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Trustinera AI

Platform

A unified platform for AI-powered financial data operations.

Trustinera AI is the enterprise-grade AI platform for financial data operations. It combines ingestion, categorisation, reconciliation, monitoring, and governance so teams can move from raw transactions to audit-ready outputs without stitching together brittle tools.

What the platform covers

  • Schema-agnostic data ingestion across common financial data sources
  • Hybrid AI categorisation with explainable outputs and correction feedback loops
  • Reconciliation workflows built for finance teams, auditors, and operators
  • Real-time observability, risk monitoring, and operational controls
  • Deployment options for cloud, hybrid, and on-premise environments

Why teams adopt it

Trustinera AI replaces spreadsheet-heavy workflows and fragmented ETL stacks with a single operational surface. That means less manual preparation, fewer compliance blind spots, and faster decisions with full traceability.

Coverage metrics

Current docs coverage across the platform corpus.

0 markdown docs indexed
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Service surface

API coverage pulled from the generated route reference.

Platform narrative

The major capability families now exposed on the site.

Data intake and schema control

The docs cover live data sources, managed ingestion databases, source-to-target mappings, schema discovery, and the multi-schema PostgreSQL foundation that underpins Trustinera.

  • Live data sources across PostgreSQL, MySQL, MariaDB, MongoDB, Redis, and REST APIs
  • System-managed ingestion destinations with sync history, reliability tracking, and selective ingestion
  • Platform schemas spanning categorise, sentrise, reconcile, trustflow, tia_system, tia_ingestion, tia_operations, and chatbot data
  • Schema reference and database comment coverage that keeps the data layer inspectable and auditable

TrustFlow agent and ML control plane

TrustFlow is documented as the orchestrated control plane for data operations, ML lifecycle management, and MCP Agent creation across ingestion, training, governance, deployment, and observability.

  • Visual DAG pipelines with retries, lineage tracking, reliability scoring, and stage-by-stage progress
  • Feature store, experiment tracking, model registry, prompt management, and policy-gated promotion
  • Agent registration, communications, orchestration routing, and collaborative workspaces
  • Real-time and batch inference, drift detection, self-optimizing schedules, and autonomous operations

Transaction intelligence with Categorise

Categorise is documented as a hybrid transaction intelligence engine with explainable classification, confidence scoring, HMRC-aware categories, and correction-driven retraining.

  • Rules engine, ML inference, and LLM fallback working as a cascading classification flow
  • HMRC-compliant category sets and exact identifiers for self-employment and landlord workflows
  • Batch upload, transaction review, and correction loops that improve future model performance
  • Retraining, inference monitoring, and low-confidence escalation for operator review

Financial operations with Reconcile

Reconcile is documented as more than matching. It spans CRM, sales funnel, billing, invoicing, bank reconciliation, and reporting workflows for operational finance teams.

  • Client and contact management with lead-to-invoice workflow coverage
  • Billing periods, invoice allocation, and accounting operations inside the same module
  • AI-assisted bank reconciliation and discrepancy handling
  • Profit and loss reporting plus double-entry accounting semantics

Risk, governance, and explainability with Sentrise

Sentrise is documented as an adaptive fraud and anomaly detection layer with fusion scoring, behavioral baselines, network intelligence, regulatory narratives, and compliance alignment.

  • 8-factor multi-signal fusion scoring with weighted explanations
  • Behavioral baselines, temporal patterns, merchant affinity, and velocity analysis
  • Graph-driven network intelligence for cycles, hubs, collusion, and risk propagation
  • AML, KYC, GDPR, PSD2, SOX, FCA, and PRA alignment through explainable outputs and audit trails

Operations, observability, and auditability

The operations docs go well beyond a status page. They document logs, audit events, alert rules, metrics, health, retention, SLOs, tracing, and cron execution tracking across the platform.

  • Structured logging, audit trail tables, alert lifecycle, and metrics snapshots in tia_operations
  • Service health monitoring, retention policy, and operational views for deployed services
  • Observability implementation guidance, data integrity rules, and monitoring strategy
  • Runtime support for system health, business KPIs, alert acknowledgement, and compliance reports

Integrations, storage, and supporting applications

The docs cover a broad integration surface: bookkeeping systems, object storage, notebooks, database connectivity, chat proxying, and supporting runtime services.

  • QuickBooks, Xero, Stripe, and Moneyhub references for connector coverage
  • MinIO artifact and object storage patterns plus notebook workflows for ML teams
  • Database access references covering primary and ingestion stores
  • AI chat, vector search, Jupyter, and gateway-adjacent platform services

Training, tutorials, and adoption paths

Trustinera ships with structured training and tutorial material for operators, administrators, analysts, and ML teams, covering onboarding through advanced TrustFlow workflows.

  • Ten formal training guides spanning schemas, configuration, categorisation, reconciliation, Sentrise, chat, audit, and user management
  • Workspace tutorials for data sources, features, experiments, registry, policy promotion, deployments, monitoring, prompts, and transforms
  • Scenario guides for HMRC categorisation, fraud prevention, and integrations
  • Quick-reference cheatsheets for service URLs, authentication, APIs, orchestration, observability, and kubectl operations

Documentation atlas

Every top-level docs area indexed under `/docs`.

Each card below represents a docs domain and lists the document titles currently surfaced on the site.

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