ODI · AI audit platform
Thinks like an expert
Calculates like a machine
ISA/IFRS procedures implemented in a deterministic architecture. Interpretation separated from computation. Reproducible by design.
Audit scales worse than data
Traditional audit processes were designed for smaller volumes
Audit requires systematic execution, not additional effort
30+ Specialized AI Agents One deterministic workflow
Upload data. Procedures are executed in code. The auditor retains judgment.
Structured data intake
GL, contracts, PDFs. Automatic classification and OCR
Semantic risk analysis
Documents analyzed by meaning, not keywords. Cross-document inconsistencies detected
Deterministic computation
All numerical procedures executed in isolated code. No generative calculations
Evidence generation (ISA 230)
Findings linked to source lines. Parameters documented
Documents loaded and normalized
AI waits for auditor request
AI highlights risks with linked evidence
Auditor validates or rejects
Calculations execute only after approval
All parameter changes are logged
Traceable evidence package prepared
Auditor and partner finalize decision
Reduced routine Increased professional judgment
Traditional audits rely on limited sampling and manual calculations. ODI systematizes procedures and expands coverage.
Traditional Audit Process
Audit with ODI
Impact
Teams reduce procedural time and reallocate effort to analysis and judgment
Teams reduce procedural time and reallocate effort to analysis and judgment
Strict adherence to ISA and IFRS Rigid logic, not creative interpretation
ISA/IFRS by design
ODI executes audit procedures in accordance with ISA and IFRS Judgment is structured, not generated
ODI executes audit procedures in accordance with ISA and IFRS Judgment is structured, not generated
Materiality calculation (ISA 320 / ISA 450)
Materiality thresholds determined systematically based on entity profile and risk level
Benchmark selection
Benchmark selected based on entity profile (PIE / non-PIE)
Threshold calibration
Overall, Performance and Clearly Trivial thresholds calculated.
Documented rationale
Working paper generated with rationale and parameters
Statistical sampling (ISA 530)
Sampling executed algorithmically in accordance with ISA 530
Method selection
Sampling method determined algorithmically (MUS / Attribute)
Sample size calculation
Sample size calculated based on confidence and tolerable error
Reproducibility
Seed number and parameters fixed in documentation
Error evaluation
Tainting Factor and Upper Error Limit calculated
Risk assessment and compliance (ISA 315 / IAS 37 / IAS 10)
Risk and compliance procedures executed through structured decision logic
Risk mapping
GL data mapped to risk library by audit cycles.
Assertions validation
Existence, Valuation, Cut-off assessed systematically
Legal decision trees
Probability assessed for provisioning or disclosure
Standards linkage
Each conclusion linked to a specific standard clause
Deterministic AI not probabilistic generation
Deterministic AI not probabilistic generation
Hybrid architecture: model interprets meaning, code executes calculations Numerical procedures isolated from generative models
Hybrid architecture: model interprets meaning, code executes calculations Numerical procedures isolated from generative models
AI Orchestrator
Upload client data — trial balances, scans, PDFs. The system structures data and builds a document map. The orchestrator applies standards, retrieves relevant documents and executes agents for analysis and calculation.
CoreDoes dividend distribution exceeding available cash flow require KAM disclosure or modification of opinion?
Preliminary conclusion:
KAM: Not required at this stage.
Modified opinion: Risk exists if disclosures are inadequate.
Recommended actions:
1. Obtain updated cash flow forecast.
2. Ensure disclosure of material uncertainty if applicable.
3. Consider modification if management does not provide sufficient evidence.
AI Orchestrator
Upload client data — trial balances, scans, PDFs. The system structures data and builds a document map. The orchestrator applies standards, retrieves relevant documents and executes agents for analysis and calculation.
Does dividend distribution exceeding available cash flow require KAM disclosure or modification of opinion?
Preliminary conclusion:
KAM: Not required at this stage.
Modified opinion: Risk exists if disclosures are inadequate.
Recommended actions:
1. Obtain updated cash flow forecast.
2. Ensure disclosure of material uncertainty if applicable.
3. Consider modification if management does not provide sufficient evidence.
Comparison
| Category | Optimal Document Insight | LLM | Traditional |
|---|---|---|---|
| Execution logic | Agents + deterministic execution layer | Text generation | Manual procedures / scripts |
| Data coverage | Full dataset coverage | Limited by context window | Sampling |
| Numerical accuracy | Deterministic computation | Probabilistic output | Manual calculation |
| Context handling | Document Map (project-wide context) | Limited session context | File-by-file review |
| Methodological framework | Embedded ISA / IFRS | General knowledge | Checklists |
| Document linkage | Contextual linkage | No structural linkage | Manual cross-reference |
| Security model | On-premise / air-gapped options | Cloud SaaS | On-premise |
ODI combines structured reasoning with deterministic execution.
Built on enterprise-grade AI and data platforms
Frontier LLM providers. Secure cloud infrastructure. Production-grade vector databases
Transparent pricing based on Audit Tokens
Full SaaS functionality on every plan
Full platform functionality across all plans Pricing scales by analytical capacity (Audit Tokens) and team size
Audit Token measures platform processing capacity Usage varies by engagement scope, volume and risk profile
30 AT included180 AT per year480 AT per year1 080 AT per year
Analytical processing capacity of the platform. Consumption varies by engagement scope and data volume
Capacity allocated per project Transparent usage tracking
Role-based access and team scaling Designed for growing audit practices
On-demand token allocation Designed for peak season workloads
Enterprise Deployment
Custom infrastructure for regulated and government environments.
Client data remains within internal perimeter AI processes segmented or anonymized context Suitable for regulated enterprises
Full platform deployment inside client infrastructure Isolated database and AI execution Maximum infrastructure control
Enterprise Security by Design
Layered control across infrastructure, data, AI execution, and access governance
Security Roadmap
Provider-agnostic infrastructure, API‑based AI abstraction layer, migration-ready design.
Tier III-equivalent deployment with cluster redundancy and Kubernetes microservice architecture.
Tenant-level logical isolation, segregated metadata and document storage across all layers.
TLS for all connections, encryption at rest via infrastructure standards, field-level tokenization.
Isolated container execution, controlled outbound connectivity, zero-retention processing.
RBAC, centralized activity logging, audit trail with integrity hashing, least privilege.
Methodology Validated in Practice
ODI is developed as a standards-driven audit platform, validated in collaboration with professional practitioners and institutional partners
TRI-S-AUDIT
Pilot validation partner
Methodology and system logic are stress-tested on real audit engagements under ISA/IFRS framework
National Association of Accountants and Auditors
Professional collaboration
Exploring structured adoption of AI-driven audit workflows and educational initiatives
IT Park Uzbekistan
Technology ecosystem support
Infrastructure and export-oriented scaling support for enterprise SaaS development
Client names and engagement details are not disclosed due to confidentiality obligations
A new standard in audit execution
Execute with enterprise rigor. Operate at machine speed
What happens next
ODI is deployed through a structured engagement process
Institutional demo aligned to your audit methodology
Architecture and deployment discussion
Technical validation session
Engagement request