IES Holdings, Inc.

AI-native vector embeddings for IES Holdings, Inc. quarterly earnings calls

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Ticker

IESC

Coverage

2020–present

Embedding dims

768

Model

gemini-embedding-2-preview

About IESC Earnings Embeddings

VectorFin provides vector embeddings for every IES Holdings, Inc. earnings call from 2020 through the present quarter. Each earnings call transcript is chunked into semantically coherent segments and vectorized using Google's gemini-embedding-2-preview model, producing 768-dimensional dense vectors optimized for cosine similarity search.

All data is bitemporal: every embedding chunk carries an effective_ts (when the earnings call occurred) and a knowledge_ts (when VectorFin ingested and vectorized the data). This enables point-in-time backtesting — query the data as it was known at any historical date.

Embedding data is updated within 24 hours of each earnings call. The full history is available via REST API (all plans) or as Apache Iceberg tables on GCS (Pro+ plans), queryable natively from Snowflake, BigQuery, or Databricks.

Common IESC use cases include quarter-over-quarter sentiment comparison, similarity search for analogous commentary at peers, and Retrieval-Augmented Generation (RAG) — grounding LLM answers about IES Holdings, Inc. in citable transcript chunks.

Available fiscal periods

2024-Q4

2024-Q3

2024-Q2

2024-Q1

2023-Q4

2023-Q3

2023-Q2

2023-Q1

Showing recent 8 quarters. 2020–present available via API (beta).

Access via API

# Fetch IESC embeddings for the latest quarter
curl https://api.vectorfinancials.com/v1/embeddings/IESC \
  -H "X-API-Key: vf_sk_your_key_here" \
  -G \
  -d "fiscal_period=2024-Q4" \
  -d "limit=10"

# Response schema
{
  "data": [{
    "ticker": "IESC",
    "fiscal_period": "2024-Q4",
    "chunk_idx": 0,
    "text": "...",
    "embedding": [0.023, -0.091, ...],
    "effective_ts": "2025-01-30T00:00:00Z",
    "knowledge_ts": "2025-01-31T06:00:00Z",
    "model_version": "gemini-embedding-2-preview"
  }],
  "next_cursor": "..."
}

Related Concepts

Embedding →Retrieval-Augmented Generation (RAG) →Matryoshka (MRL) →Cosine Similarity →Bitemporal Data →Effective Timestamp →Knowledge Timestamp →Semantic Search →

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Free tier includes top 100 tickers with 1,000 API calls/month.

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