The Architecture Behind
Autonomous Document Intelligence
Antevolt's multi-agent orchestration system is purpose-built for document-heavy technical analysis in regulated energy markets — running on dedicated GPU infrastructure with compressed persistent memory and human expert verification at every decision point.
Why Not RAG?
We read documents. We don't retrieve fragments. Most AI systems use RAG: chunk documents, embed them in a vector database, retrieve similar chunks at query time. This fails for rigorous analysis:
Context Fragmentation
A 36-page EPC contract has warranty terms on page 28 referencing definitions on page 3, modified by a separate amendment. RAG retrieves page 28 in isolation. Our agents read the entire contract together.
Cross-Document Reasoning
Connecting a pile pull-out test result (800 kg) from a geotechnical protocol to a structural demand calculation (1,210 kg) in an engineering doc to a manufacturer’s adequacy declaration in a third file. No retrieval system makes this connection.
Hallucinated Evidence
RAG systems cite documents they haven’t fully read. A 512-token chunk mentioning “EN 1090” doesn’t mean a compliance certificate exists.
Our approach: Direct Document Comprehension.
Each agent reads complete documents, maintains full context, backed by TurboQuant-compressed persistent memory enabling 6× more context retention. Runs on dedicated NVIDIA H100/H200 GPU clusters.
Agent Swarm Architecture
Autonomous agent swarms. Coordinated sub-agents. One orchestrator. Human verification at the gate.
Agent Roster
Regulatory & Scope Agent
Establishes the regulatory framework, market context, and analysis scope for the entire swarm.
Portfolio & Site Agent
Analyzes site characteristics, portfolio composition, and geographic considerations.
Contract Analysis Agent
Reviews EPC contracts, O&M agreements, warranties, and commercial structures.
Energy Assessment Agent
Evaluates energy yield assessments, resource data, and production forecasts.
Technical Review Agent Swarm
Sub-agents: 4a Design, 4b Technology, 4c As-Built. Deep engineering analysis across all technical domains.
Operational Performance Agent
Analyzes operational history, performance ratios, degradation, and availability records.
Annexes Agent
Compiles supporting materials, document registers, and cross-reference tables.
The Orchestrator
Canonical entity enforcement, finding ID management, severity calibration, cross-agent deduplication, quality gates, and report assembly.
Human Expert Verification
Every RED/AMBER finding reviewed by a qualified domain expert. Not optional. A core architectural layer.
Wave Execution Model
Parallel where possible. Sequential where necessary. Always dependency-aware.
Regulatory, Portfolio, Contract, Technical sub-agents
Energy Assessment
Depends on Technical
Operational Performance
Depends on Energy
Annexes
Depends on all
Orchestrator
Depends on all
Reduces processing time by 60% compared to sequential execution.
Persistent Memory
Architecture
Context that persists. Memory that compounds. Zero accuracy loss.
Structured handoff protocol transfers typed data, evidence chains, confidence signals, and gap declarations between agents.
KV Cache Compression
TurboQuant compression with zero accuracy loss.
PolarQuant Transform
Coordinate transformation and QJL error correction.
Persistent Context
Same precision on page 2,000 as on page 1.
GPU-Accelerated Inference
Dedicated hardware. No API rate limits. No shared infrastructure.
NVIDIA H100/H200 Tensor Core GPU clusters. Self-hosted. Full data sovereignty. Deterministic performance. TurboQuant acceleration delivers up to 8× speedup in attention logits.
Evidence Verification Layer
Every material finding is verified. Independently.
Cross-Dataroom Search
Automated cross-dataroom search for each AMBER finding across all ingested documentation.
Independent Re-Verification
A different agent with different search strategies independently re-verifies each material finding.
Human Expert Review
Every RED/AMBER finding reviewed by a domain expert before delivery. No exceptions.
Triple-Engine OCR Pipeline
Three OCR engines. Zero unreadable documents.
GLM-OCR (VLM)
State-of-the-art on OmniDocBench V1.5. Vision-language model for complex document layouts, tables, and mixed-content pages.
PaddleOCR v5
80+ language support. Optimized for multilingual document extraction with high throughput on diverse scripts and character sets.
Tesseract 5
Baseline extraction engine. Battle-tested reliability for standard document formats and clean text extraction.
The Pipeline.
Extract native text first. If scanned, run all three engines in parallel, merge with confidence-weighted consensus, flag disagreements for human review. 100% processing rate in benchmark.