Fragile glue code is the silent killer of production AI systems. Teams spend months stitching together OCR pipelines, transcription services, LLM APIs, and data warehouses—creating maintenance nightmares that break at the slightest change. Industry surveys consistently report that most generative AI pilots struggle to reach production. For example, an MIT report noted that only about 5% of pilots deliver measurable business impact, underscoring the infrastructure challenges that block scaling.

Typedef.ai tackles this problem head-on with Fenic, an open-source DataFrame framework that treats inference as a first-class operation rather than a bolted-on afterthought. Instead of managing brittle microservices and hacky UDFs, developers get deterministic workflows built on non-deterministic models.

The Hidden Cost of Glue Code in AI Systems

1.1. What Makes Glue Code Fragile

Traditional AI pipelines require custom scripts to connect every component:

• OCR models to extract text from PDFs
• Transcription services for audio files
• Computer vision APIs for image analysis
• Multiple LLM providers with different rate limits
• Vector databases for embeddings
• Data warehouses for storage
• Custom microservices to orchestrate everything

Each connection point introduces:

• New failure modes and error handling requirements
• Latency from data serialization/deserialization
• Version compatibility issues between components
• Manual rate limit management across providers
• Context window chunking logic scattered throughout code
• Cost optimization hacks for balancing expensive vs cheap models

1.2. The Operational Nightmare

The glue code problem manifests in three critical ways:

1. Development Velocity Collapse

• Engineers spend 80% of time managing infrastructure, 20% building features
• Simple changes require updating multiple disconnected systems
• Testing becomes impossible with so many external dependencies

2. Production Failures at Scale

• Rate limit errors cascade through pipelines
• Model API changes break entire workflows
• Debugging requires tracing through dozens of custom scripts

3. Cost Explosion

• Duplicate API calls from poor caching strategies
• Expensive models used where cheaper ones would suffice
• No visibility into which operations drive costs

Why Legacy Data Platforms Fail for AI Workloads

2.1. Built for Rows and Columns, Not Inference

Traditional data engines assume structured, deterministic operations. They treat LLM calls as external black boxes through User Defined Functions (UDFs). This creates fundamental impedance mismatches:

python
import time
from openai import OpenAI
import pandas as pd

client = OpenAI()

def extract_sentiment(text):
    # Manual rate limiting
    time.sleep(0.1)

    try:
        response = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[{"role": "user", "content": f"Analyze sentiment: {text}"}]
        )
        return response.choices[0].message.content

    except Exception as e:
        # Manual retry logic (placeholder)
        return retry_with_backoff(extract_sentiment, text)

# Example DataFrame
df = pd.DataFrame({"text": ["I love this product!", "This is terrible."]})
df["sentiment"] = df["text"].apply(extract_sentiment)

The query engine has no visibility into what’s happening inside the UDF. It cannot:

• Batch API calls for efficiency
• Cache repeated inference patterns
• Optimize operation ordering
• Provide accurate cost estimates
• Handle rate limits intelligently

2.2. The Retrofitting Problem

Retrofitting creates:

• Architectural debt: Inference bolted onto systems designed for deterministic operations
• Abstraction leaks: LLM-specific concerns bleeding into application logic
• Performance bottlenecks: No ability to optimize across inference boundaries

Fenic’s Inference-First Architecture

3.1. Making Inference a First-Class Citizen

Fenic rebuilds the query engine from first principles with inference awareness baked in. Semantic operators like semantic.extract, semantic.filter, and semantic.join are native DataFrame operations, not external functions.

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

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

# Assuming df and claims_df are already DataFrames
results = (
    df
    .select("*", fc.semantic.extract(fc.col("policy_text"), PolicyInsight).alias("policy_insight"))
    .filter(fc.semantic.predicate(
        "{{ policy_insight }} has non-empty coverage gaps",
        policy_insight=fc.col("policy_insight")
    ))
    .semantic.join(
        other=claims_df,
        predicate="The policy {{ left_on }} is related to claim {{ right_on }}",
        left_on=fc.col("policy_id"),
        right_on=fc.col("claim_policy_ref")
    )
)

# Show or collect results
results.show()

The query engine understands exactly when inference happens. This enables:

• Automatic batching: Group API calls for maximum throughput
• Intelligent caching: Reuse inference results across pipeline stages
• Cost optimization: Identify opportunities to use smaller models
• Operation reordering: Minimize expensive operations
• Rate limit handling: Self-throttle based on provider limits

3.2. DataFrames Bring Structure to Chaos

Fenic’s core insight: AI workloads are fundamentally pipelines. They take inputs, reason over context, generate outputs, and log results—exactly what DataFrame APIs handle best.

DataFrames provide:

• Lineage tracking: Every column and row has traceable origins
• Columnar consistency: Structured data even from probabilistic operations
• Deterministic transformations: Model + prompt + input → output
• Lazy evaluation: Optimize entire pipelines before execution
• Type safety: Pydantic schemas eliminate runtime surprises

Eliminating Common Glue Code Patterns

Pattern 1: Document Processing Pipelines

Before (Fragile Glue Code):

python
# Scattered across multiple files and services
def process_documents(pdfs):
    texts = []

    for pdf in pdfs:
        # Manual OCR handling
        text = ocr_service.extract(pdf)
# Manual chunking
chunks = custom_chunk_function(text, max_tokens=1000)

# Manual rate limiting
for chunk in chunks:
    time.sleep(0.5)

    # Manual API calls
    summary = llm_api.summarize(chunk)
    texts.append(summary)

# Manual aggregation
return combine_summaries(texts)

After (Fenic - No Glue Code):

python
import fenic as fc
from pydantic import BaseModel
from typing import Literal

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

# Assuming df and claims_df are already DataFrames
results = (
    df
    .semantic.extract("policy_text", PolicyInsight)
    # Assuming filter takes a predicate expression or semantic query
    .semantic.filter("coverage_gaps IS NOT EMPTY")
    .semantic.join(
        other=claims_df,
        left_on="policy_id",
        right_on="claim_policy_ref"
    )
)

# Show or collect results
results.show()  # or results.collect()

Pattern 2: Multi-Provider Model Management

Before (Fragile Glue Code):

python
import time
import random

# Mock providers for demonstration
class OpenAIProvider:
    def complete(self, text):
        # Simulate API call
        return f"OpenAI response to: {text}"

class AnthropicProvider:
    def complete(self, text):
        # Simulate API call
        return f"Anthropic response to: {text}"

class RateLimiter:
    def wait(self):
        time.sleep(0.1)  # Simulate waiting for rate limit

class ModelOrchestrator:
    def __init__(self):
        self.openai = OpenAIProvider()
        self.anthropic = AnthropicProvider()

        self.rate_limiters = {
            self.openai: RateLimiter(),
            self.anthropic: RateLimiter()
        }

        self.retry_counts = {self.openai: 0, self.anthropic: 0}

    def call_model(self, text, task):
        # Manual model selection
        provider = self.openai if task == "extract" else self.anthropic

        # Manual rate limiting per provider
        self.rate_limiters[provider].wait()

        # Manual retry logic
        for attempt in range(3):
            try:
                if random.random() < 0.7 and attempt < 2:  # simulate failures
                    raise Exception("Temporary failure")
                return provider.complete(text)
            except Exception as e:
                self.retry_counts[provider] += 1
                time.sleep(2 ** attempt)

# Example usage
orchestrator = ModelOrchestrator()

print(orchestrator.call_model("Extract sentiment from this text", task="extract"))
print(orchestrator.call_model("Summarize this text", task="summarize"))

After (Fenic - Declarative Configuration):

python
import fenic as fc
from pydantic import BaseModel

# Configure multiple providers declaratively
config = fc.SessionConfig(
    semantic=fc.SemanticConfig(
        language_models={
            "fast": fc.OpenAILanguageModel(model_name="gpt-4o-mini", rpm=100, tpm=100000),
            "accurate": fc.AnthropicLanguageModel(model_name="claude-3-5-haiku-latest", rpm=50, input_tpm=100000, output_tpm=50000),
            "cheap": fc.GoogleVertexLanguageModel(model_name="gemini-2.0-flash", rpm=200, tpm=200000)
        },
        default_language_model="fast"
    )
)

# Create a session
session = fc.Session.get_or_create(config)

# Example DataFrame usage
df = session.read.csv("feedback.csv")

# Define schema
class Summary(BaseModel):
    summary: str

# Use the configured model declaratively
results = df.select(
    "*",
    fc.semantic.extract(fc.col("text"), Summary, model_alias="accurate").alias("summary_data")
)

results.show()

Pattern 3: Schema Extraction and Validation

Before (Fragile Glue Code):

python
import json
import random

# Mock LLM class to simulate API response
class MockLLM:
    def complete(self, prompt):
        # Simulate a JSON response
        fake_responses = [
            '{"name": "Alice", "age": 30, "status": "active"}',
            '{"name": "Bob", "age": "not a number", "status": "active"}',
            '{"name": "Charlie", "age": 45, "status": "unknown"}'
        ]
        return random.choice(fake_responses)

llm = MockLLM()

# Manual prompt engineering and validation
def extract_customer_data(text):
    prompt = """
Extract the following from the text:

- Name (string)
- Age (integer between 0-150)
- Status (one of: active, inactive, pending)

Return as JSON...
"""
    response = llm.complete(prompt + text)

    # Manual parsing
    try:
        data = json.loads(response)
    except:
        return None

    # Manual validation
    if not isinstance(data.get('age'), int):
        return None
    if data.get('status') not in ['active', 'inactive', 'pending']:
        return None

    return data

# Example usage
print(extract_customer_data("Customer Alice is 30 years old and active."))
print(extract_customer_data("Customer Bob might be invalid data."))

After (Fenic - Type-Safe Extraction):

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

# Define schema with validation rules
class CustomerData(BaseModel):
    name: str
    age: int = Field(ge=0, le=150)
    status: Literal["active", "inactive", "pending"]

# Setup a Fenic session (mock config for demo)
config = fc.SessionConfig(
    semantic=fc.SemanticConfig(
        language_models={
            "default": fc.OpenAILanguageModel(model_name="gpt-4o-mini", rpm=100, tpm=100000)
        }
    )
)

session = fc.Session.get_or_create(config)

# Mock DataFrame with customer text
df = session.create_dataframe({
    "text": [
        "Alice is 30 years old and active.",
        "Bob is 200 years old and inactive.",
        "Charlie is 40 and pending approval."
    ]
})

# Automatic extraction with type-safe validation
df_processed = df.select(
    "*",
    fc.semantic.extract(fc.col("text"), CustomerData).alias("customer_data")
)

df_processed.show()

Production-Ready Features Built In

Automatic Optimization

Fenic’s query engine optimizes entire pipelines before execution:

python
import fenic as fc
from pydantic import BaseModel

# Define schema for ticket extraction
class TicketSchema(BaseModel):
    customer_id: str
    issue: str
    sentiment: str  # e.g., "frustrated", "neutral", "satisfied"

# Create a Fenic session
config = fc.SessionConfig(
    semantic=fc.SemanticConfig(
        language_models={
            "default": fc.OpenAILanguageModel(model_name="gpt-4o-mini", rpm=100, tpm=100000)
        }
    )
)

session = fc.Session.get_or_create(config)

# Mock ticket data
df = session.create_dataframe({
    "priority": ["high", "low", "high"],
    "content": [
        "The app keeps crashing, I'm really annoyed!",
        "General feedback, nothing urgent.",
        "Payment failed again, this is frustrating!"
    ]
})

# Mock knowledge base
knowledge_base = session.create_dataframe({
    "solution_id": [1, 2],
    "solution_text": ["Restart the app", "Check payment settings"]
})

# Define pipeline lazily
pipeline = (
    df
    .filter(fc.col("priority") == "high")
    .select("*", fc.semantic.extract(fc.col("content"), TicketSchema).alias("ticket_info"))
    .filter(fc.semantic.predicate(
        "The sentiment {{ sentiment }} is frustrated",
        sentiment=fc.col("ticket_info.sentiment")
    ))
    .semantic.join(
        other=knowledge_base,
        predicate="The issue {{ left_on }} can be resolved by {{ right_on }}",
        left_on=fc.col("ticket_info.issue"),
        right_on=fc.col("solution_text")
    )
)

# Trigger optimized execution
result = pipeline.collect()

5.1. Native Unstructured Data Types

Instead of preprocessing mazes, Fenic provides specialized types:

• MarkdownType: Parse and extract structure from markdown
• TranscriptType: Handle SRT, WebVTT with speaker awareness
• JsonType: Manipulate nested JSON with JQ expressions
• DocumentPathType: Load PDFs, docs, and text files
• EmbeddingType: First-class support for vector operations

python
import fenic as fc
from pydantic import BaseModel
from typing import Optional

# Define schema for meeting action items
class MeetingActionItems(BaseModel):
    description: str
    owner: str
    due_date: Optional[str] = None

# Create session
config = fc.SessionConfig(
    semantic=fc.SemanticConfig(
        language_models={
            "default": fc.OpenAILanguageModel(model_name="gpt-4o-mini", rpm=100, tpm=100000)
        }
    )
)

session = fc.Session.get_or_create(config)

# Mock input DataFrame with transcript files
df = session.create_dataframe({
    "file": ["meeting1.srt", "meeting2.vtt"]
})

# Process meeting transcripts with speaker awareness
meetings = (
    df
    .with_column("transcript", fc.col("file").cast(fc.TranscriptType))
    .select("*", fc.semantic.extract(fc.col("transcript"), MeetingActionItems).alias("action_items"))
    .filter(fc.col("action_items.owner") == "Engineering")
)

result = meetings.collect()

Row-Level Lineage and Debugging

Every operation is traceable:

python
# Every operation is traceable
result = df.select(
    fc.semantic.map("Analyze sentiment: {{ text }}", text=fc.col("text"))
).collect()

# Access comprehensive metrics
print(result.metrics.total_lm_metrics.num_output_tokens)
print(result.metrics.total_lm_metrics.cost)
print(result.metrics.execution_time_ms)

Real-World Impact

6.1. Media Companies: Content Intelligence at Scale

A major content platform reports: “Typedef’s engine gives us a powerful way to blend traditional OLAP-style analysis with LLM inference in a single, unified workflow. We conduct large-scale content classification for labeling, grouping, and enriching articles semantically using high-level operators, without writing brittle glue code or managing separate inference infrastructure.”

6.2. Insurance: Policy Analysis in Days, Not Months

Matic transformed their operations: “Typedef lets us build and deploy semantic extraction pipelines across thousands of policies and transcripts in days not months. We’ve dramatically reduced the time it takes to eliminate errors caused by human analysis, significantly cut costs, and lowered our Errors and Omissions (E&O) risk.”

6.3. Enterprise Analytics: 100x Time Savings

An anonymous customer shares: “Typedef transforms our OLAP warehouse into a dynamic product-signal engine. Previously, product managers spent weeks manually processing data for basic queries. Now, they query and dive deep across diverse datasets, leveraging LLM categorizations and summarizations. This is 100x time savings.”

Getting Started with Fenic

Installation

python
pip install fenic

Basic Setup

python
import fenic as fc

# Configure providers
config = fc.SessionConfig(
    app_name="production_pipeline",
    semantic=fc.SemanticConfig(
        language_models={
            "default": fc.OpenAILanguageModel(model_name="gpt-4o-mini", rpm=100, tpm=100000)
        }
    )
)

session = fc.Session.get_or_create(config)

Your First Pipeline

python
from pydantic import BaseModel

class InsightSchema(BaseModel):
    summary: str
    key_points: list[str]
    sentiment: str

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

# Build pipeline - no glue code needed
insights = (
    df
    .select("*", fc.semantic.extract(fc.col("feedback"), InsightSchema).alias("insights"))
    .with_column("key_points_embedding", fc.semantic.embed(fc.col("insights.key_points").cast(fc.StringType)))
    .semantic.with_cluster_labels(
        by=fc.col("key_points_embedding"),
        num_clusters=5,
        label_column="cluster_label"
    )
    .group_by("cluster_label")
    .agg(fc.semantic.reduce("Summarize cluster themes", fc.col("feedback")))
)

insights.show()

7. Best Practices for Glue Code Elimination

1. Define Schemas Once

Use Pydantic models to eliminate prompt brittleness:

python
from pydantic import BaseModel, Field

class ExtractedData(BaseModel):
    """Single source of truth for data structure"""
    entities: list[str]
    relationships: dict[str, str]
    confidence: float = Field(ge=0, le=1)

# Reuse across entire pipeline
df.select(fc.semantic.extract(fc.col("text"), ExtractedData))

2. Leverage Lazy Evaluation

Build entire pipelines before execution:

python
# Define complex multi-stage pipeline
pipeline = (
    df
    .filter(condition1)
    .semantic.operation1()
    .join(other_df)
    .semantic.operation2()
    .cache()  # Explicit caching points
)

# Execute when ready
results = pipeline.collect()

3. Use Appropriate Models

Configure model tiers for cost optimization:

python
language_models = {
    "nano": fc.OpenAILanguageModel(model_name="gpt-4o-mini", rpm=100, tpm=100000),   # Fast, cheap
    "standard": fc.AnthropicLanguageModel(model_name="claude-3-5-haiku-latest", rpm=100, input_tpm=100000, output_tpm=50000),  # Balanced
    "power": fc.OpenAILanguageModel(model_name="gpt-4o", rpm=100, tpm=100000)  # Accurate
}

# Use appropriate model for each task
df.select("*", fc.semantic.map(
    "Classify {{ text }} into one of these categories",
    text=fc.col("text"),
    model_alias="nano"
).alias("category"))

df.select("*", fc.semantic.extract(
    fc.col("complex_doc"),
    Schema,
    model_alias="power"
).alias("extracted"))

From Local Development to Production Scale

Fenic enables seamless scaling from prototype to production:

Local Development:

python
# Develop and test locally
df = session.read.csv("local_data.csv")

processed = df.select(fc.semantic.extract(fc.col("text"), Schema).alias("extracted"))

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

Production Deployment:

python
# Same code, cloud execution
config = fc.SessionConfig(
    cloud=fc.CloudConfig(
        size=fc.CloudExecutorSize.MEDIUM
    )
)

session = fc.Session.get_or_create(config)

# Automatic scaling, no code changes
df = session.read.csv("s3://bucket/data/*.csv")

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

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

Join the Movement

Typedef is building the future of AI infrastructure—one where glue code becomes obsolete. The latest Fenic release features innovations that result in significantly less glue code, fewer brittle prompts, and cheaper, more reliable pipelines.

Resources to Get Started

• GitHub Repository: Explore the open-source framework
• Fenic 0.4.0 Release: Learn about the latest features
• Open Source Announcement: Read about Fenic’s journey

Cloud Platform Access

For enterprise-scale deployments, Typedef Cloud provides:

• Serverless execution without infrastructure management
• Support for advanced mixed AI workflows
• Web-based collaboration interface
• Advanced reporting and analytics
• Rapid iterative experimentation

Visit typedef.ai to request access.

Conclusion

Fragile glue code has plagued AI projects for too long, turning promising prototypes into production nightmares. Fenic eliminates this problem by making inference a first-class operation within the DataFrame abstraction developers already know.

The results speak for themselves: companies report 100x time savings, dramatic cost reductions, and the ability to ship AI workflows in days instead of months. By treating AI workloads as pipelines rather than scattered microservices, Fenic brings the reliability of traditional data processing to the probabilistic world of LLMs.

Stop fighting with glue code. Start building reliable AI systems with Fenic and Typedef.