thalabus

Whitepaper Series

thalabus Building Blocks Dependency Map

A layered roadmap of thalabus building blocks and their logical dependencies

📏 Evaluation — every layer must be measurable
Layer 4
Decision Intelligence & Action
Outcomes: Insights, Evidence, Risks, Open Questions, Recommendations, Next Best Action
16From Case Documents to Decisions
End-to-end pipeline: ingest, extract, detect conflicts and index into a structured case record, then retrieve, reason, verify, synthesize and recommend next steps
17Uncertainty, Contradictions & Risks
Expose conflicting claims, sources, dates and confidence; suggest explanations and required clarification without silently choosing
18Next Best Action & Recommendations
Actionable advice, prioritized tasks, decision support
19Human-in-the-Loop AI
Explainable, auditable, human oversight at key steps
Layer 3
Knowledge, Reasoning & Evidence
Outcomes: Understanding, Evidence, Similarity, Applicable Knowledge
12Similar Cases Discovery
Builds a structured case record from the current case, then finds similar cases via BM25, embeddings, reranking, metadata, entities, time and graph relationships
13Multidisciplinary RAG beyond vectors
Graph, vector, indexed, combinatorial (Boolean), prompt augmentation & hybrid RAG. Connects entities, relationships, ontologies and domain knowledge
14Domain Knowledge Integration
Import knowledge bases, documents and domain expertise: laws, regulations, precedents, prior rulings, country/organization-specific rules and ontology lexicons in multiple languages
15Evidence-Grounded Reasoning
Evidence: what do we know? Reasoning: what follows from it? Traces every source → evidence → claim → inference → conclusion, with links and calibrated confidence
Layer 2
Document Understanding & Knowledge Extraction
Outcomes: Extract, Parse, Translate, Structure
9AI Document Intelligence
What is in this document? OCR, handwriting recognition, layout analysis, document structure, classification, speech/audio transcription and visual content
10Trustworthy AI Transcription & Translation
How do we reliably transform it into another linguistic representation? Translation, transcription, confidence, terminology, verification, back-translation and reconstruction
11Facts, Entities, Events & Relationships
Extract knowledge: people, places, events, statements, timelines, ... including their relationships
Foundation
AI Engineering & Platform
Outcomes: Act, Route, Generate, Evaluate
1Security, Privacy & Sovereignty
Encryption, access control, audit
2Data & Storage
Graph DB, Vector DB, Object Storage
3Compute & Deployment
GPUs, Containers, Edge/Cloud Hybrid
4Observability & Governance
Logging, Monitoring, Compliance
5AI Orchestration: Tools, Skills & MCP
Orchestrates multi-step workflows and acts through search, databases, knowledge graphs, Python, APIs, document services and enterprise systems
6Model-Agnostic Architecture & Intelligent Routing
The architecture is independent of any single foundation model: route across local, private and cloud models by sensitivity, capability, latency, cost, GPU capacity, context, language and regulatory constraints
7de2generator: Reliable Long-Form Generation
Detect token drift and structural failure; plan, decompose, checkpoint, generate in parallel, verify and recover
8Evaluating Enterprise AI
Prove retrieval, factuality, citations, completeness, calibration, task success, privacy, cost and latency

End-to-End Use Case Example:
Asylum Case Analysis

📄 Input Documents
Identity, interviews, letters, medical docs, country info, ...
⚙ AI Processing (All Layers)
Multi-step analysis with thalabus
✔ Outputs
  • Similar cases
  • Applicable laws
  • Evidence map
  • Contradictions
  • Recommendations
  • Open questions
  • Next best action
🔍 Output Verification
Machine verification with second model, confidence scoring, audit trail
🧑 Human Review
Human review, correction and decision on next step
This roadmap shows how thalabus builds, layer by layer, from raw documents to trusted decisions. Layer 2 extracts and structures information from source documents; Layer 3 turns that structure into retrievable knowledge and evidence-grounded reasoning; Layer 4 synthesizes it into case analysis, recommendations and human-in-the-loop decisions. The gray Foundation row underpins all layers, both with core engineering (security, data, compute, observability) and with building blocks such as tool use, model routing, long-form generation and evaluation.

Legend

Direct dependency
Supporting / influences
# Building Block Number