Aidena

— AI STACK RECOMMENDATION

Multimodal Customer Feedback Analysis Stack

Unified pipeline for ingesting text, voice, and survey data from customers, extracting insights via multimodal AI, and storing results for analysis. Scales from startup to enterprise with managed services.

Stays alive for 365 days after the last visit.

Other

Multimodal Customer Feedback Analysis Stack

Unified pipeline for ingesting text, voice, and survey data from customers, extracting insights via multimodal AI, and storing results for analysis. Scales from startup to enterprise with managed services.

high confidence

Core Stack ℹ︎

Airbyte

Primary

Centralize customer feedback from multiple sources (surveys, CRM, support tickets, voice recordings) into a single data warehouse. 300+ connectors enable easy integration of diverse feedback channels.

$0-$500/month

Deepgram

Primary

Convert voice feedback and call recordings to text with speaker diarization. Real-time and batch processing supports high-volume customer call analysis.

$0-$300/month

Cohere Embed API

Primary

Multimodal embeddings for text and images in feedback. Unified semantic space enables cross-modal similarity search and clustering of customer sentiment across channels.

$0-$200/month

Claude Sonnet 4

Primary

Extract structured insights, sentiment, themes, and actionable recommendations from multimodal feedback. Low hallucination rate critical for accurate customer analysis.

$100-$500/month

Complete the Stack ℹ︎

Elasticsearch Vector Search

Alternative

Hybrid search combining full-text BM25 and vector similarity. Query feedback by keyword and semantic meaning for trend discovery and root cause analysis.

$0-$300/month

Dagster

Alternative

Orchestrate the entire feedback pipeline: ingest → transcribe → embed → analyze → store. Asset-based approach ensures data quality and lineage tracking.

$0-$400/month

Getting started

  1. 1Set up Airbyte to sync survey platforms (Typeform, Qualtrics), CRM (Salesforce, HubSpot), and support tickets (Zendesk) to a PostgreSQL or Snowflake warehouse.
  2. 2Configure Deepgram to transcribe voice recordings and call center audio in batch or real-time.
  3. 3Use Dagster to orchestrate daily pipelines: fetch new feedback → transcribe audio → generate embeddings via Cohere.
  4. 4Store embeddings in Elasticsearch for hybrid search.
  5. 5Use Claude Sonnet to analyze transcripts and text feedback, extracting sentiment, themes, and recommendations.
  6. 6Build dashboards (Metabase or Looker) to visualize trends, sentiment distribution, and top feedback themes.
Copy link to clipboard

What are you building?

Build your own AI stack →