How to Solve Data Fragmentation in Multimodal AI Inputs

Data fragmentation blocks production deployment of multimodal AI systems. When processing text, images, audio, video, and structured data together, traditional architectures create isolated pipelines where each data type requires separate handling. This fragmentation creates scattered preprocessing scripts, disconnected inference services, and brittle integration code that fails under production load.

The Data Fragmentation Problem

Root Causes

Multimodal AI systems handle diverse input types: PDFs requiring OCR, audio files needing transcription, images requiring vision analysis, and structured databases. Legacy data infrastructure treats each modality as a separate problem.

This separation creates three failure modes:

Architectural silos: OCR pipelines run independently from transcription services. Vector databases operate separately from data warehouses. Each component has distinct APIs, authentication, rate limits, and error handling.

Data movement overhead: Moving data between specialized systems consumes latency and compute. Serialization and deserialization at each boundary adds processing time and creates opportunities for data loss.

Context loss: Processing fragments across disconnected systems breaks semantic relationships between modalities. A product mentioned in meeting transcripts and support tickets appears in separate systems with no connection.

Impact on Development and Operations

Teams report spending 80% of development time managing infrastructure versus building features. Simple changes require updates across multiple disconnected systems. Testing becomes difficult with numerous external dependencies creating unpredictable failures.

Production deployments face cascading failures when rate limits trigger. Model API changes break workflows. Debugging requires tracing through dozens of custom scripts with limited visibility.

Cost optimization lacks unified visibility. Duplicate API calls waste resources through poor caching. Expensive models run where cheaper alternatives suffice. Teams lack metrics showing which operations drive spending.

Inference-First Architecture

Treating Inference as Native Data Operations

Fenic rebuilds the query engine with inference awareness built in. Rather than adding LLM calls onto systems designed for deterministic operations through User Defined Functions, semantic operations become native DataFrame primitives.

This shift enables the query engine to know exactly when inference happens:

Intelligent batching groups API calls for maximum throughput while respecting provider limits
Smart caching reuses inference results across pipeline stages without manual management
Cost optimization routes simple tasks to smaller, cheaper models while preserving accuracy
Operation reordering minimizes expensive inference calls

DataFrames for Multimodal Processing

The DataFrame abstraction brings structure to probabilistic systems. Even with stochastic inference across OCR, transcription, and LLM operations, DataFrames provide:

Lineage tracking: Every column and row maintains traceable origins, even from model outputs. This enables debugging by tracing individual records through transformations.

Columnar consistency: Summaries, embeddings, and scores maintain structured representations throughout the pipeline. This simplifies downstream processing.

Deterministic transformations: Wrapping inference in the pattern model + prompt + input → output enables caching, versioning, and debugging. The framework treats probabilistic operations as predictable components.

Unified Multimodal Processing Implementation

AI-Native Data Types

Fenic's type system eliminates preprocessing steps with first-class column types for multimodal content:

MarkdownType: Native markdown parsing with structure extraction as a DataFrame operation
TranscriptType: Handles SRT, WebVTT, and generic formats with speaker awareness and timestamps
JsonType: Manipulates nested JSON using JQ expressions within DataFrame operations
EmbeddingType: Fixed-length vectors with similarity operations as native functions
DocumentPathType: Loads PDFs, documents, and text files from local or remote sources

These types integrate with semantic operators, enabling sophisticated pipelines through a unified interface.

Processing Multiple Modalities in Single Pipelines

python
import fenic as fc
from pydantic import BaseModel, Field
from typing import List, Literal

class SupportAnalysis(BaseModel):
    issue_category: Literal["bug", "feature_request", "question"]
    sentiment: Literal["frustrated", "neutral", "satisfied"]
    priority_indicators: List[str]
    technical_context: str

# Configure session with multiple providers
config = fc.SessionConfig(
    semantic=fc.SemanticConfig(
        language_models={
            "fast": fc.OpenAILanguageModel(
                model_name="gpt-4o-mini",
                rpm=500,
                tpm=200_000
            ),
            "accurate": fc.AnthropicLanguageModel(
                model_name="claude-3-5-haiku-latest",
                rpm=200,
                input_tpm=100_000,
                output_tpm=50_000
            )
        },
        default_language_model="fast"
    )
)

session = fc.Session.get_or_create(config)

# Process multiple modalities in unified pipeline
results = (
    session.read.docs("/data/support_tickets/", content_type="markdown", recursive=True)
    .with_column("transcript", fc.col("file_path").cast(fc.TranscriptType))
    .with_column("markdown_content", fc.col("content"))
    .select(
        "*",
        fc.semantic.extract(
            fc.col("transcript"),
            SupportAnalysis,
            model_alias="accurate"
        ).alias("analysis")
    )
    .filter(fc.col("analysis.priority_indicators").size() > 0)
    .with_column(
        "embeddings",
        fc.semantic.embed(fc.col("analysis.technical_context"))
    )
)

results.show()

This pipeline processes transcripts, markdown documents, and structured data through a single DataFrame operation. The framework handles rate limiting, batching, and error recovery automatically.

Semantic Operations for Content Processing

Schema-Driven Extraction

The semantic.extract operator transforms unstructured text into structured data using Pydantic schemas:

python
from pydantic import BaseModel
from typing import Literal, List

class PolicyInsight(BaseModel):
    risk_level: Literal["low", "medium", "high", "critical"]
    coverage_gaps: List[str]
    recommendations: List[str]

# Extract structure from unstructured documents
policies = (
    df.select(
        "*",
        fc.semantic.extract(
            fc.col("policy_text"),
            PolicyInsight
        ).alias("insights")
    )
    .filter(fc.col("insights.risk_level") == "critical")
)

The schema serves as both documentation and validation.

Semantic Joins Across Modalities

Traditional joins require exact key matches. Semantic joins enable matching based on meaning:

python
# Join candidates with jobs based on semantic fit
prompt = """
Is this candidate a good fit for the job?

Candidate Background: {{left_on}}
Job Requirements: {{right_on}}

Consider:
- Technical skill alignment
- Experience level match
- Domain expertise
"""

matched = (
    applicants
    .semantic.join(
        other=jobs,
        predicate=prompt,
        left_on=fc.col("resume"),
        right_on=fc.col("job_description")
    )
    .order_by(fc.col("application_date"))
    .limit(10)
)

This handles nuanced matching that keyword-based systems cannot capture while maintaining DataFrame join semantics.

Multi-Provider Model Management

Configure multiple model providers declaratively, routing operations based on accuracy requirements and cost:

python
config = fc.SessionConfig(
    semantic=fc.SemanticConfig(
        language_models={
            "nano": fc.OpenAILanguageModel(
                model_name="gpt-4o-mini",
                rpm=500,
                tpm=200_000
            ),
            "standard": fc.AnthropicLanguageModel(
                model_name="claude-3-5-haiku-latest",
                rpm=200,
                input_tpm=100_000,
                output_tpm=50_000
            ),
            "power": fc.OpenAILanguageModel(
                model_name="gpt-4o",
                rpm=100,
                tpm=100_000
            )
        },
        default_language_model="standard"
    )
)

# Route operations to appropriate models
df.select(
    "*",
    # Fast, cheap model for simple classification
    fc.semantic.classify(
        fc.col("text"),
        classes=["support", "sales", "technical"],
        model_alias="nano"
    ).alias("category"),
    # Powerful model for extraction
    fc.semantic.extract(
        fc.col("complex_doc"),
        DetailedSchema,
        model_alias="power"
    ).alias("extracted")
)

The framework handles provider-specific rate limits, retry logic, and error patterns while exposing consistent interfaces.

Batch Processing and Parallel Operations

Async UDFs for Concurrent I/O

Fenic 0.4.0's async UDF support enables parallel API calls and database lookups across rows while maintaining type safety:

python
import fenic as fc
from fenic.core.types import IntegerType
import aiohttp

@fc.async_udf(
    return_type=IntegerType,
    max_concurrency=10,
    timeout_seconds=5,
    num_retries=2
)
async def enrich_customer_data(customer_id: int) -> int:
    async with aiohttp.ClientSession() as session:
        async with session.get(
            f"https://api.example.com/customer/{customer_id}"
        ) as resp:
            data = await resp.json()
            return data["lifetime_value"]

# Apply parallel enrichment
enriched = df.select(
    fc.col("customer_id"),
    enrich_customer_data(fc.col("customer_id")).alias("ltv")
)

This maximizes throughput on I/O-bound workloads while preserving DataFrame semantics. Configurable concurrency, automatic retries, and timeouts ensure production-safe execution.

Directory Content Loading

Process entire directories of multimodal content in single operations:

python
# Load all documents from directory
docs = session.read.docs(
    "/data/product_docs/",
    content_type="markdown",
    recursive=True
)

# Process all documents in batch
processed = (
    docs
    .with_column("sections", fc.markdown.extract_header_chunks(
        fc.col("content"),
        header_level=2
    ))
    .explode("sections")
    .select(
        "*",
        fc.semantic.extract(
            fc.col("sections.content"),
            DocumentationSchema
        ).alias("structured")
    )
)

The framework handles file loading and parallelization automatically.

Production Reliability

Automatic Optimization

Fenic's lazy evaluation enables query optimization across entire pipelines:

python
# Define pipeline
pipeline = (
    df
    .filter(fc.col("priority") == "high")
    .select(
        "*",
        fc.semantic.extract(fc.col("content"), TicketSchema).alias("ticket")
    )
    .filter(fc.semantic.predicate(
        "The sentiment {{sentiment}} indicates frustration",
        sentiment=fc.col("ticket.sentiment")
    ))
    .semantic.join(
        other=knowledge_base,
        predicate="Issue {{left_on}} matches solution {{right_on}}",
        left_on=fc.col("ticket.issue"),
        right_on=fc.col("solution_text")
    )
)

# Trigger optimized execution
results = pipeline.collect()

The query planner reorders operations to minimize expensive inference calls, batches similar operations, and applies caching automatically.

Cost and Performance Tracking

Built-in metrics provide visibility into inference costs:

python
# Query local metrics
metrics = session.table("fenic_system.query_metrics")

cost_analysis = (
    metrics
    .select(
        "query_id",
        "model",
        "latency_ms",
        "cost_usd",
        "num_input_tokens",
        "num_output_tokens"
    )
    .order_by(fc.col("cost_usd").desc())
)

cost_analysis.show()

This enables teams to identify optimization opportunities and track spending.

Error Handling

Production pipelines require robust error handling. The framework provides built-in retry logic, rate limiting, and error reporting:

python
# Access execution metrics
result = pipeline.collect()

# Access metrics from the QueryResult
print(f"Execution time: {result.metrics.execution_time}ms")
print(f"Rows processed: {result.metrics.rows_processed}")

# Note: Detailed token-level metrics are available in the session's query_metrics table
metrics_df = session.table("fenic_system.query_metrics")

The framework handles rate limits and transient API failures with comprehensive logging.

Production Results

RudderStack Case Study

RudderStack's implementation achieved production-scale results. The warehouse-native CDP processes unstructured inputs from sales calls, support tickets, and documentation.

Results:

95% reduction in PM time per triage task
90% first-pass category acceptance rate
Automatic citation of prospect signals and feedback
Semantic linking between issues and PRDs for explainable decisions

The system ingests support ticket threads, sales call transcripts, product documentation, and Notion PRDs into unified warehouse context. Semantic indexing creates connections across data sources, enabling the triage agent to classify requests, surface related work, and propose decisions with rationale.

Enterprise Analytics Results

Product managers previously spent weeks manually processing data for basic queries. With unified multimodal processing, they query and analyze diverse datasets using LLM categorizations, achieving 100x time savings.

Insurance Deployment

Insurance companies deploying semantic extraction pipelines across thousands of policies and transcripts report completion in days rather than months. The unified approach reduces errors from human analysis, cuts costs, and lowers operational risk.

Implementation Best Practices

Define Schemas Once

Use Pydantic models as single sources of truth:

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

class SupportTicket(BaseModel):
    customer_tier: Literal["free", "pro", "enterprise"]
    issue_type: Literal["bug", "feature", "question"]
    priority: int = Field(ge=1, le=5)
    technical_details: List[str]

# Reuse schema across pipeline
extracted = df.select(
    "*",
    fc.semantic.extract(fc.col("raw_text"), SupportTicket).alias("ticket")
)

validated = extracted.filter(fc.col("ticket.priority") >= 3)

This eliminates prompt brittleness and manual validation.

Use Lazy Evaluation

Build complete pipelines before execution:

python
# Define multi-stage pipeline
pipeline = (
    df
    .filter(initial_conditions)
    .select(
        "*",
        fc.semantic.extract(fc.col("content"), Schema).alias("extracted")
    )
    .join(reference_data, on="key_column")
    .with_column(
        "category",
        fc.semantic.classify(fc.col("extracted.field"), classes=["A", "B", "C"])
    )
    .cache()
)

# Execute when ready
results = pipeline.collect()

The optimizer works across the entire pipeline.

Route to Appropriate Models

Configure model tiers and route based on task requirements:

python
language_models = {
    "nano": fc.OpenAILanguageModel(
        model_name="gpt-4o-mini",
        rpm=500,
        tpm=200_000
    ),
    "standard": fc.AnthropicLanguageModel(
        model_name="claude-3-5-haiku-latest",
        rpm=200,
        input_tpm=100_000,
        output_tpm=50_000
    ),
    "power": fc.OpenAILanguageModel(
        model_name="gpt-4o",
        rpm=100,
        tpm=100_000
    )
}

# Route based on complexity
df.select(
    "*",
    fc.semantic.classify(
        fc.col("text"),
        classes=["A", "B", "C"],
        model_alias="nano"
    ).alias("category"),
    fc.semantic.extract(
        fc.col("document"),
        ComplexSchema,
        model_alias="power"
    ).alias("extracted")
)

This balances accuracy with cost.

Scaling to Production

Local Development

Develop and test pipelines locally with full engine capability:

python
# Local development
session = fc.Session.get_or_create()

df = session.read.csv("sample_data.csv")
processed = df.select(
    "*",
    fc.semantic.extract(fc.col("text"), Schema).alias("extracted")
)

processed.write.parquet("results.parquet")

Cloud Deployment

Same code scales to cloud execution:

python
# Production configuration
config = fc.SessionConfig(
    cloud=fc.CloudConfig(
        size=fc.CloudExecutorSize.MEDIUM
    )
)

session = fc.Session.get_or_create(config)

# Same code, cloud-scale execution
df = session.read.csv("s3://bucket/production_data/*.csv")
processed = df.select(
    "*",
    fc.semantic.extract(fc.col("text"), Schema).alias("extracted")
)

processed.write.parquet("s3://bucket/results/output.parquet")

Zero code changes between local testing and production.

Getting Started

Installation

bash
pip install fenic

Basic Configuration

python
import fenic as fc
from pydantic import BaseModel

# Configure providers
config = fc.SessionConfig(
    app_name="multimodal_pipeline",
    semantic=fc.SemanticConfig(
        language_models={
            "default": fc.OpenAILanguageModel(
                model_name="gpt-4o-mini",
                rpm=500,
                tpm=200_000
            )
        }
    )
)

session = fc.Session.get_or_create(config)

First Unified Pipeline

python
from pydantic import BaseModel
from typing import List

class ContentInsights(BaseModel):
    summary: str
    key_topics: List[str]
    sentiment: str

# Load data
df = session.read.csv("feedback.csv")

# Process through unified pipeline
from fenic.core.types import StringType

insights = (
    df
    .select(
        "*",
        fc.semantic.extract(
            fc.col("feedback"),
            ContentInsights
        ).alias("insights")
    )
    .with_column(
        "topic_embedding",
        fc.semantic.embed(
            fc.col("insights.key_topics").cast(StringType)
        )
    )
    .semantic.with_cluster_labels(
        by=fc.col("topic_embedding"),
        num_clusters=5,
        label_column="cluster"
    )
    .group_by("cluster")
    .agg(
        fc.semantic.reduce(
            "Summarize common themes",
            fc.col("feedback")
        )
    )
)
insights.show()

Related Resources

Summary

Data fragmentation in multimodal AI systems stems from architectural mismatches between traditional data infrastructure and inference-first workloads. Treating semantic operations as first-class DataFrame primitives eliminates brittle glue code while maintaining familiar development patterns.

The inference-first architecture provides automatic optimization, intelligent batching, and unified processing across text, images, audio, and structured data. Specialized data types handle multimodal content natively. Production features including automatic error handling, cost tracking, and lineage support ensure reliable deployment.

Organizations report 95% time reductions in workflows, 100x productivity gains on data analysis, and the ability to ship AI pipelines in days instead of months. Unifying fragmented preprocessing across a single DataFrame abstraction enables teams to build deterministic workflows on probabilistic models at production scale. How to Solve Data Fragmentati ... 1efcf080f7acd6d202cb1fbc9d.md External Displaying How to Solve Data Fragmentation in Multimodal AI I 2aadf41efcf080f7acd6d202cb1fbc9d.md.