{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_eilla-ai","slug":"eilla-ai","name":"Eilla AI","type":"agent","url":"https://eilla.ai","page_url":"https://unfragile.ai/eilla-ai","categories":["documentation"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_eilla-ai__cap_0","uri":"capability://text.generation.language.bank.level.encrypted.document.generation.with.compliance.audit.trails","name":"bank-level encrypted document generation with compliance audit trails","description":"Generates financial and legal documents (contracts, reports, disclosures) with end-to-end encryption at rest and in transit, maintaining immutable audit logs of all document modifications and access events. Uses AES-256 encryption for stored documents and TLS 1.3 for transmission, with cryptographic signing to ensure document integrity and non-repudiation for regulatory compliance (SOC 2, GDPR, HIPAA).","intents":["Generate financial reports that meet regulatory audit requirements without exposing raw data","Create legally-binding documents with cryptographic proof of authorship and modification history","Ensure sensitive client documents remain encrypted and inaccessible to unauthorized parties including the service provider"],"best_for":["Financial compliance teams handling SEC filings and regulatory submissions","Law firms and legal departments generating client-facing agreements","Accountants managing client financial data with strict confidentiality obligations"],"limitations":["Encryption overhead adds ~500ms-1s latency to document generation compared to unencrypted alternatives","Audit trail storage grows linearly with document modifications, requiring periodic archival for large-scale operations","Limited to predefined document templates; custom schema generation requires manual configuration"],"requires":["Enterprise or Professional tier subscription for compliance certifications","Valid API key or OAuth 2.0 credentials for programmatic access","TLS 1.3 capable client (all modern browsers and HTTP libraries support this)"],"input_types":["structured financial data (JSON, CSV)","template specifications (Handlebars or Jinja2 syntax)","user-provided text content"],"output_types":["encrypted PDF documents","encrypted Word documents (.docx)","audit log JSON with timestamp and user identity"],"categories":["text-generation-language","data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_eilla-ai__cap_1","uri":"capability://planning.reasoning.financial.decision.making.analysis.with.domain.specific.reasoning","name":"financial decision-making analysis with domain-specific reasoning","description":"Analyzes financial scenarios (investment decisions, loan approvals, budget allocations) using domain-specific reasoning chains that incorporate financial ratios, risk metrics, and regulatory constraints. Implements multi-step reasoning that decomposes complex financial questions into sub-analyses (liquidity assessment, solvency checks, profitability trends) before synthesizing recommendations, with explicit reasoning traces showing which financial metrics drove each conclusion.","intents":["Evaluate whether a business loan application meets lending criteria without manual ratio calculations","Compare investment opportunities by analyzing risk-adjusted returns across multiple scenarios","Identify budget optimization opportunities by analyzing spending patterns against industry benchmarks"],"best_for":["Credit analysts and loan officers evaluating borrower creditworthiness","Financial advisors synthesizing investment recommendations for clients","CFOs and finance managers conducting scenario analysis for strategic planning"],"limitations":["Reasoning chains are deterministic and cannot adapt to novel financial instruments or emerging market conditions without retraining","Requires clean, normalized financial data; garbage input (misaligned fiscal years, inconsistent accounting standards) produces unreliable analyses","Cannot access real-time market data or regulatory updates; analysis reflects training data cutoff"],"requires":["Structured financial data in standardized format (XBRL, standardized CSV with defined column headers)","At least 2-3 years of historical financial statements for trend analysis","API key for Eilla AI with Financial Analysis tier or higher"],"input_types":["financial statements (balance sheet, income statement, cash flow)","structured scenario parameters (interest rates, growth assumptions)","regulatory constraints (debt covenants, capital requirements)"],"output_types":["structured recommendation (approve/deny/conditional with confidence score)","reasoning trace showing financial metrics and decision logic","risk assessment with quantified downside scenarios"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_eilla-ai__cap_2","uri":"capability://safety.moderation.sensitive.data.masking.and.pii.redaction.in.document.analysis","name":"sensitive data masking and pii redaction in document analysis","description":"Automatically detects and redacts personally identifiable information (PII), financial account numbers, and regulated data elements (SSN, credit card numbers, tax IDs) from documents before analysis or sharing. Uses pattern-matching (regex for structured data like account numbers) combined with NER (Named Entity Recognition) models trained on financial documents to identify context-dependent PII (e.g., distinguishing account numbers from reference numbers), with configurable redaction policies (full masking, tokenization, or encryption).","intents":["Share financial documents with external auditors without exposing client PII or account details","Analyze document collections for compliance violations while protecting individual privacy","Prepare documents for archival or sharing while maintaining GDPR/CCPA compliance"],"best_for":["Compliance and legal teams preparing documents for external disclosure","Financial institutions processing customer documents at scale","Accountants sharing client data with third-party service providers"],"limitations":["Context-dependent PII detection (e.g., distinguishing legitimate reference numbers from account numbers) has ~85-90% accuracy; edge cases require manual review","Redaction is irreversible; tokenization requires maintaining a separate token-to-value mapping for later recovery","Performance degrades on scanned/OCR'd documents with low text quality; requires >95% OCR confidence for reliable detection"],"requires":["Document input in text or searchable PDF format (scanned images require OCR preprocessing)","Configuration of PII detection rules (default includes SSN, credit card, account numbers, email, phone)","API key with Data Protection tier or higher"],"input_types":["PDF documents","plain text","structured data (CSV, JSON with financial records)"],"output_types":["redacted PDF or text with PII removed","redaction report showing detected PII locations and types","tokenized data with mapping file for recovery"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_eilla-ai__cap_3","uri":"capability://text.generation.language.template.driven.financial.document.generation.with.variable.interpolation","name":"template-driven financial document generation with variable interpolation","description":"Generates standardized financial documents (loan agreements, investment prospectuses, financial statements) by interpolating user-provided data into pre-built templates with conditional logic and calculated fields. Templates support Handlebars-style syntax for variable substitution, conditional sections (e.g., 'if loan amount > $1M, include additional covenants'), and formula evaluation (e.g., 'total = sum of line items'), with validation rules ensuring generated documents meet regulatory formatting requirements before output.","intents":["Generate 50+ loan agreements in batch with different borrower details without manual document editing","Create investment prospectuses that automatically calculate risk disclosures based on fund composition","Produce financial statements with auto-calculated subtotals and compliance-required footnotes"],"best_for":["Loan origination teams processing high-volume applications","Investment firms generating client-specific prospectuses","Accounting firms preparing standardized financial statement packages"],"limitations":["Template customization requires understanding Handlebars syntax; non-technical users need developer support for complex conditional logic","Calculated fields are limited to arithmetic operations; complex financial modeling (Monte Carlo, scenario analysis) requires external tools","Template versioning is manual; no built-in version control or change tracking for regulatory compliance"],"requires":["Pre-built or custom template in Handlebars format","Structured input data matching template variable schema (JSON or CSV)","API key with Document Generation tier or higher"],"input_types":["JSON object with template variables","CSV file for batch generation","Handlebars template specification"],"output_types":["PDF document","Word document (.docx)","HTML for preview"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_eilla-ai__cap_4","uri":"capability://safety.moderation.role.based.access.control.with.granular.document.permissions","name":"role-based access control with granular document permissions","description":"Enforces fine-grained access control at the document level, allowing administrators to grant users permissions to view, edit, or approve specific documents based on role (analyst, manager, compliance officer) and organizational hierarchy. Implements attribute-based access control (ABAC) where permissions are evaluated based on user role, document classification level, and organizational unit, with audit logging of all access attempts (successful and denied) for compliance reporting.","intents":["Ensure junior analysts can only view anonymized financial data while managers see full details","Restrict document editing to authorized signatories while allowing read-only access to stakeholders","Generate compliance reports showing who accessed which sensitive documents and when"],"best_for":["Large financial institutions with complex organizational hierarchies","Compliance-heavy organizations requiring granular audit trails","Multi-tenant environments where data isolation between clients is critical"],"limitations":["Permission evaluation adds ~50-100ms latency per document access due to ABAC rule evaluation","Requires pre-configured role hierarchy and attribute mappings; dynamic role changes may take up to 5 minutes to propagate","No built-in delegation mechanism; users cannot temporarily grant permissions to others without admin intervention"],"requires":["User directory integration (LDAP, Active Directory, or SAML 2.0 SSO)","Pre-defined role taxonomy and permission matrix","Enterprise tier subscription with RBAC features"],"input_types":["user identity and role attributes","document classification metadata","permission policy rules"],"output_types":["access grant/deny decision","audit log entry with timestamp and reason","compliance report with access patterns"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_eilla-ai__cap_5","uri":"capability://data.processing.analysis.financial.data.extraction.from.unstructured.documents.via.ocr.and.nlp","name":"financial data extraction from unstructured documents via ocr and nlp","description":"Extracts structured financial data (amounts, dates, account numbers, transaction details) from unstructured sources (scanned invoices, bank statements, handwritten forms) using OCR for text recognition combined with NLP-based entity extraction and rule-based post-processing. Implements a pipeline: OCR → text normalization → financial entity recognition (using domain-specific NER models) → validation against expected formats (e.g., amounts must match currency patterns) → structured output (JSON or CSV), with confidence scores for each extracted field.","intents":["Automatically extract invoice line items and totals from 100+ supplier invoices for expense reconciliation","Parse bank statements to identify transaction categories and reconcile against accounting records","Extract loan terms and conditions from scanned loan documents for compliance verification"],"best_for":["Accounts payable teams processing high-volume invoices","Banks and financial institutions automating document intake","Accountants reconciling financial records from multiple sources"],"limitations":["OCR accuracy on low-quality scans (faxes, photocopies) drops to 70-80%; high-quality documents achieve 95%+ accuracy","Entity extraction confidence varies by field type (dates: 98%, amounts: 95%, account numbers: 85%); low-confidence extractions require manual review","Cannot handle novel document layouts or formats not seen during training; requires retraining for new document types"],"requires":["Document image or PDF file (minimum 200 DPI for reliable OCR)","API key with Data Extraction tier or higher","Expected document schema (field names and types) for validation"],"input_types":["scanned PDF documents","image files (JPG, PNG, TIFF)","searchable PDF"],"output_types":["structured JSON with extracted fields and confidence scores","CSV for bulk import into accounting systems","validation report highlighting low-confidence extractions"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_eilla-ai__cap_6","uri":"capability://automation.workflow.multi.party.document.approval.workflow.with.digital.signatures","name":"multi-party document approval workflow with digital signatures","description":"Orchestrates multi-step approval workflows where documents route through multiple signatories (e.g., loan officer → manager → compliance officer) with digital signature capture at each step. Implements state machine-based workflow engine that tracks document status (draft → pending approval → approved/rejected), enforces sequential or parallel approval paths, sends notifications to next approvers, and maintains cryptographic signatures from each party with timestamp and IP address logging for non-repudiation.","intents":["Route loan documents through 3-step approval process (underwriter → manager → legal) with automatic notifications","Collect digital signatures from multiple parties on investment agreements with tamper-proof audit trail","Enforce approval hierarchy where certain users can only approve documents up to a dollar threshold"],"best_for":["Financial institutions with multi-level approval requirements","Legal teams managing contract signature workflows","Compliance departments enforcing approval hierarchies"],"limitations":["Workflow state transitions are synchronous; large-scale parallel approvals (100+ simultaneous approvers) may experience latency","Digital signatures are legally binding only in jurisdictions that recognize electronic signatures (ESIGN Act in US, eIDAS in EU); international workflows require jurisdiction-specific compliance","Workflow logic is configured via UI; complex conditional routing (e.g., 'if amount > $1M, require legal approval') requires custom development"],"requires":["Document in PDF or Word format","Pre-configured approval workflow with defined roles and sequence","Digital certificate or API key for signature generation","Enterprise tier subscription with Workflow features"],"input_types":["document to be approved (PDF, Word)","workflow configuration (JSON with approval steps and conditions)","signer identity and credentials"],"output_types":["signed document with embedded signatures and timestamps","workflow audit log showing approval history","notification events for next approvers"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_eilla-ai__cap_7","uri":"capability://data.processing.analysis.real.time.financial.data.validation.and.anomaly.detection","name":"real-time financial data validation and anomaly detection","description":"Validates financial data against business rules and detects anomalies in real-time as documents are created or updated. Implements rule engine that checks constraints (e.g., 'total assets must equal liabilities + equity', 'revenue cannot decrease by >50% YoY'), statistical anomaly detection (identifies outliers using z-score or isolation forest algorithms), and cross-document consistency checks (e.g., 'invoice amount must match PO amount'). Flags violations with severity levels (error, warning, info) and suggests corrections.","intents":["Catch data entry errors in financial statements before submission to regulators","Identify suspicious transactions that may indicate fraud or accounting errors","Ensure consistency across related documents (PO, invoice, payment) in procurement workflows"],"best_for":["Financial reporting teams ensuring accuracy of regulatory submissions","Fraud detection and compliance teams monitoring transaction patterns","Accounting departments automating data quality checks"],"limitations":["Rule engine requires pre-configuration of business rules; generic rules may produce false positives in edge cases","Anomaly detection is statistical and may miss novel fraud patterns not represented in training data","Cross-document validation requires documents to be in the same system; external documents require manual import"],"requires":["Pre-configured validation rules (provided templates or custom rules)","Historical financial data for anomaly detection baseline (minimum 12 months)","API key with Data Validation tier or higher"],"input_types":["financial data (JSON, CSV, or direct API input)","validation rule definitions","historical baseline data for anomaly detection"],"output_types":["validation report with flagged violations and severity","anomaly scores for each data point","suggested corrections or remediation steps"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_eilla-ai__cap_8","uri":"capability://automation.workflow.compliance.reporting.and.regulatory.submission.automation","name":"compliance reporting and regulatory submission automation","description":"Automates generation of regulatory reports (SEC filings, tax returns, audit reports) by extracting required data from financial documents, applying regulatory formatting rules, and generating submission-ready documents. Implements mapping between internal financial data structures and regulatory schemas (XBRL for SEC, OMB for government, FATCA for tax), with validation that generated reports conform to regulatory requirements (field presence, data types, numeric precision), and audit trails showing which source documents contributed to each report line item.","intents":["Generate SEC 10-K filings from internal financial statements with automatic XBRL tagging","Create tax return documents (1040, 1120) from accounting records with regulatory compliance validation","Produce audit-ready financial statements with footnotes and disclosures automatically populated from source documents"],"best_for":["Public companies managing SEC reporting obligations","Tax professionals preparing client returns at scale","Audit firms automating financial statement preparation"],"limitations":["Regulatory schemas change annually; updates require manual configuration or service updates","Complex financial structures (consolidations, foreign operations, derivatives) may require manual adjustments to auto-generated reports","Regulatory compliance is jurisdiction-specific; US-focused tool requires significant customization for international reporting (IFRS, local GAAP)"],"requires":["Financial data in standardized format (GL accounts mapped to regulatory line items)","Regulatory schema configuration (XBRL, OMB, or custom)","Enterprise tier subscription with Compliance Reporting features"],"input_types":["general ledger data (JSON or CSV)","financial statements (balance sheet, income statement, cash flow)","regulatory schema definitions"],"output_types":["XBRL-formatted regulatory filing","PDF report with regulatory formatting","audit trail showing source documents for each report line item"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"high","permissions":["Enterprise or Professional tier subscription for compliance certifications","Valid API key or OAuth 2.0 credentials for programmatic access","TLS 1.3 capable client (all modern browsers and HTTP libraries support this)","Structured financial data in standardized format (XBRL, standardized CSV with defined column headers)","At least 2-3 years of historical financial statements for trend analysis","API key for Eilla AI with Financial Analysis tier or higher","Document input in text or searchable PDF format (scanned images require OCR preprocessing)","Configuration of PII detection rules (default includes SSN, credit card, account numbers, email, phone)","API key with Data Protection tier or higher","Pre-built or custom template in Handlebars format"],"failure_modes":["Encryption overhead adds ~500ms-1s latency to document generation compared to unencrypted alternatives","Audit trail storage grows linearly with document modifications, requiring periodic archival for large-scale operations","Limited to predefined document templates; custom schema generation requires manual configuration","Reasoning chains are deterministic and cannot adapt to novel financial instruments or emerging market conditions without retraining","Requires clean, normalized financial data; garbage input (misaligned fiscal years, inconsistent accounting standards) produces unreliable analyses","Cannot access real-time market data or regulatory updates; analysis reflects training data cutoff","Context-dependent PII detection (e.g., distinguishing legitimate reference numbers from account numbers) has ~85-90% accuracy; edge cases require manual review","Redaction is irreversible; tokenization requires maintaining a separate token-to-value mapping for later recovery","Performance degrades on scanned/OCR'd documents with low text quality; requires >95% OCR confidence for reliable detection","Template customization requires understanding Handlebars syntax; non-technical users need developer support for complex conditional logic","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:30.283Z","last_scraped_at":"2026-04-05T13:23:42.561Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=eilla-ai","compare_url":"https://unfragile.ai/compare?artifact=eilla-ai"}},"signature":"kgOM/Eh5mSlrhtQFYjVO/P75WyF2++6ZdbjbJErr0hD+9euwCe1jyyT3NI5YtlWr1l317xd0EVgQlDD2DGLuBg==","signedAt":"2026-06-21T00:04:59.386Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/eilla-ai","artifact":"https://unfragile.ai/eilla-ai","verify":"https://unfragile.ai/api/v1/verify?slug=eilla-ai","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}