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AI Capabilities Roadmap

$140 billion in public benefits go unclaimed every year. This roadmap tracks the AI capabilities funded by the GitLab Foundation AI for Economic Opportunity grant to address this gap.

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The Four Pillars

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Shared AI Platform Layer (Cross-Cutting)

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Core Capabilities

• Model Router: Multi-provider routing, fallback, and cost/latency-aware selection.
• Prompt + Policy Registry: Versioned prompts, policy templates, and safe defaults per workflow.
• Evaluation Harness: Golden datasets, regression tests, and confidence calibration per document type.
• Observability: Per-request traces, latency, cost, and error analytics tied to submissions.
• PII Handling: Redaction, field-level access controls, and content filtering by program.
• Human Review Queue: Shared reviewer tooling, SLAs, and audit trails across pillars.
• Feature Store: Verified fields and risk signals shared between eligibility, fraud, and outreach.

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Data Lineage & Human-in-the-Loop

• Track lineage for every AI-derived field: source document, model/version, confidence, and reviewer action.
• All overrides require a reason code and persist an immutable audit log.
• Reviewer corrections feed back into evaluation datasets and model improvements.

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Compliance Readiness (Future)

• Data retention policies and deletion workflows per program.
• Least-privilege access to AI outputs and raw documents.
• Vendor model logging controls and secure transport defaults.

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Model Router Options (Decision Matrix)

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Router Decision Criteria

• Data class (PII sensitivity), residency, and retention requirements.
• Cost targets and latency SLAs per use case.
• Model coverage (vision, OCR, structured extraction, reasoning).
• Observability depth (per-request traces, prompt/version tracking).
• Contracting complexity and vendor support timelines.

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Internal Router Interface (Draft)

request:
  use_case: document_extraction
  inputs:
    document_id: doc_123
  constraints:
    max_latency_ms: 6000
    max_cost_usd: 0.05
    data_class: pii_high
  policy:
    model_family: vision
    fallback: [primary, secondary]
    prompt_version: doc_extract_v3

response:
  provider: primary
  model: vision-large
  cost_usd: 0.032
  latency_ms: 2810
  confidence: 0.91
  safety_flags: []
  trace_id: trc_abc123

• All AI calls must emit trace_id, prompt_version, and policy_id.
• Router decisions should be auditable and replayable for evaluation.

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Roadmap Dependencies (Q1 2026)

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1. Automated Document Review & Data Extraction

Most benefit programs struggle to turn documentation into structured information. When someone submits a paystub to verify income, staff manually review it, interpret what they’re seeing, and enter data into systems. Human perception varies. Mistakes happen. Processing takes hours.

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What Exists

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What’s Missing

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Architecture

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Downstream Implementation Notes

• Document processing pipeline is downstream of upload and classification.
• Pipeline stages are event-driven and re-runnable (classification → extraction → confidence → review).
• All outputs are stored with lineage metadata for auditability and retraining.

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2. Fraud Detection That Protects Legitimate Applicants

We will analyze patterns across our more than 400,000 existing applications to build a comprehensive understanding of fraudulent submission behavior. As generative AI makes fake documents increasingly realistic, there need to be models that develop deeper fraud vectors that identify coordinated attempts and synthetic identities.

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What Exists

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What’s Missing

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Architecture

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Risk Scoring Model

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3. Program Template Generation from Proven Models

Good benefit programs share common patterns in eligibility criteria, documentation requirements, workflow design, and fraud controls. By analyzing our existing programs alongside publicly available benefit programs that people love, AI can identify what makes programs work well. Administrators describe their goals in plain language. The goal is to generate customized intake flows, eligibility logic, and operational frameworks based on proven models.

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What Exists

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What’s Missing

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Architecture

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4. Personalized Outreach in Community Languages

Families miss opportunities because they don’t know about deadlines, timelines, or programs they qualify for. AI enables personalized outreach at scale, informing applicants about relevant opportunities, upcoming deadlines, and next steps in their own language.

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What Exists

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What’s Missing

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Architecture

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Implementation Timeline

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Success Metrics

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Related Documentation