How we validate
Structured intelligence across every financial data type.
Prajna AI ingests structured data, unstructured documents, images, and transaction XML — applying different reasoning engines per data type. Every output is confidence-scored, field-traceable, and designed to survive compliance review.
01
Multi-modal Data Intake
Accepts PDFs, scanned images, structured tables, PAIN/CAMT XML, and financial databases. Handles both digital forms and image-based documents without manual pre-processing.
DocuDigest · ImaGenetiX
02
Automated Data Extraction
Captures all fields including checkboxes, bounding boxes, table structures, and free-form text. Validates completeness against required field definitions — no manual sampling.
DocGenetiX · DocuLens
03
Graph-Linked Intelligence
GrasPh connects related entities across multiple documents — linking transaction records, counterparties, and financial instruments into a queryable knowledge graph for cross-document reasoning.
GrasPh · AutoInsight
04
Natural Language Query & Visualisation
Bankers ask questions in plain language. VisionIQ and AINalyzer generate charts, dashboards, and structured answers in real time. All outputs are traceable to the underlying source data.
VisionIQ · AINalyzer
Regulatory & compliance
Standards your banking teams will recognize
These are not marketing claims. Each reflects real datasets, enterprise integrations, and compliance frameworks used in production banking environments.
Active
Interoperability
ISO 20022 & Payment Interoperability
Standardized PAIN and CAMT messaging enables structured, machine-readable transaction data across payment initiation, clearing, and settlement.
Supports seamless interoperability across banking systems while eliminating legacy transformation layers.
Active
Privacy & Security
Secure & Compliant AI (PCI DSS · SOC 2 · LLM Governance)
Enterprise-grade security architecture aligned with PCI DSS and SOC 2, combined with privacy-first AI design.
Ensures tokenization, zero sensitive data persistence, and auditable GenAI execution across financial systems.
Active
Clinical Terminology
Financial Ontology (FIBO)
Standardized financial vocabulary enabling consistent interpretation of instruments, entities, and contracts.
Supports semantic reasoning, entity resolution, and cross-system financial intelligence.
In progress
Trust & Assurance
Document Intelligence
Transforms financial filings and unstructured documents into structured, queryable data.
Enables automated parsing of statements and supports investment analysis, due diligence, and reporting workflows.
Active
Revenue Cycle
KYC & Regulatory Compliance
Automates extraction, validation, and cross-document verification of onboarding and tax forms.
Ensures compliance through structured outputs, completeness checks, and audit-ready workflows.
Active
Imaging & Diagnostics
Risk & Fraud Intelligence
Real-time transaction monitoring and anomaly detection for financial crime prevention.
Supports AML compliance through behavioral analysis, rule-based validation, and risk scoring.
Validated use cases
Transform banking workflows with AI-driven intelligence across onboarding, risk, lending, compliance, and analytics. Prajna AI enables faster decisions, reduced operational effort, and improved accuracy by connecting data, documents, and transactions into a single intelligent layer.
AINalyzer · Clinical Analytics
Encounter Records to Early Clinical Signals
Converts structured encounter data into clinically meaningful insight — identifying disease progression patterns, delayed diagnosis risks, geographic access effects, and emerging capacity pressures using existing clinical and administrative data sources.
Challenge
- EHR data optimised for billing, not intelligence
- Fragmented longitudinal patient record visibility
- Unquantified access-to-care disparities
- Retrospective utilisation-based demand planning
Prajna AI approach
- FHIR Encounter / Condition / Procedure sequence mining
- Risk-adjusted benchmarking across multi-EHR environments
- Geospatial access inequity quantification
- ADT + FHIR streams → predictive demand signals
Observed outcomes
12%
of conditions drove 68% of downstream procedures — surfaced from FHIR Encounter sequences
74%
of high-acuity readmissions linked to top 3 progression chains
31%
higher late-stage diagnoses in patients travelling >25 miles — geospatial inequity quantified
89%
forecast accuracy at 30-day capacity horizon
VisionIQ · Computer Vision
Visual Observation to Measured Patient Flow
A privacy-preserving computer vision system that quantifies patient flow, room utilisation, and congestion dynamics using existing camera infrastructure — with zero PHI exposure.
Challenge
- Unmeasured throughput latency across flow stages
- Manual room and resource status tracking
- Heuristic staffing vs encounter-driven demand
- HIPAA / PHI exposure risk with camera systems
Prajna AI approach
- FHIR-driven room & chair visibility
- HL7 v2 ADT-informed staffing optimisation
- HIPAA-compliant edge analytics — no PHI stored
- Sub-second zone state updates (<1s latency)
Observed outcomes
0
manual data entry — fully automated patient flow capture
<1s
room & zone state update latency — real-time operational visibility
10–15%
throughput uplift potential via turnover optimisation
0
PHI or biometric identity persisted — anonymous tracking only
DocuLens · Document Intelligence
Contract Interpretation to Structured Compliance Validation
Standardises contract validation across heterogeneous healthcare documents — from 837 claims artifacts to payer contracts — eliminating reviewer-dependent interpretation and missed requirement exposure.
Challenge
- Clause-by-clause manual validation overhead
- Missing requirement exposure risks
- Reviewer-dependent interpretations
- No structural visibility across document sets
Prajna AI approach
- 837 claims, HL7 messaging, ICD-10, SNOMED CT aware
- Automated structural parsing & semantic indexing
- Reference-standard deviation detection
- Weighted compliance scoring + audit-ready reports
Observed outcomes
80%
minimum compliance confidence threshold — below which findings are escalated for review
100%
section-level traceability — every finding linked to source clause
↓
manual review effort significantly reduced across contract sets
↑
audit preparedness — structured, reproducible validation output
DocuDigest · Equipment Intelligence
Equipment Specs to Deterministic Performance Validation
Interprets equipment manuals, safety constraints, and operating specifications — delivering traceable, explainable validation for biomedical and clinical engineering teams who previously depended on SME availability.
Challenge
- Fragmented device / equipment data sources
- Unvalidated safety & constraint rules
- SME-dependent operational analysis
- No explainable decision reasoning
Prajna AI approach
- HL7, FHIR, device manuals, SOPs ingested natively
- Deterministic + AI reasoning for constraint-aware analysis
- Automated OEM / accreditation compliance validation
- Cross-device benchmarking — imaging to therapeutic assets
Observed outcomes
↓
engineering assessment time — deterministic reasoning replaces manual interpretation
↑
safety validation confidence — OEM limits and accreditation thresholds enforced programmatically
↑
decision repeatability — answers traceable to source documentation, not SME memory
↓
expert dependency — engineering teams operate without specialist availability bottlenecks
AINalyzer · Clinical Analytics
Encounter Records to Early Clinical Signals
Converts structured encounter data into clinically meaningful insight — identifying disease progression patterns, delayed diagnosis risks, geographic access effects, and emerging capacity pressures using existing clinical and administrative data sources.
Challenge
- EHR data optimised for billing, not intelligence
- Fragmented longitudinal patient record visibility
- Unquantified access-to-care disparities
- Retrospective utilisation-based demand planning
Prajna AI approach
- FHIR Encounter / Condition / Procedure sequence mining
- Risk-adjusted benchmarking across multi-EHR environments
- Geospatial access inequity quantification
- ADT + FHIR streams → predictive demand signals
Observed outcomes
12%
of conditions drove 68% of downstream procedures — surfaced from FHIR Encounter sequences
74%
of high-acuity readmissions linked to top 3 progression chains
31%
higher late-stage diagnoses in patients travelling >25 miles — geospatial inequity quantified
89%
forecast accuracy at 30-day capacity horizon
VisionIQ · Computer Vision
Visual Observation to Measured Patient Flow
A privacy-preserving computer vision system that quantifies patient flow, room utilisation, and congestion dynamics using existing camera infrastructure — with zero PHI exposure.
Challenge
- Unmeasured throughput latency across flow stages
- Manual room and resource status tracking
- Heuristic staffing vs encounter-driven demand
- HIPAA / PHI exposure risk with camera systems
Prajna AI approach
- FHIR-driven room & chair visibility
- HL7 v2 ADT-informed staffing optimisation
- HIPAA-compliant edge analytics — no PHI stored
- Sub-second zone state updates (<1s latency)
Observed outcomes
0
manual data entry — fully automated patient flow capture
<1s
room & zone state update latency — real-time operational visibility
10–15%
throughput uplift potential via turnover optimisation
0
PHI or biometric identity persisted — anonymous tracking only
DocuLens · Document Intelligence
Contract Interpretation to Structured Compliance Validation
Standardises contract validation across heterogeneous healthcare documents — from 837 claims artifacts to payer contracts — eliminating reviewer-dependent interpretation and missed requirement exposure.
Challenge
- Clause-by-clause manual validation overhead
- Missing requirement exposure risks
- Reviewer-dependent interpretations
- No structural visibility across document sets
Prajna AI approach
- 837 claims, HL7 messaging, ICD-10, SNOMED CT aware
- Automated structural parsing & semantic indexing
- Reference-standard deviation detection
- Weighted compliance scoring + audit-ready reports
Observed outcomes
80%
minimum compliance confidence threshold — below which findings are escalated for review
100%
section-level traceability — every finding linked to source clause
↓
manual review effort significantly reduced across contract sets
↑
audit preparedness — structured, reproducible validation output
DocuDigest · Equipment Intelligence
Equipment Specs to Deterministic Performance Validation
Interprets equipment manuals, safety constraints, and operating specifications — delivering traceable, explainable validation for biomedical and clinical engineering teams who previously depended on SME availability.
Challenge
- Fragmented device / equipment data sources
- Unvalidated safety & constraint rules
- SME-dependent operational analysis
- No explainable decision reasoning
Prajna AI approach
- HL7, FHIR, device manuals, SOPs ingested natively
- Deterministic + AI reasoning for constraint-aware analysis
- Automated OEM / accreditation compliance validation
- Cross-device benchmarking — imaging to therapeutic assets
Observed outcomes
↓
engineering assessment time — deterministic reasoning replaces manual interpretation
↑
safety validation confidence — OEM limits and accreditation thresholds enforced programmatically
↑
decision repeatability — answers traceable to source documentation, not SME memory
↓
expert dependency — engineering teams operate without specialist availability bottlenecks
Before and after
What changes when you switch from reactive to predictive banking intelligence
| Dimension | Traditional environments | With Prajna AI |
|---|---|---|
| Demand planning | Retrospective reporting Describes what happened, not what's coming |
Predictive system signals 89% forecast accuracy at 30-day horizon via FHIR ADT streams |
| Capacity visibility | Hidden operational pressure Strain identified after it becomes a crisis |
85% early strain detection Sub-second zone state updates; automatic congestion flagging |
| Clinical data use | Billing-optimised records EHR data structured for reimbursement, not insight |
Progression-aware analytics FHIR Encounter/Condition/Procedure sequences mined for clinical patterns |
| Compliance posture | Buried acronyms Standards cited without technical implementation evidence |
Traceable to source 100% section-level auditability; 80% confidence threshold enforced |
| Procurement defensibility | Vendor-supplied claims |
Downloadable validation report Full methodology documentation for your CMO, CFO, and IT security board |
Agentic framework
Five layers. All traceable.
Each layer of the Prajna AI Agentic Framework is independently auditable and maps to a defined compliance perimeter.
Layer 01
Control
Standards-Aligned Intake
Interprets HL7 v2, FHIR, and 837. Normalises signals across EHR, LIS, PACS, RCM. Eliminates cross-system data ambiguity.
Layer 02
Intelligence
Deterministic Reasoning
Anchored to ICD-10, SNOMED, HCPCS. Detects progression & utilisation patterns.
74%readmissions from top 3 chains
47hidden comorbidity pairs detected
Layer 03
Execution
Operational Intelligence
Converts signals into early-warning indicators. Supports capacity & demand planning.
85%early strain detection
89%30-day forecast accuracy
Layer 04
Governance
Compliance-Safe
Deterministic reasoning with traceability. Standards-constrained validation logic. No non-deterministic inference risk.
80%min confidence threshold
100%section-level traceability
Layer 05
Presentation
Clinician-Readable
Converts analytics into decision signals. Highlights risk, flow, and bottlenecks. Reduces interpretation overhead and accelerates operational decisions.
Ready to build your business case?
Talk to a clinical informaticist on our team — not a sales rep.