Roadmap

This roadmap outlines planned enhancements to the Signal Database. Timelines are estimates and subject to change based on customer feedback.

📬

Feedback welcome: Have a signal you'd like to see or a schema improvement that would help? Contact [email protected]

Quick Navigation

Now (January–February 2026)

🔧 Schema Standardization

{
  "data": {
    "competitors_mentioned": ["Salesforce", "HubSpot"],
    "competitors_mentioned_domains": ["salesforce.com", "hubspot.com"]
  }
}

{
  "data": {
    "moderation_score": 0.95,
    "summary": "..."
  }
}

{
  "data": {
    "confidence": "high",
    "confidence_score": 0.92
  }
}

📦 Delivery Improvements

{
  "drop_date": "2026-01-15T00:00:00Z",
  "buckets_updated": ["hiring-velocity", "job-changes", "news"],
  "record_counts": {
    "hiring-velocity": 145000,
    "job-changes": 89000,
    "news": 12500
  }
}

See Delivery for current bucket structure details.

⚡ Refresh Frequency Improvements

Our goal is to move all signal types to weekly refresh, then prioritize high-value signals for daily delivery.

Daily refresh priority: Once weekly refresh is achieved across all categories, we will prioritize daily delivery for high-velocity signals like LinkedIn Posts and News.

See Signal Catalog for current refresh frequencies.

📊 Monitoring Pool Expansion

We're significantly expanding the number of companies and contacts in our monitoring pool across all signal categories.

This expansion improves coverage for:

• Mid-market and SMB companies
• International markets (EMEA, APAC)
• Emerging industries and verticals

See Signal Catalog for current coverage statistics by signal type.

🔗 Entity Resolution Improvements

We're actively improving the resolution quality of core company and contact fields included with each signal.

This work is actively in progress and will roll out incrementally across signal types.

🏭 Industry Normalization

We're standardizing the industries field across all signals. Currently, industry values vary by source (e.g., SEC filings use different industry classifications than LinkedIn-derived data).

Target: A normalized taxonomy of ~150 industries applied consistently across all signal types, with mapping to common standards (NAICS, SIC, LinkedIn industries).

This will enable more reliable industry-based filtering and segmentation.

Next (March–April 2026)

🕸️ Company Connections Flat File

A new bulk data product: the complete relationship graph for all companies in our database.

{
  "company_domain": "stripe.com",
  "connections": [
    {
      "connected_domain": "shopify.com",
      "connection_type": "customer",
      "confidence": 0.92,
      "first_seen": "2023-01-15",
      "last_seen": "2026-01-10",
      "source_url": "https://stripe.com/customers/shopify"
    },
    {
      "connected_domain": "adyen.com",
      "connection_type": "competitor",
      "confidence": 0.88,
      "first_seen": "2022-06-01",
      "last_seen": "2026-01-10",
      "source_url": "https://g2.com/compare/stripe-vs-adyen"
    },
    {
      "connected_domain": "sequoiacap.com",
      "connection_type": "investor",
      "confidence": 0.99,
      "first_seen": "2021-03-01",
      "last_seen": "2026-01-10",
      "source_url": "https://www.sequoiacap.com/companies/stripe"
    }
  ]
}

Connection types: competitor · customer · vendor · partner · investor

Enables:

• Resolving competitors_mentioned in earnings transcripts to full company records
• Building competitive conquest campaigns
• Identifying shared investors or vendors

See Competitive Companies for current API-based access.

🤝 Relationship Classification on LinkedIn

For LinkedIn comments, we'll classify the relationship between commenter and poster:

{
  "signal_type": "linkedin-comment",
  "data": {
    "relationship_type": "competitor",
    "relationship_confidence": 0.85,
    "commenter_company_domain": "hubspot.com",
    "poster_company_domain": "salesforce.com"
  }
}

Relationship types: colleague · competitor · vendor · customer · friend · recruiter · unknown

This helps filter high-intent engagement from noise.

📚 Historical Backfill (12 Months)

Expanding historical access for all signal types:

Enables:

• Trend analysis ("hiring velocity ↑ 3 consecutive months")
• Predictive model training
• Better sampling for coverage analysis

🏷️ Cross-Signal Subtype Standardization

We're standardizing subtypes across signal sources. The same business event should have the same subtype regardless of source.

This enables cross-signal deduplication and unified filtering.

Later (Q2 2026)

🆕 New Data Sources

New review sources will follow the G2 Reviews schema pattern and be unified under a product-reviews signal type with a source field for filtering.

📰 News Signal Enrichment

Expanding the metadata extracted from news events to match the depth of our SEC filing and Reddit signals:

{
  "signal_type": "news",
  "signal_subtype": "funding",
  "data": {
    "summary": "Acme Corp raises $50M Series B",
    "amount": "$50M",
    "amount_normalized": 50000000,
    "financing_type": "Series B",
    "investors": ["Sequoia Capital", "Andreessen Horowitz"],
    "investor_domains": ["sequoiacap.com", "a16z.com"],
    "lead_investor": "Sequoia Capital",
    "use_of_funds": "Expand AI infrastructure and grow engineering team",
    "competitors_mentioned": ["Competitor Corp"],
    "competitors_mentioned_domains": ["competitor.com"],
    "technologies_mentioned": ["AI", "Machine Learning"]
  }
}

This brings news signals to parity with the rich metadata already available in 10-K, Earnings Transcripts, and Reddit signals.

📋 Job Posting Content Analysis

A new signal type that extracts insights from the content of job postings, not just hiring velocity:

{
  "signal_type": "job-posting-content",
  "signal_subtype": "tech_stack_signal",
  "association": "company",
  "company": {
    "name": "Acme Corp",
    "domain": "acme.com"
  },
  "data": {
    "summary": "Hiring for Snowflake and dbt expertise across 5 open roles",
    "technologies_mentioned": ["Snowflake", "dbt", "Airflow", "Python"],
    "tools_mentioned": ["Salesforce", "HubSpot", "Tableau"],
    "pain_points_mentioned": ["scaling data infrastructure", "real-time analytics"],
    "departments": ["engineering", "data"],
    "seniority_levels": ["senior", "staff"],
    "role_count": 5,
    "compensation_range": {
      "low": 150000,
      "high": 200000,
      "currency": "USD"
    }
  }
}

Available subtypes:

Why it matters: If a company is hiring for "Snowflake engineers," that's a technographic signal. If they mention "scaling challenges," that's a pain point.

🎤 Conference & Event Signals

Track company participation in industry events:

{
  "signal_type": "event",
  "signal_subtype": "conference_sponsoring",
  "association": "company",
  "company": {
    "name": "Acme Corp",
    "domain": "acme.com"
  },
  "data": {
    "summary": "Acme Corp is a Gold sponsor at SaaStr Annual 2026",
    "event_name": "SaaStr Annual 2026",
    "event_date": "2026-09-15",
    "event_location": "San Francisco, CA",
    "participation_type": "sponsor",
    "sponsorship_level": "Gold",
    "source_url": "https://saastrannual.com/sponsors"
  }
}

Available subtypes:

👔 Executive Movement Graph

Beyond individual job changes, track the executive network:

{
  "company_domain": "acme.com",
  "executive_connections": {
    "alumni_sources": [
      {
        "company_domain": "google.com",
        "executive_count": 3,
        "titles": ["CTO", "VP Engineering", "Director of Product"]
      }
    ],
    "alumni_destinations": [
      {
        "company_domain": "startup.io",
        "executive_count": 2,
        "titles": ["CEO", "COO"]
      }
    ],
    "board_overlap": [
      {
        "company_domain": "portfolio-co.com",
        "shared_board_members": ["Jane Smith"]
      }
    ]
  }
}

Enables:

• "Their new CTO came from your customer Google" — warm intro signal
• Track where departing executives go
• Identify companies with shared board members or investors

📜 Regulatory & Compliance Signals

New subtypes for SEC filings focused on compliance and regulatory mentions:

Why it matters: Compliance is a major budget driver. "Company mentioned SOC2 in their 10-K" signals security spending.

🔌 API-First Signal Access

Real-time signal lookup via REST API:

curl -X GET "https://api.autobound.ai/signals/v1/company/stripe.com" \
  -H "X-API-KEY: your-key" \
  -d "signal_types=hiring-velocity,news,earnings" \
  -d "max_age_days=30"

Features:

• Fetch signals for a specific company or contact
• Filter by signal type, subtype, age
• Webhook subscriptions for new signals

This complements bulk delivery for point-of-action use cases.

🔄 Cross-Signal Deduplication

Improved deduplication across signal sources:

Deduplicated signals will include a related_signals array linking to other sources.

Platform Features (H2 2026)

{
  "company": {
    "autobound_id": "ab_company_12345",
    "domain": "stripe.com",
    "linkedin_url": "linkedin.com/company/stripe"
  }
}

Schema Standardization Summary

Fields Being Standardized

New Standard Fields (All Signals)

Subtype Consolidation & Expansion

We're both consolidating fragmented subtypes and launching many new ones.

Consolidation

Current State: 400+ subtypes across 24 categories with inconsistencies:

• Similar subtypes with different names (ceoChange vs leadershipChange vs hires)
• Legacy subtypes (talentRetentionConcerns vs glassdoorTalentRetentionConcerns)
• Inconsistent casing (hiringVelocity vs hiring_velocity)

Target State: Consistent naming convention → category_specificEvent

Examples: leadership_ceo_change · funding_series_a · acquisition_completed

Legacy subtypes will be supported via aliases for backward compatibility.

New Subtypes Coming

New subtypes will be documented in the Signal Catalog as they launch.

Stay Updated

Track all schema changes, new signals, and API updates in our Changelog.

Questions?