Solution approach
Engineering intelligence
built on deterministic reasoning.
Platform capabilities
Built for industrial data complexity.
From comparative technical specifications to multimodal diagram interpretation — each capability validated on real engineering documents.
Validated use cases
Each capability demonstrated on the Caterpillar Performance Handbook — one of the most technically dense equipment documents in the industry.
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.
- 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
- 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
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.
- 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
- 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
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.
- 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
- 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
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.
- 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
- 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
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.
- 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
- 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
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.
- 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
- 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
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.
- 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
- 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
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.
- 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
- 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
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.
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