Chaining Operations

Operations can be chained together to build complete workflows. Each step refines your data further.

Example scenario: A wizard rescuing magical creatures for a sanctuary.

class CreatureSighting(BaseModel):
    is_magical: bool = Field(description="True if genuinely magical")

screened = await screen(
    session=session,
    task="Keep only confirmed magical creatures, not mundane animals or tall tales",
    input=sightings,
    response_model=CreatureSighting,
)

Common Workflows

Here are practical examples of chaining 2-3 operations for everyday analyst tasks.

Consolidate messy lead lists

Problem: You have leads from a trade show, a purchased list, and your CRM export. Same companies appear under different names.

Workflow: Concatenate → Dedupe → Rank

# Combine sources
all_leads = pd.concat([trade_show, purchased_list, crm_export])

# Dedupe across sources
deduped = await dedupe(
    input=all_leads,
    equivalence_relation="Same company, accounting for Inc/LLC variations, abbreviations, and parent/subsidiary relationships",
)

# Prioritize for outreach
ranked = await rank(
    task="Score by likelihood to need our data integration product",
    input=deduped.data,
    field_name="fit_score",
)

Vet vendors before RFP

Problem: You have 200 potential vendors. Need to shortlist ones that meet basic requirements, then rank by fit.

Workflow: Screen → Rank

# Filter to qualified vendors
screened = await screen(
    task="Must have SOC2 certification, 50+ employees, and enterprise references",
    input=vendor_list,
    response_model=VendorQualification,
)

# Rank survivors by fit
ranked = await rank(
    task="Score by alignment with our technical requirements and budget constraints",
    input=screened.data,
    field_name="fit_score",
)

Match products to approved suppliers

Problem: Procurement has a list of software products in use. Need to check each against the approved vendor list—but product names don't match company names (Photoshop vs Adobe).

Workflow: Research → Merge

# First, enrich products with parent company info
enriched = await agent_map(
    task="Find the parent company that makes this software product",
    input=software_products,
)

# Now merge with approved vendors
matched = await merge(
    task="Match products to approved vendors by parent company",
    left_table=enriched.data,
    right_table=approved_vendors,
    merge_on_left="parent_company",
    merge_on_right="vendor_name",
)

# Flag unapproved software for review
unapproved = matched.data[matched.data["vendor_id"].isna()]

Enrich accounts before territory planning

Problem: CRM has company names but missing firmographic data. Need employee count and industry before assigning to sales territories.

Workflow: Research → Rank

# Enrich with firmographics
enriched = await agent_map(
    task="Find employee count, industry, and headquarters location",
    input=accounts,
)

# Score for territory assignment
ranked = await rank(
    task="Score by revenue potential based on company size and industry fit",
    input=enriched.data,
    field_name="territory_priority",
)

Dedupe research from multiple sources

Problem: You scraped company data from LinkedIn, Crunchbase, and news articles. Same companies appear with different descriptions.

Workflow: Concatenate → Dedupe

# Combine all research
all_research = pd.concat([linkedin_data, crunchbase_data, news_mentions])

# Consolidate into canonical records
deduped = await dedupe(
    input=all_research,
    equivalence_relation="""
        Same company if names match accounting for legal suffixes,
        or if they share the same website domain or headquarters address
    """,
)
# Result includes best available data from all sources