Solution approach
Engineering intelligence
built on deterministic reasoning.
Technical engineering documents — performance handbooks, equipment specs, safety manuals — contain layered information that requires specialised interpretation. Critical details are often embedded inside nomographs, dense measurement tables, and dimensional diagrams. Prajna AI reads these the way an engineer would, with a structured, multi-step retrieval pipeline that guarantees citation-backed answers.
01
Document Ingestion
PDFs, manuals, spreadsheets, and performance charts ingested with stable text chunking and high-quality embeddings tuned for engineering-heavy content.
BGE + CLIP Encoding
02
Multi-Step Retrieval
Strict ordered sequence: initial context retrieval → page-level deep dive → targeted specification lookup → grounded synthesis. No heuristic shortcuts.
Vector DB · Neo4j
03
Engineering Validation
SAE tipping load rules, swell factor consistency, efficiency factor validation, and unit normalisation checks applied deterministically before any answer is generated.
SAE / ISO Constraint Rules
04
Role-Conditioned Output
Every response scoped to the user's access role — section-level graph permissions enforced at the database layer, not in the UI. Answers are traceable, repeatable, and citation-linked.
RBAC · Knowledge Graph
Platform capabilities
Built for industrial data complexity.
From comparative technical specifications to multimodal diagram interpretation — each capability validated on real engineering documents.
Active
Document Intelligence
DocuDigest
Natural language querying across performance handbooks, equipment specs, and technical manuals. Deterministic formula extraction, comparative spec analysis, and multi-attribute lookups with citation-backed answers.
Active
Access Security
GrasPh RBAC
Role-based knowledge graph that decomposes documents into sections, paragraphs, and entities with node-level permissions. Security enforced at the database retrieval layer — not the UI. Admin, Manager, and Analyst roles with scoped semantic search.
Active
Operational Analytics
AINalyzer + AutoInsight
Real-time conversational data exploration for structured operational data — production metrics, maintenance records, fleet performance. Automated intuitive analysis from vast amounts of structured manufacturing data without exposing sensitive operational information.
Validated use cases
Each capability demonstrated on the Caterpillar Performance Handbook — one of the most technically dense equipment documents in the industry.
DocuDigest · Equipment Intelligence
Equipment Specifications to Engineering-Grade Intelligence
Transforms dense technical handbooks into structured, deterministic answers — retrieving formulas, comparing machine specs, validating safety constraints, and producing executive-ready reports from documents like the Caterpillar Performance Handbook. Engineers interact conversationally; the platform routes queries to specialised reasoning agents and traces every answer to its source.
Challenge
- Equipment data fragmented across manuals, spreadsheets, and charts
- Production estimates relied on manual calculations or static tables
- Safety constraints not automatically validated
- No integrated reasoning layer to explain engineering outcomes
- Cross-equipment benchmarking required domain experts
Prajna AI approach
- Multi-step retrieval: context → deep dive → spec lookup → synthesis
- Deterministic formula extraction with SAE rule enforcement
- CLIP encoding for visual chart and nomograph interpretation
- Natural language query interface with citation-backed answers
- Executive-ready industrial reports generated automatically
Observed outcomes · Caterpillar pilot
↓
engineering assessment time — deterministic reasoning replaces SME-dependent manual interpretation
100%
section-level traceability — every answer linked to source clause in the handbook
↑
safety validation confidence — SAE 50% tipping load rule and constraint thresholds enforced programmatically
↓
expert dependency — engineering teams operate without specialist availability bottlenecks
GrasPh · Knowledge Graph Security
Document-Level Binary Access to Node-Level Role Permissions
Traditional documents operate on all-or-nothing access — once access is granted, everything is visible. GrasPh decomposes documents into a structured knowledge graph where permissions are applied at the node level: per section, per paragraph, per concept. Security is structural, not superficial — enforced at the database retrieval layer before any content reaches the application.
Challenge
- Sensitive cost data exposed alongside general maintenance procedures
- Binary file access — all or nothing, regardless of role
- LLMs can surface restricted information dynamically if not constrained
- No granular section or concept-level permission enforcement
Prajna AI approach
- Documents decomposed into graph nodes: sections, paragraphs, entities
- Role mapping via Neo4j graph relationships — Admin / Manager / Analyst
- Cypher WHERE clause injects section filters at database query time
- Even semantic vector search respects access — scoped to permitted nodes only
- LLM response conditioned on role — tailored synthesis, not just filtered retrieval
Observed outcomes · Caterpillar pilot
3
distinct access roles validated — Admin (all 18 sections), Manager (sections 1–6), Analyst (sections 7–13)
<0
unauthorised sections retrieved — restricted content never reaches the application layer
↑
answer quality for Admins — global relevance ranking vs role-scoped retrieval for restricted users
↑
audit preparedness — structured, reproducible, role-traceable output for every query
DocuDigest · Fleet Analytics
Static Spec Tables to Multi-Equipment Production Intelligence
Goes beyond static lookups to apply production equations, validate safety constraints, and benchmark equipment classes — wheel loaders, haul trucks, drills — against each other. Combines deterministic engineering models with LLM-powered explanation to generate multi-dimensional insight across equipment classes, operating conditions, and cost structures.
Challenge
- No contextual understanding of engineering dependencies across equipment
- Limited cross-equipment benchmarking capabilities
- Production feasibility not validated against safety constraints
- No integrated reasoning layer to explain engineering trade-offs
Prajna AI approach
- Applies wheel loader production formula with swell factor and efficiency validation
- Performs multi-equipment productivity comparison across drills, loaders, haul trucks
- Validates 50% SAE tipping load rule, unit normalisation, and constraint conflicts
- Generates structured, executive-ready industrial reports automatically
Observed outcomes · Fleet intelligence
↓
equipment downtime through better planning — predictive failure detection from spec-aware analysis
↑
production scenario modelling speed — calculations grounded in handbook procedures, not heuristics
↑
engineering compliance confidence — safety rules enforced before any production estimate is returned
↑
maintenance forecasting accuracy — spec-aware diagnostics replace manual review
DocuDigest · Voice Interface
Hands-Free Technical Documentation Access for Field Environments
Industrial users require hands-free access to technical documentation in field environments and customer care settings. Prajna AI extends the deterministic retrieval pipeline with a speech-to-text interface that captures microphone input, accurately transcribes complex technical terminology, and routes queries into the same multi-step engineering reasoning workflow — preserving engineering-grade accuracy across voice interactions.
Challenge
- Field engineers cannot type queries while operating equipment
- Customer care teams need fast spec access while assisting clients live
- Standard STT fails on dense engineering terminology
- Voice interfaces typically lose traceability and citation quality
Prajna AI approach
- Speech-to-text interface tuned for engineering and technical vocabulary
- Voice input routed into the same structured multi-step retrieval pipeline
- Core deterministic reasoning layer unchanged — full citation traceability preserved
- Grounded, evidence-based responses — no hallucinated actions from voice queries
Observed outcomes · Voice pilot
↑
field accessibility — hands-free query capability for technicians and on-site engineers
100%
pipeline consistency — voice queries go through identical deterministic reasoning as text queries
↓
response latency for customer care teams — immediate spec access without manual document navigation
↑
answer grounding — citation-backed outputs preserved across voice interaction modality
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 move from
static manuals to living intelligence
| Dimension | Traditional environments | With Prajna AI |
|---|---|---|
| Equipment spec access | Manual document navigation Engineers browse PDFs manually, relying on memory and bookmarks |
Conversational spec retrieval Natural language queries → deterministic answers with section-level citations |
| Production calculations | Static tables & manual math Spreadsheet-based estimates prone to formula errors and outdated assumptions |
Deterministic formula engine Production equations applied from handbook procedures with swell factor and efficiency validation |
| Safety constraint enforcement | Unchecked manual compliance SAE tipping load rules and safety thresholds validated ad hoc by individual engineers |
Programmatic rule enforcement 50% SAE rule, unit normalisation, and constraint conflict detection applied before any answer |
| Document access control | All-or-nothing file access Sensitive cost data exposed alongside general maintenance procedures |
Node-level role permissions Section, paragraph, and concept-level access enforced at database layer — not UI |
| Answer traceability | Expert-dependent interpretation Outputs traceable to SME memory, not source documentation |
100% section-level traceability Every finding linked to the exact source clause — reproducible, audit-ready outputs |
Agentic framework
Five layers. All traceable.
Each layer of the Prajna AI Manufacturing Framework is independently auditable and maps to a defined engineering accuracy and compliance boundary.
Layer 01
Ingestion
Document-Aware Intake
PDFs, manuals, performance charts, nomographs, and CAD-linked specs loaded with stable engineering-tuned chunking and BGE text + CLIP image embeddings.
Layer 02
Retrieval
Multi-Step Structured Search
Ordered sequence: initial context → page-level deep dive → specification keyword lookup → grounded synthesis. Avoids hallucinated agent actions entirely.
4retrieval stages per query
↑precision vs generic RAG
Layer 03
Validation
Engineering Safety Layer
SAE tipping load rules, swell factor consistency, efficiency factor validation, unit normalisation, and constraint conflict detection applied deterministically before synthesis.
6+constraint checks per answer
100%pre-synthesis validation
Layer 04
Access
Role-Conditioned Security
Neo4j graph database with RBAC node permissions. Cypher WHERE clause enforces section-level access at query time. Scoped vector search — users cannot retrieve unauthorised sections semantically.
3access roles validated
0unauthorised retrievals
Layer 05
Output
Citation-Backed Intelligence
Every answer traceable to source section and clause. Executive-ready industrial reports generated automatically. Voice interface routes through identical pipeline — no traceability loss across modalities.
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