KOREV cognitive architecture
Evidence organizes agents and workflows. Atlas gives them business understanding. PRISM steps in when processing requires reinforced cognitive control.
Category
KOREV combines an environment for business agents, enterprise reasoning intelligence, and a cognitive engine dedicated to critical processing.
Thesis
Generative AI is already present in enterprises.
General-purpose models are effective for low-stakes tasks: drafting, summarizing, and exploratory research.
Yet organizations hesitate to entrust them with their truly sensitive processes.
Because a probabilistic answer alone is no longer enough when the enterprise must be able to answer for the data, the models, the processing steps, the approval rules, and the final decision.
KOREV builds the infrastructure that makes it possible to cross this threshold.
Cognitive principles
KOREV does not stack infrastructure layers: the system organizes operational interaction, business understanding, and the protection of critical decisions.
The workspace for agents.
Evidence organizes analysis, research, case files, decision preparation, and the production of deliverables across your business processes.
Technology: Evidence
An intelligence that knows your enterprise.
Atlas is KOREV's reasoning engine: specialized for and grounded in your knowledge to power agents and workflows.
Technology: Atlas
Reinforced cognitive control when the stakes require it.
PRISM cross-checks several independent models on critical processing, detects disagreements, and applies your decision policy.
Technology: PRISM
Architecture
Evidence organizes the agents' work. Atlas gives them business understanding. When criticality requires it, PRISM applies reinforced control before delivery.
Evidence organizes the workflow and interacts with Atlas for context and reasoning. After the criticality assessment, processing either follows the standard path or branches to PRISM when critical control is required. The result then returns to Evidence.
The workflow can classify processing by its sensitivity and the applicable policy in order to adjust the level of control.
Searches, information summaries, and routine analyses run directly, without systematically triggering multi-model control.
Result · Sources · Required approvals
Deliverables are returned to the operational workspace with their justifications and the configured human review points.
Operational Intelligence Interface
The workspace where teams use KOREV agents to handle case files, follow the workflow, and deliver the outputs.
The point is not adding one more model. It is knowing which one to use, under which rules, and when to suspend its result.
The workspace for KOREV agents
Evidence is the operational environment in which your teams work with specialized AI agents, designed around your business flows, rules, and knowledge. It coordinates the agents across the steps of the process, flags sensitive issues, and returns usable results with the traceability elements defined by the workflow — your experts keep the decision.
KOREV's business brain
Atlas gives Evidence agents a semantic understanding of your environment. It draws on the internal documents, procedures, and rules specific to your organization to interpret each task with discernment, without claiming to blindly inject all your data into its neural parameters.
The KOREV architecture dynamically adjusts the intensity of control to the actual criticality of the operation. This avoids needlessly weighing down simple processing while applying maximum rigor to high-stakes actions.
Document searches, information summaries, and exploratory analyses run directly through Evidence and Atlas, on a routine path separate from reinforced critical control, without unnecessary multi-model computation.
When the applicable criticality policy requires it (a regulatory judgment call, a contractual commitment, or a financial decision), PRISM can be called on to cross-check several models and apply the approval policy.
KOREV's critical brain
PRISM maintains the state and context the critical case requires, calls on several independent intelligences, cross-checks their conclusions and applies your decision policy. If the required criteria are not met, PRISM suspends automatic use of the result and forwards the complete case file to an authorized reviewer.
Delivery & output
The operational loop closes in the workspace: the result is delivered with all the elements required to check it, according to the workflow concerned — cited sources, a trace of the steps, the controls applied, and the decision brief. For critical outputs submitted to the finalization pipeline, an integrity artifact can make it possible to verify their integrity, depending on the pipeline concerned. The expert keeps final approval and legal responsibility.
Deployment
The allocation of cognitive roles (Evidence, Atlas, PRISM) is independent of the hosting model. KOREV adapts to your confidentiality and compliance requirements.
KOREV maintains a generic, sector-specific Atlas foundation. For an organization, a dedicated instance can be created and then specialized using datasets prepared and vetted by KOREV teams. This version is deployed within the client's architecture and complements its training through the dynamic use of authorized documents, procedures, and reference frameworks. The runtime can be private, hybrid, or hosted in a dedicated European environment. Data used to specialize one client is not used to train another client's instance without explicit agreement.
The cognitive layer (Evidence, Atlas, PRISM) keeps its structural roles whatever hosting perimeter is chosen.
KOREV maintains a shared Atlas foundation for general capabilities. For each organization, a dedicated instance can be specialized through training on prepared and vetted datasets, separately from production execution.
Generic & sector-specific foundation
Evolves on sector corpora kept separate from private client datasets.
Isolated client base
Each client has an isolated base, with no model pooling.
Prepared & vetted
Curated and validated by KOREV engineering and data science.
Supervised training on dedicated infrastructure: validated dataset, formal versioning, evaluation benchmarks, and controlled promotion.
Model version frozen and deployed within the client perimeter. Production interactions never trigger automatic retraining.
Future changes through governed, periodic adaptation cycles (no uncontrolled loop in production)
Coordinates specialized agents on the case file and runs the workflow.
Reasoning engine grounded in your vocabulary and business rules.
Documents, reference frameworks, internal procedures, and records retrieved on demand.
Directly usable result, cited sources for review, and an integrity artifact depending on the pipeline.
Each client specialization is isolated from other organizations. One company's data is not reused to train another client's instance without explicit agreement.
Rigor & verifiability
Every public signal is tied to an explicit method, scope, and limitation.
14
14 active profiles under agents/: compliance, contradictor, default, developer, finance, hacker, infrastructure, legal_drafting_guarded, legal_safe, marketing, medical, multitask, researcher, sales. The _example folder is excluded.
3 levels
CriticalityRouter architecture (ADR-010 / ADR-011): LEVEL 1 (simple request: consensus bypassed by default, source baseline mandatory); LEVEL 2 (professional zone: analysis/advice, consensus can be enabled through explicit user opt-in or caller force_consensus, source baseline); LEVEL 3 (critical decision, liability, dispute, critical action: PRISM consensus mandatory, strict mode if the domain is critical). Fail-closed always takes priority in case of ambiguity.
5 required checks
Production release chain for the Evidence backend: protected main branch, mandatory PR, 5 required checks, enforce_admins, build/deploy guardrails, drain before restart, /healthz, and VERSION.json.build_commit.
2/3
Canonical run_consensus() API: a 2/3 quorum of the valid votes cast by the cross-checked models is required.
Cross-cutting
Approval points defined per process; automatic escalation when the conditions are not met.
Models, sources, controls, approvals, and decisions retained, timestamped, and reviewable.
Rules, permissions, responsibilities, and history built natively into the architecture.
Encryption, flow isolation, access management, and logging in the chosen environment.
Data location, movement, and processing defined and governed by the organization.
Regulatory compliance depends on the context, the system, its use, and the organization's role. KOREV provides technical governance capabilities; on its own, it does not constitute a legal certification of compliance.
In practice
Atlas brings business understanding; Evidence organizes the agents' work; PRISM protects critical decisions before delivery.
An architecture discussion with KOREV: your systems, your data constraints, your current models, and a realistic path to a first process in production.
KOREV — AI that shows its work.