What makes your agents intelligent?
Two layers, AgentMind and InsightEngine, sit at the cognitive core of every agent in the Covasant Agent Management Suite (CAMS). AgentMind manages language, memory, and reasoning. InsightEngine handles perception, detection, and prediction. Together, they determine the difference between an agent that answers and an agent that acts.
Built for AI Engineers · Data Scientists · MLOps Teams
Language and reasoning. Perception and prediction.
AgentMind gives your agents the ability to retrieve, remember, and reason. InsightEngine gives them the ability to see anomalies, extract meaning from documents, forecast outcomes, and classify inputs. Neither layer is sufficient alone.
Layer 1 · InsightEngine
Perception & Analytics
The sensing and analytics layer that turns raw data into signals that agents can act on. Vision, speech, document extraction, anomaly detection, forecasting, classification, and optimization. AgentMind manages language, while InsightEngine manages the physical world, including documents, transactions, audio, images, and time-series data.
VisionIQDocPulseVoiceCoreAnomalyRadarForecastIQClassifyAIOptiCore
Layer 2 · AgentMind
Agent Intelligence
The cognitive infrastructure that makes agents intelligent. Hybrid retrieval, cross-session memory, enterprise knowledge graphs, domain-specific small language models, and intelligent LLM routing. Every agent's ability to understand context, find the right information, and generate trustworthy outputs depends on AgentMind.
HybridRAGContextCoreSemanticGraphSwiftLMModelHub
Retrieval, memory, reasoning, and model routing.
Five components that form the cognitive backbone of every CAMS agent. These are more than just plug-in modules. They are integrated capabilities designed to work together so that your agents produce outputs that you can trust.
RetrievalHybridRAG · Intelligent Hybrid Retrieval
The retrieval engine that eliminates both failure modes
Pure vector RAG misses exact-match requirements. Pure keyword search misses semantic context. HybridRAG combines dense vector search, sparse keyword retrieval (BM25/TF-IDF), and structured data querying simultaneously, with configurable weighting and a re-ranking layer for relevance optimization. Every retrieved passage carries confidence scores and full source attribution.
Model RoutingModelHub · LLM Registry & Intelligent Routing
Stop hardcoding models. Route intelligently.
Central registry and intelligent routing layer for all language models, such as LLMs, SLMs, and embedding models. Routes inference requests to the right model based on task type, cost ceiling, latency SLA, and data residency requirements. Multi-provider support (Vertex AI, OpenAI, Anthropic, open-source). A/B testing between model versions. PII detection before data leaves the enterprise boundary. Full cost and latency telemetry per agent per model.
3
ContextCore · Memory Engine
Agents that remember within sessions and across them
Short-term working memory and long-term episodic memory for agents. User and entity memory stores. Memory decay and relevance scoring. Privacy-preserving memory with access controls. Without ContextCore, every agent interaction starts from zero.
AI Engineers · Platform Architects designing multi-turn workflows
4
Semantic Graph · Knowledge Graph
Multi-hop reasoning across your enterprise entities
Entity extraction, relationship mapping, ontology management, SPARQL/Cypher-compatible graph queries, and change detection pipelines. Agents reason across the graph rather than querying flat data, enabling multi-hop inference like "which vendors supply our highest-risk sites?"
Knowledge Engineers · Data Scientists · AI Architects
5
SwiftLM · Small Language Models
Domain-specific SLMs: faster, cheaper, and more accurate for narrow tasks
A curated library of fine-tuned domain SLMs (finance, healthcare, legal, manufacturing) for tasks where a 70B LLM is expensive overkill. Quantized model variants for edge deployment. Fine-tuning pipelines for custom enterprise adaptation.
AI Engineers optimizing cost/latency · Data Scientists fine-tuning
3×
Retrieval accuracy improvement with HybridRAG vs. pure vector search
60%
Inference cost reduction when ModelHub routes to task-optimized SLMs
0
PII exposure incidents when VaultGuard + ModelHub data residency controls are active
∞
Hops: SemanticGraph enables unlimited multi-hop reasoning across enterprise entities
Perception, detection, prediction, and optimization.
Seven analytical capabilities that give agents the ability to perceive and act in the physical world, analyzing documents, audio, images, transactions, time-series streams, and decision spaces. Each component is independently deployable and deeply integrated with AgentOS.
01
Anomaly Radar · Detection Engine
Continuously monitors every data stream for deviations from expected patterns
The core detection engine across CAMS products. Unsupervised and semi-supervised anomaly detection, time-series analysis, multivariate pattern analysis, adaptive thresholding that self-adjusts to seasonal variation, and real-time batch detection. Explainable outputs, every anomaly carries a reason. Powers konaAI, CyberProTX, and AI Agent Control Tower.
Powers konaAI, CyberProTX, AI Agent Control Tower
02
DocPulse · Document Intelligence
Structured data from unstructured documents like contracts, invoices, records, and filings
Layout-aware document parsing for tables, multi-column layouts, and nested structures. Named entity extraction, key-value form extraction, clause and obligation extraction from contracts. Cross-document relationship detection. Confidence scoring per field with flagged low-confidence outputs for human review. PDFs, Word, images, HTML, email, all formats supported. Primary intelligence layer for NegotiumAI, konaAI, and LexAI.
Powers NegotiumAI, konaAI, LexAI
VisionIQ
Computer vision for enterprise
Object detection, defect detection for manufacturing QA, OCR on complex layouts, identity document verification, video frame analysis, multi-modal document understanding.
Defect detectionOCRVideo analysis
VoiceCore
Enterprise TTS / ASR
Domain vocabulary fine-tuning, speaker diarization, real-time and batch transcription, noise-robust models for field/industrial environments, PII redaction from transcripts.
ASR / TTSDiarizationPII redaction
ForecastIQ
Time-series forecasting ensemble
Multi-model ensemble (ARIMA, Prophet, Transformer-based, gradient boosting). Hierarchical forecasting, uncertainty quantification, what-if scenario simulation, continuous retraining pipelines.
Ensemble modelsUncertainty boundsScenarios
ClassifyAI
Multi-class, multi-label classification
Fine-tuned transformer classifiers, tabular classification, zero-shot and few-shot for new categories without retraining, active learning with human feedback. Routes, triages, and categorizes at scale.
Zero-shotMulti-labelActive learning
OptiCore
Constraint optimization engine
Linear and mixed-integer programming, multi-objective optimisation, reinforcement learning for sequential decisions, simulation-based optimization. Explainable solution outputs with trade-off analysis.
MIPMulti-objectiveRL
Three ways in which teams adopt AgentMind and InsightEngine
Both layers are available as modular components or as a complete integrated stack. Start with the capability most relevant to your current use case, and expand progressively.
01
For RAG and LLM engineering teams
Start with HybridRAG + ModelHub
Replace your current retrieval pipeline with HybridRAG and route your model inference through ModelHub. Immediate improvement in retrieval accuracy, cost visibility, and data residency enforcement, without changing your agent architecture.
02
For teams processing unstructured enterprise data
DocPulse + AnomalyRadar as a detection stack
Extract structured data from your document corpus with DocPulse. Monitor transactional and operational streams for anomalies with AnomalyRadar. Feed both into AgentOS for agents that act on document content and data deviations simultaneously.
03
For predictive and operational intelligence teams
ForecastIQ + ClassifyAI + OptiCore for decision agents
Combine ForecastIQ's predictions with ClassifyAI's triage outputs and OptiCore's recommendations to build agents that forecast a situation, classify its severity, and optimize the response, end-to-end, in a single orchestrated workflow.
See HybridRAG, ModelHub, and AnomalyRadar
running on your data.
We'll walk your AI and data engineering team through a live technical session showing AgentMind and InsightEngine layers working together in a real agentic workflow. No slides. Actual infrastructure on GCP.