflection System on AWS

1. Project Overview

The project is a bilingual AI coaching system that analyzes a short conversation recording or transcript after the conversation ends. It produces a structured report with a summary, topic intent, weak reasoning points, improved response suggestions, and practice questions.

The system is not designed to secretly answer for users during a live conversation. Instead, it helps users review their own communication, understand where their reasoning was unclear, and practice better responses for future situations.

2. Problem Being Solved

Students, interns, and junior professionals often struggle in conversations even when they understand part of the topic. Common issues include:

  • Not organizing thoughts quickly under pressure.
  • Freezing when challenged with repeated “why” questions.
  • Giving answers without evidence or examples.
  • Realizing better responses only after the conversation ends.
  • Having no structured way to review and improve communication.

The problem is not only language fluency. It is also reasoning structure, confidence, and reflection.

3. Goals

  • Build a practical AWS-based system using at least three AWS services.
  • Allow users to upload a short audio file or transcript for analysis.
  • Convert speech to text using Amazon Transcribe when audio is provided.
  • Use Amazon Bedrock to generate a coaching report.
  • Store job status and results in a structured way.
  • Provide reproducible implementation steps, testing, monitoring, security, cost, and cleanup documentation.
  • Keep the project realistic for one student and suitable for a bootcamp final project.

4. Solution Architecture

flowchart LR User["User browser"] --> APIGW["Amazon API Gateway"] APIGW --> UploadLambda["Lambda: create upload URL"] UploadLambda --> S3["Amazon S3 private bucket"] S3 --> SFN["AWS Step Functions workflow"] SFN --> Transcribe["Amazon Transcribe"] SFN --> Bedrock["Amazon Bedrock"] SFN --> DDB["Amazon DynamoDB"] APIGW --> ResultLambda["Lambda: get job result"] ResultLambda --> DDB SFN --> CW["Amazon CloudWatch"]

5. AWS Services Used

ServiceRoleReason for Selection
Amazon S3Stores uploaded audio, transcripts, and generated reportsDurable, low-cost object storage with private access controls
AWS LambdaHandles upload URL creation, job creation, and result retrievalServerless compute suitable for short backend tasks
Amazon API GatewayExposes REST API endpoints for the frontend or test clientManaged API layer with authentication and throttling options
AWS Step FunctionsOrchestrates transcription and AI analysis workflowMakes async processing visible, retryable, and easier to debug
Amazon TranscribeConverts audio conversation files into textManaged speech-to-text service, avoids building custom ASR
Amazon BedrockGenerates structured coaching feedbackManaged foundation model access without managing model infrastructure
Amazon DynamoDBStores job metadata, status, and report referencesServerless NoSQL database with simple key-value access pattern
Amazon CloudWatchStores logs and operational metricsRequired for debugging, monitoring, and validation
AWS IAMControls service permissionsSupports least-privilege security design

6. Timeline

PhaseDurationDeliverables
Requirement analysisWeek 1Problem statement, user journey, project scope
AWS foundationWeeks 2-3IAM review, S3 bucket, DynamoDB table, basic Lambda
AI workflowWeeks 4-6Transcribe flow, Bedrock prompt, Step Functions workflow
API and validationWeeks 7-8API Gateway endpoints, sample input testing
DocumentationWeeks 9-10Workshop steps, diagrams, screenshots, bilingual content
Optimization and defenseWeeks 11-12Security review, cost cleanup, final rehearsal

7. Budget Awareness

This project is designed for low bootcamp-scale usage:

  • Audio files should be limited to 3-5 minutes during testing.
  • S3 storage should remain small because only sample files are used.
  • Lambda and Step Functions usage should stay within low-volume testing.
  • Bedrock cost depends on model choice and token usage, so prompts should be concise.
  • All resources must be cleaned up after the demo to avoid unnecessary charges.

The final report should include a screenshot or exported estimate from AWS Pricing Calculator after the student’s actual region and model choice are confirmed.

8. Risks

RiskImpactMitigation
Audio transcription is inaccurateAI report quality decreasesUse clear sample audio, allow transcript upload fallback
Bedrock output is too vagueWeak demo valueUse a structured prompt and fixed evaluation rubric
Cost grows from repeated AI callsUnexpected chargesLimit file duration, delete test files, monitor usage
Privacy concernsSensitive conversation data exposurePrivate S3, least-privilege IAM, retention cleanup, consent notice
Workflow errors are hard to debugImplementation delaysUse Step Functions execution history and CloudWatch logs
Scope creep into real-time assistantProject becomes unrealisticKeep MVP async and post-conversation only

9. Success Criteria

  • A sample audio or transcript can be uploaded.
  • The system creates a job record and stores processing status.
  • Audio input is converted into text, or a text transcript is accepted directly.
  • Bedrock produces a structured coaching report.
  • Results can be retrieved and reviewed.
  • Logs, errors, permissions, cost controls, and cleanup steps are documented.