archeai-sdk

Official Python SDK for the ArcheAI behavioral intelligence API — executive behavioral histories, multi-speaker session analysis, deception-relevant signals, personality, and communication-style analytics from speech.

pip install "archeai-sdk[pandas]"

Documentation

Every public method also carries the same documentation in its docstring (help(ArcheClient.analyze_session)), and the REST API's own reference is at api.arche-ai.com/docs.

30-second tour

from arche_ai import ArcheClient
c = ArcheClient(api_key="arche_sk_...")

# Quant: one row per (executive, quarter), point-in-time safe
df = c.behavioral_history("AAPL", from_q="2018Q1")

# Text: async job -> wait -> result dict
style = c.wait(c.analyze_style(exec_answers)).result

# Single-speaker clip: upload -> analyze -> wait -> (delete), one call
out = c.analyze_file("clip.wav", lines=("deception", "voice-personality"),
                     delete_after=True)

# Multi-speaker recording: diarize + transcribe + per-speaker scores
sess = c.analyze_session("interview.wav", phi=True)   # HIPAA-mode example
sess.speaker_frame()      # per-speaker Big Five + style
sess.segment_frame()      # per-speaker behavioral time series

Quant quickstart

from arche_ai import ArcheClient

c = ArcheClient(api_key="arche_sk_...")

# Flagship: per-executive quarterly behavioral trajectories, from
# ArcheAI's 1.8M-earnings-call research corpus (2005-present).
# Point-in-time safe: each quarter's scores derive only from that
# quarter's calls; history is append-only — safe for backtests.
df = c.behavioral_history("AAPL", from_q="2018Q1")
#   one row per (executive, quarter):
#   7 communication-style z-scores + Big Five estimates

# Example: flag executives breaching their own uncertainty baseline
pivot = df.pivot_table(index="quarter", columns="executive",
                       values="style_uncertain_certain")
z = (pivot - pivot.expanding().mean()) / pivot.expanding().std()
print(z.tail(4))  # who no longer sounds like themselves?

These are the same measures behind ArcheAI's published event-study results (out-of-sample lift over price, Loughran-McDonald sentiment, and SUE baselines at 1–5 day horizons).

Text analyses (async jobs)

job = c.analyze_fraud_language(qa_transcript_text)  # >= 200 chars
job = c.analyze_personality(text)
job = c.analyze_style(text)

result = c.wait(job)          # poll to completion
print(result.result)

Audio (bring your own recordings)

audio_id = c.upload_audio("interview.wav")   # one call: 3-step flow
job = c.analyze_deception_audio(audio_id)    # research signal
job = c.analyze_voice_personality(audio_id)  # voice-only Big Five
final = c.wait(job)

Uploaded audio is automatically deleted from storage after 7 days; delete sooner via the API or your account's My Data page.

Utilities

c.models()    # available models + per-call credit costs
c.account()   # remaining credits

Important framing

All scores are research signals intended to inform professional judgment — not investment advice, accusations, lie detection, or determinations of fact. Validation methodology, published nulls, and boundary conditions are documented at api.arche-ai.com/docs.

Roadmap

• Speaker identification against ArcheAI's executive voiceprint database (speakers="auto")
• Bulk/point-in-time history export for backtesting
• Event-study starter kit (reproduce our published results on your own universe)

© ArcheAI. Contact: contact@arche-ai.com