Policy documents and transcripts contain critical information—regulatory requirements, compliance mandates, meeting decisions, and stakeholder communications. Traditional manual review and brittle text processing scripts cannot handle the volume and variability at scale.

Semantic intelligence transforms unstructured policy text and conversation data into structured, queryable datasets using DataFrame operations. This guide demonstrates production-ready implementation patterns for policy document and transcript analysis.

Infrastructure Requirements

Fenic provides the DataFrame framework for semantic document processing. Install the framework:

bash
pip install fenic

Configure environment variables for LLM providers:

bash
export OPENAI_API_KEY="your-key"
export ANTHROPIC_API_KEY="your-key"
export GOOGLE_API_KEY="your-key"

Initialize a session with multi-provider configuration:

python
import fenic as fc
from fenic.api.session import Session
from fenic.api.session.config import SessionConfig, SemanticConfig
from fenic.core.types.datatypes import MarkdownType
from fenic.api.functions import semantic, markdown, text

config = SessionConfig(
    app_name="policy_analysis",
    semantic=SemanticConfig(
        language_models={
            "nano": fc.OpenAILanguageModel(
                model_name="gpt-4o-nano",
                rpm=500,
                tpm=200_000
            ),
            "mini": fc.OpenAILanguageModel(
                model_name="gpt-4o-mini",
                rpm=300,
                tpm=150_000
            ),
            "claude": fc.AnthropicLanguageModel(
                model_name="claude-opus-4-0",
                rpm=100,
                input_tpm=100_000,
                output_tpm=100_000
            ),
        },
        default_language_model="mini",
    ),
)

session = Session.get_or_create(config)

Model aliases abstract provider-specific details. Rate limiting parameters (rpm for requests per minute, tpm for tokens per minute) prevent throttling. Fenic automatically batches requests and implements retry logic.

Schema-Driven Policy Document Extraction

Schema-driven extraction eliminates regex patterns and custom parsing logic by defining output structure once using Pydantic models.

Policy Document Schema Definition

Create schemas that capture required structure:

python
from pydantic import BaseModel, Field
from typing import List, Literal

class Requirement(BaseModel):
    category: Literal["mandatory", "recommended", "prohibited"]
    description: str = Field(description="What is required or prohibited")
    applies_to: List[str] = Field(description="Who this requirement affects")
    effective_date: str = Field(description="When this takes effect")
    consequences: str = Field(description="What happens if not followed")

class PolicyDocument(BaseModel):
    title: str = Field(description="Official policy title")
    document_id: str = Field(description="Policy identification number")
    department: str = Field(description="Issuing department or agency")
    effective_date: str = Field(description="When policy becomes active")
    requirements: List[Requirement]
    definitions: List[str] = Field(description="Key terms defined in policy")
    supersedes: List[str] = Field(description="Previous policies this replaces")

Field descriptions guide extraction. The schema functions as both documentation and validation.

Document Processing Implementation

Load documents from storage and extract structured data:

python
import fenic.api.functions as fc

# Load policy documents
policies_df = session.read.docs(
    "policies/**/*.md",
    content_type="markdown",
    recursive=True
)

# Cast to MarkdownType for structure-aware processing
policies_df = policies_df.with_column(
    "structured_content",
    fc.col("content").cast(MarkdownType)
)

# Extract structured policy information
extracted_policies = policies_df.with_column(
    "policy_data",
    semantic.extract(
        fc.col("structured_content"),
        PolicyDocument,
        model_alias="mini"
    )
)

# Unnest extracted structure into columns
flat_policies = extracted_policies.unnest("policy_data")

# Explode requirements list into separate rows
requirements_df = flat_policies.select(
    fc.col("file_path"),
    fc.col("title"),
    fc.col("department"),
    fc.col("effective_date"),
    fc.explode(fc.col("requirements")).alias("req")
)

# Access nested fields
final_requirements = requirements_df.select(
    fc.col("title").alias("policy_title"),
    fc.col("department"),
    fc.col("req.category").alias("requirement_type"),
    fc.col("req.description").alias("requirement"),
    fc.col("req.applies_to").alias("affected_parties"),
    fc.col("req.consequences").alias("non_compliance_impact")
)

final_requirements.show()

The pipeline reads files, casts content to MarkdownType for structure-aware processing, extracts structured information using schemas, unnests nested structures, and explodes lists into rows.

Semantic-Aware Document Chunking

Policy documents often exceed model context windows. Native markdown support enables structure-preserving chunking:

python
from fenic.api.functions import markdown

chunked_policies = policies_df.select(
    fc.col("file_path"),
    fc.col("title"),
    markdown.extract_header_chunks(
        "structured_content",
        header_level=2
    ).alias("sections")
).explode("sections")

# Extract from each section
section_analysis = chunked_policies.with_column(
    "section_data",
    semantic.extract(
        fc.col("sections.content"),
        Requirement,
        model_alias="mini"
    )
)

The extract_header_chunks function splits documents at header boundaries, preserving semantic context rather than splitting mid-sentence.

Transcript Processing with Temporal Awareness

Fenic's TranscriptType handles multiple formats with unified processing while maintaining speaker identity, timestamps, and conversation flow.

Multi-Format Transcript Parsing

Fenic supports SRT, WebVTT, and generic transcript formats:

python
from fenic.api.functions import text

# Load transcript files
transcripts_df = session.read.docs(
    ["meetings/**/*.srt", "calls/**/*.vtt", "interviews/**/*.txt"],
    content_type="markdown",
    recursive=True
)

# Detect format from file extension
formatted_df = transcripts_df.with_column(
    "format",
    fc.when(fc.col("file_path").like("%.srt"), fc.lit("srt"))
    .when(fc.col("file_path").like("%.vtt"), fc.lit("webvtt"))
    .otherwise(fc.lit("generic"))
)

# Parse based on format
parsed_df = formatted_df.select(
    fc.col("file_path"),
    text.parse_transcript(
        fc.col("content"),
        fc.col("format")
    ).alias("segments")
)

# Explode segments into rows
segments_df = parsed_df.select(
    fc.col("file_path"),
    fc.explode(fc.col("segments")).alias("seg")
).select(
    fc.col("file_path"),
    fc.col("seg.index").alias("sequence"),
    fc.col("seg.speaker").alias("speaker"),
    fc.col("seg.start_time").alias("start_seconds"),
    fc.col("seg.end_time").alias("end_seconds"),
    fc.col("seg.content").alias("text")
)

segments_df.show()

The unified schema provides consistent access to speaker, timestamps, and content regardless of original format.

Structured Information Extraction from Conversations

Define schemas for conversation analysis:

python
class MeetingDecision(BaseModel):
    decision: str = Field(description="What was decided")
    rationale: str = Field(description="Why this decision was made")
    responsible_party: str = Field(description="Who will execute this")
    deadline: str = Field(description="When this must be completed")
    dependencies: List[str] = Field(description="What must happen first")

class PolicyDiscussion(BaseModel):
    policy_area: str = Field(description="Which policy is discussed")
    concerns_raised: List[str] = Field(description="Issues mentioned")
    proposed_changes: List[str] = Field(description="Suggested modifications")
    consensus_level: Literal["unanimous", "majority", "divided", "no_consensus"]

Apply extraction to transcript segments:

python
# Extract decisions from meeting segments
decisions_df = segments_df.filter(
    semantic.predicate(
        "Does this segment contain a decision or action item? Text: {{text}}",
        text=fc.col("text")
    )
)
).with_column(
    "decision_data",
    fc.semantic.extract(
        fc.col("text"),
        MeetingDecision,
        model_alias="mini"
    )
)

# Unnest for analysis
final_decisions = decisions_df.select(
    fc.col("file_path"),
    fc.col("speaker"),
    fc.col("start_seconds"),
    fc.col("decision_data.decision").alias("what"),
    fc.col("decision_data.responsible_party").alias("who"),
    fc.col("decision_data.deadline").alias("when"),
    fc.col("decision_data.rationale").alias("why")
)

final_decisions.write.csv("meeting_decisions.csv")

Semantic predicates filter segments to only those containing decisions, reducing extraction costs by processing fewer rows.

Semantic Analysis Patterns

Document Classification

Categorize documents without training custom models:

python
from fenic.api.functions import semantic

classified_policies = flat_policies.with_column(
    "category",
    semantic.classify(
        fc.col("structured_content"),
        [
            "Data Privacy",
            "Information Security",
            "Acceptable Use",
            "Records Retention",
            "Code of Conduct",
            "Procurement",
            "Travel and Expenses"
        ],
        model_alias="nano"
    )
)

# Aggregate by category
category_summary = classified_policies.group_by("category").agg(
    fc.count("*").alias("policy_count"),
    fc.collect_list("title").alias("policies")
)

category_summary.show()

Sentiment Analysis on Stakeholder Feedback

Track sentiment across policy discussions:

python
sentiment_df = segments_df.with_column(
    "sentiment",
    semantic.analyze_sentiment(fc.col("text"))
)

# Aggregate sentiment by speaker
speaker_sentiment = sentiment_df.group_by("speaker").agg(
    fc.count("*").alias("total_segments"),
    fc.sum(
        fc.when(fc.col("sentiment") == "positive", 1).otherwise(0)
    ).alias("positive_count"),
    fc.sum(
        fc.when(fc.col("sentiment") == "negative", 1).otherwise(0)
    ).alias("negative_count"),
    fc.sum(
        fc.when(fc.col("sentiment") == "neutral", 1).otherwise(0)
    ).alias("neutral_count")
)

# Calculate sentiment ratio
speaker_sentiment = speaker_sentiment.with_column(
    "positivity_ratio",
    fc.col("positive_count") / fc.col("total_segments")
)

speaker_sentiment.order_by(fc.col("positivity_ratio").desc()).show()

Temporal Sentiment Analysis

Track sentiment evolution during meetings:

python
# Create 5-minute time buckets
windowed_sentiment = sentiment_df.with_column(
    "time_bucket",
    (fc.col("start_seconds") / 300).cast("integer") * 300
)

temporal_sentiment = windowed_sentiment.group_by(
    "file_path",
    "time_bucket"
).agg(
    fc.count("*").alias("segment_count"),
    fc.avg(
        fc.when(fc.col("sentiment") == "positive", 1)
        .when(fc.col("sentiment") == "negative", -1)
        .otherwise(0)
    ).alias("sentiment_score")
)

# Identify sentiment shifts
temporal_sentiment.order_by(
    "file_path",
    "time_bucket"
).show()

This pattern reveals when discussions become contentious or reach consensus.

Semantic Joins for Cross-Document Analysis

Link policy requirements with meeting discussions using semantic joins:

python
# Policy requirements DataFrame
policy_reqs = final_requirements.select(
    fc.col("policy_title"),
    fc.col("requirement")
)

# Meeting discussion segments
meeting_segments = segments_df.select(
    fc.col("file_path").alias("meeting_file"),
    fc.col("text").alias("discussion")
)

# Join on semantic similarity
policy_discussion_links = policy_reqs.semantic.join(
    other=meeting_segments,
    predicate="""
    Does this meeting discussion address or relate to this policy requirement?

    Policy Requirement: {{left_on}}
    Meeting Discussion: {{right_on}}

    Return true if the discussion mentions, questions, or proposes changes
    to the requirement.
    """,
    left_on=fc.col("requirement"),
    right_on=fc.col("discussion")
)

policy_discussion_links.select(
    fc.col("policy_title"),
    fc.col("requirement"),
    fc.col("meeting_file"),
    fc.col("discussion")
).show(truncate=100)

This identifies which policies are discussed in which meetings without requiring exact keyword matches.

Compliance Gap Detection

Identify policy requirements without corresponding implementation plans:

python
# Extract action items from transcripts
action_items_df = segments_df.filter(
    fc.semantic.predicate(
        "Is this an action item or implementation plan? Text: {{text}}",
        text=fc.col("text")
    )
).select(
    fc.col("text").alias("action_description")
)

# Semantic anti-join: requirements WITHOUT matching actions
compliance_gaps = final_requirements.semantic.join(
    other=action_items_df,
    predicate="""
    Does this action item address this policy requirement?

    Requirement: {{left_on}}
    Action: {{right_on}}
    """,
    left_on=fc.col("requirement"),
    right_on=fc.col("action_description"),
    join_type="left_anti"
)

# Requirements without implementation plans
compliance_gaps.select(
    fc.col("policy_title"),
    fc.col("requirement_type"),
    fc.col("requirement")
).show()

Semantic Aggregation

Generate summaries that preserve context:

python
# Summarize policy discussions by topic
discussion_summaries = segments_df.with_column(
    "topic",
    semantic.classify(
        fc.col("text"),
        ["Data Security", "Employee Rights", "Budget", "Timeline", "Other"],
        model_alias="nano"
    )
).group_by("file_path", "topic").agg(
    semantic.reduce(
        "Summarize the key points discussed about this topic",
        fc.col("text"),
        order_by=fc.col("start_seconds"),
        model_alias="mini"
    ).alias("topic_summary")
)

discussion_summaries.show(truncate=150)

The semantic.reduce operator aggregates text semantically while maintaining temporal order, producing coherent summaries that reflect conversation flow.

Production Deployment

Cost Optimization Through Model Selection

Use appropriate model sizes:

python
config = SessionConfig(
    semantic=SemanticConfig(
        language_models={
            "nano": fc.OpenAILanguageModel("gpt-4o-nano", rpm=500, tpm=200_000),
            "mini": fc.OpenAILanguageModel("gpt-4o-mini", rpm=300, tpm=150_000),
            "claude": fc.AnthropicLanguageModel("claude-opus-4-0", rpm=100)
        },
        default_language_model="mini"
    )
)

# Use nano for simple classification
.semantic.classify(col, classes, model_alias="nano")

# Use mini for structured extraction
.semantic.extract(col, schema, model_alias="mini")

# Use claude only for advanced reasoning
.semantic.join(..., model_alias="claude")

Strategic model selection reduces total processing costs by 60-80% while maintaining quality for appropriate tasks.

Batch Processing

Process documents in batches to amortize fixed costs:

python
# Process large document sets
large_policy_set = session.read.docs(
    "s3://policy-bucket/**/*.pdf",
    content_type="markdown",
    recursive=True
)

# Automatic batching of semantic operations
processed = (
    large_policy_set
    .with_column("extracted", fc.semantic.extract("content", PolicyDocument))
    .unnest("extracted")
    .filter(fc.col("effective_date") > "2024-01-01")
)

# Write results to lakehouse
processed.write.parquet("s3://processed-policies/")

Automatic batching groups API calls efficiently, maximizing throughput while respecting rate limits through self-throttling.

Caching for Iterative Development

Cache expensive operations:

python
# Extract structure once, cache for multiple analyses
base_extraction = (
    policies_df
    .with_column("policy_data", fc.semantic.extract("content", PolicyDocument))
    .cache()
)

# Run multiple analyses on cached data
compliance_analysis = base_extraction.filter(
    fc.col("policy_data.category") == "mandatory"
)

risk_analysis = base_extraction.filter(
    fc.semantic.predicate(
        "Does this policy have compliance risk? {{content}}",
        content=fc.col("content")
    )
)

Caching prevents redundant API calls when running multiple analyses on the same extracted data.

Monitoring and Observability

Track costs and performance:

python
result = processed.collect()
metrics = result.metrics()

print(f"Total tokens used: {metrics.lm_metrics.total_tokens}")
print(f"Total cost: ${metrics.lm_metrics.total_cost}")
print(f"Execution time: {metrics.execution_time}s")

# Per-operation metrics
for op_metric in metrics.operator_metrics:
    if op_metric.cost > 5.0:
        print(f"High cost operator: {op_metric.name}")
        print(f"  Cost: ${op_metric.cost}")
        print(f"  Duration: {op_metric.duration}s")

Built-in metrics provide observability into LLM operations for targeted optimization.

Error Handling

Production pipelines require robust error handling:

python
# Add error handling for extraction failures
safe_extraction = policies_df.with_column(
    "extracted",
    fc.when(
        fc.col("content").is_not_null() & (fc.length(fc.col("content")) > 100),
        fc.semantic.extract(fc.col("content"), PolicyDocument, model_alias="mini")
    ).otherwise(fc.lit(None))
)

# Separate successful extractions from failures
successful = safe_extraction.filter(fc.col("extracted").is_not_null())
failed = safe_extraction.filter(fc.col("extracted").is_null())

# Log failures
failed.select(
    fc.col("file_path"),
    fc.lit("Extraction failed - content too short or null").alias("error")
).write.csv("extraction_errors.csv")

# Process successful extractions
successful.unnest("extracted").write.parquet("processed_policies.parquet")

Automatic retry logic handles transient failures, while explicit error handling ensures graceful degradation.

End-to-End Policy Analysis Pipeline

Production-ready implementation:

python
import fenic as fc
from fenic.api.session import Session
from fenic.api.session.config import SessionConfig, SemanticConfig
from fenic.core.types.datatypes import MarkdownType
import fenic.api.functions as fc_funcs
from fenic.api.functions import semantic, text
from pydantic import BaseModel, Field
from typing import List, Literal

# Schema definitions
class PolicyRequirement(BaseModel):
    category: Literal["mandatory", "recommended", "prohibited"]
    description: str
    applies_to: List[str]
    effective_date: str

class PolicyDocument(BaseModel):
    title: str
    department: str
    requirements: List[PolicyRequirement]

class MeetingAction(BaseModel):
    action: str
    owner: str
    deadline: str

# Initialize session
config = SessionConfig(
    app_name="policy_compliance_analysis",
    semantic=SemanticConfig(
        language_models={
            "nano": fc.OpenAILanguageModel("gpt-4o-nano", rpm=500, tpm=200_000),
            "mini": fc.OpenAILanguageModel("gpt-4o-mini", rpm=300, tpm=150_000),
        },
        default_language_model="mini"
    )
)
session = Session.get_or_create(config)

# Load and process policy documents
policies = (
    session.read.docs("policies/**/*.md", content_type="markdown", recursive=True)
    .with_column("structured", fc_funcs.col("content").cast(MarkdownType))
    .with_column("policy_data", semantic.extract("structured", PolicyDocument, model_alias="mini"))
    .cache()
)

# Extract requirements
requirements = (
    policies
    .unnest("policy_data")
    .select(
        fc_funcs.col("file_path"),
        fc_funcs.col("title"),
        fc_funcs.col("department"),
        fc_funcs.explode(fc_funcs.col("requirements")).alias("req")
    )
    .select(
        fc_funcs.col("title").alias("policy"),
        fc_funcs.col("req.category").alias("type"),
        fc_funcs.col("req.description").alias("requirement")
    )
)

# Load and process meeting transcripts
transcripts = (
    session.read.docs("meetings/**/*.srt", content_type="markdown", recursive=True)
    .select(
        fc_funcs.col("file_path"),
        text.parse_transcript(fc_funcs.col("content"), "srt").alias("segments")
    )
    .select(
        fc_funcs.col("file_path"),
        fc_funcs.explode(fc_funcs.col("segments")).alias("seg")
    )
    .select(
        fc_funcs.col("file_path"),
        fc_funcs.col("seg.speaker").alias("speaker"),
        fc_funcs.col("seg.content").alias("text")
    )
)

# Extract action items
actions = (
    transcripts
    .filter(
        semantic.predicate(
            "Is this an action item or commitment? Text: {{text}}",
            text=fc_funcs.col("text")
        )
    )
    .with_column("action_data", semantic.extract("text", MeetingAction, model_alias="mini"))
    .select(
        fc_funcs.col("file_path").alias("meeting"),
        fc_funcs.col("action_data.action").alias("action")
    )
)

# Identify compliance gaps
gaps = requirements.semantic.join(
    other=actions,
    predicate="""
    Does this action address this requirement?
    Requirement: {{left_on}}
    Action: {{right_on}}
    """,
    left_on=fc_funcs.col("requirement"),
    right_on=fc_funcs.col("action"),
    join_type="left_anti"
)

# Generate compliance report
gaps.select(
    fc_funcs.col("policy"),
    fc_funcs.col("type"),
    fc_funcs.col("requirement")
).write.csv("compliance_gaps_report.csv")

# Summary statistics
summary = requirements.group_by("policy", "type").agg(
    fc_funcs.count("*").alias("requirement_count")
)

summary.show()

# Display metrics
result = gaps.collect()
metrics = result.metrics()
print(f"\nProcessing completed:")
print(f"Total cost: ${metrics.lm_metrics.total_cost}")
print(f"Total tokens: {metrics.lm_metrics.total_tokens}")
print(f"Execution time: {metrics.execution_time}s")

session.stop()

This pipeline loads policy documents, extracts structured requirements, processes meeting transcripts, identifies action items, performs semantic matching to find compliance gaps, and generates reports with automatic batching, error handling, and cost tracking.

Performance and Scalability

Lakehouse-Native Architecture

Lakehouse-native design eliminates data movement:

python
# Read directly from data lake
policies = session.read.parquet("s3://policy-lake/raw/")

# Process with semantic operations
processed = policies.semantic.extract(...)

# Write back to lake (Fenic supports parquet format for writing)
processed.write.parquet("s3://policy-lake/processed/policies.parquet")

Local Development with Cloud Deployment

Develop locally, deploy to cloud without code changes:

python
# Local development
local_policies = session.read.docs("local_data/*.md")
local_processed = local_policies.semantic.extract(...)

# Cloud deployment - same code, different path
cloud_policies = session.read.docs("s3://policies/**/*.md")
cloud_processed = cloud_policies.semantic.extract(...)

Zero-code-change deployment from prototype to production.

Multi-Provider Support

Avoid vendor lock-in:

python
config = SessionConfig(
    semantic=SemanticConfig(
        language_models={
            "openai": fc.OpenAILanguageModel("gpt-4o-mini"),
            "claude": fc.AnthropicLanguageModel("claude-opus-4-0"),
            "gemini": fc.GoogleVertexLanguageModel("gemini-2.0-flash"),
        },
        default_language_model="openai"
    )
)

# Switch providers without changing pipeline code
.semantic.extract(col, schema, model_alias="claude")

Industry Applications

Financial Services Compliance

Banks process regulatory documents and board meeting transcripts to ensure compliance. Semantic extraction identifies requirements, semantic joins link requirements to control implementations, and gap analysis flags compliance risks before audits.

Healthcare Policy Management

Healthcare organizations manage HIPAA compliance documents, patient privacy policies, and clinical protocol updates. Schema-driven extraction transforms policy documents into structured datasets that integrate with clinical systems.

Government and Public Sector

Government agencies process legislative documents, public hearing transcripts, and policy proposals. Semantic intelligence enables cross-referencing between proposed legislation and existing regulations, identifies conflicts or overlaps, and generates summaries.

Enterprise Policy Compliance

Organizations maintain hundreds of internal policies covering data security, employee conduct, procurement rules, and operational procedures. Semantic analysis automates policy review, identifies outdated requirements, and ensures policy communications reach affected stakeholders.

Legal and Regulatory Analysis

Law firms and compliance consultancies analyze regulatory filings, court transcripts, and policy documentation. Semantic operators enable precedent analysis, regulatory change tracking, and automated brief generation.

Implementation Approach

Start with a focused pilot:

Select 10-20 representative policy documents and transcripts
Define schemas for specific domain requirements
Build a pipeline processing sample dataset
Validate extraction accuracy and iterate on schemas
Monitor costs and optimize model selection
Scale to full production dataset

Declarative pipelines built for small pilots directly translate to production systems. The same code that processes 20 documents handles 20,000 with automatic batching, error handling, and cost optimization.

Resources

Load and process policy documents

policies = ( session.read.docs("policies/**/*.md", content_type="markdown", recursive=True) .with_column("structured", fc_funcs.col("content").cast(MarkdownType)) .with_column("policy_data", semantic.extract("structured", PolicyDocument, model_alias="mini")) .cache() ) How to Analyze Policy Docum ... fcf080829937f16d725abde5.md External Displaying How to Analyze Policy Documents and Transcripts wi 290df41efcf080829937f16d725abde5.md.