An Inline Distributed Cognitive Fabric for the Enterprise
Cortexa unifies and orchestrates specialized intelligence inside a governed harness for enterprise reasoning, governance, memory, observability, and model-agnostic AI execution. It helps teams understand what is happening, control risk, estimate cost, and make better AI decisions.
Most organizations cannot see inside their AI operations.
Enterprises are deploying copilots, chatbots, RAG systems, and agentic workflows faster than they can govern, explain, or cost-manage them. Cortexa becomes the inline harness around enterprise AI execution.
AI cost is difficult to predict
Token usage, model selection, long context windows, repeated retries, agent loops, and unnecessary model calls make AI costs unpredictable.
Decision logic is opaque
Many AI workflows produce outputs, but organizations cannot clearly explain how the answer was created, reviewed, or accepted.
Governance is outside the flow
AI policies often sit in documents while execution happens elsewhere. Cortexa brings governance directly into the AI request path.
Plug Cortexa into the AI you already use.
Cortexa is designed to work inline with enterprise AI systems. It can sit between applications and models, between agents and tools, between chatbots and retrieval systems, or between enterprise workflows and external LLMs.
It does not force organizations to rebuild their AI stack. It adds the missing layer of governance, orchestration, visibility, and control.
What the harness adds
Identifies what the user, application, or agent is trying to do.
Applies governance rules before and during execution.
Routes to retrieval, planners, challenger modules, tools, domain SLMs, external LLMs, or approval workflows.
Tracks request source, intent, context, model, tokens, latency, cost, evidence, quality, confidence, and outcome.
Built to wrap, govern, and improve enterprise AI workflows.
Enterprise apps
Cortexa works with CRM, ERP, HRIS, internal portals, workflow systems, document systems, and custom enterprise applications. It helps ensure AI requests are governed, routed, monitored, and recorded.
Agents and agentic systems
Cortexa works with workflows where AI systems plan, call tools, retrieve data, make decisions, and perform multi-step tasks. It helps control tool usage, retry loops, approval checkpoints, risk boundaries, state, cost escalation, and decision lineage.
Chatbots and copilots
Cortexa can wrap existing assistants to add governance, evidence tracking, cost visibility, quality scoring, and auditability. This is useful when organizations already have AI assistants but cannot fully see what they are doing.
Any model
Cortexa is model-agnostic. It can work with GPT, Claude, Gemini, open-source models, domain-specific SLMs, on-prem models, cloud models, and private enterprise models.
Cortexa is not only a gateway.
A simple AI gateway can route model calls. Cortexa goes further by providing specialized intelligence for understanding, governing, orchestrating, evaluating, and improving AI workflows.
Classify intent
Understand whether the task requires search, retrieval, reasoning, compliance review, calculation, report generation, tool execution, or approval.
Apply governance
Check tenant policies, role permissions, data access, PII, regulatory constraints, model rules, guardrails, and approval requirements.
Select execution path
Route to retrieval, planner, challenger, composer, domain SLM, external LLM, tools, fallback, or human approval.
Measure quality
Evaluate groundedness, completeness, evidence quality, contradictions, confidence, compliance, usefulness, citation strength, and risk.
Write back learning
Store decision records, evidence trails, feedback, output scores, cost patterns, drift signals, workflow performance, and improvement signals.
Governance should be visible, not hidden.
Cortexa makes governance understandable for business, technology, compliance, risk, and finance teams. Instead of simply blocking or allowing AI requests, Cortexa explains which rule was applied, why a route was selected, why a model was chosen, why approval was needed, what context was used, what risk was detected, what the output cost, and whether the output was reliable.
Govern AI execution directly in the request path.
Convert enterprise rules into executable controls across AI workflows.
Enforce boundaries around risky actions, model use, source access, sensitive topics, and output behavior.
Detect, redact, restrict, or route sensitive data through safer execution paths.
Require human approval before execution, before final response, or before downstream action.
Record the full decision trail for review, audit, and accountability.
AI costs should not be a surprise.
AI cost does not depend only on the model. It depends on prompt size, retrieved context, model calls, retry logic, agent loops, tool calls, output length, user volume, workflow complexity, evaluation calls, fallback calls, and human review loops. Cortexa makes these cost drivers visible.
Token usage
Track input tokens, output tokens, context tokens, retrieval size, and total token consumption.
Model usage
See which models are used by which workflow, department, project, tenant, or user group.
Cost attribution
Allocate AI cost by tenant, project, department, user, workflow, application, model, purpose, and use case.
Model routing economics
Compare when to use a premium LLM, smaller LLM, domain SLM, open-source model, retrieval-only flow, tool-based execution, or human-in-the-loop flow.
Quality-cost tradeoff
Understand whether higher model cost, larger context, or agent loops are producing better outcomes or simply increasing spend.
Budget decisions
Help leadership identify high-cost, low-value workflows and prioritize optimization opportunities.
Help leaders answer the questions regulators, auditors, and boards will ask.
As AI becomes part of business operations, organizations need to explain who used the system, what the purpose was, what data was used, whether sensitive data was protected, which model was selected, whether the response was grounded, whether policy was applied, whether human approval was required, what the workflow cost, and whether the final outcome was accepted or corrected.
Inline control, governed execution, and full observability.
Cortexa brings together five major capabilities across connection, governance, reasoning, memory, lineage, telemetry, and tokenomics.
Cortexa Nexus
Enterprise connection fabric for apps, documents, APIs, data stores, events, agentic systems, chatbots, and external tools.
Cortexa Govern
Policy, trust, intent control, guardrails, PII controls, approval gates, governed context, tenant policies, and risk boundaries.
Cortexa Core
Cognitive reasoning and orchestration for intent dispatch, state management, planning, challenger checks, composition, and assurance.
Cortexa Fabric
Enterprise memory and lineage through decision ledger, knowledge graph, semantic cache, feedback store, evidence records, and retrieval index.
Cortexa Signal
Cognitive telemetry and observability for tracing, cost attribution, quality scoring, drift detection, evaluation, dashboards, and tokenomics.
Cortexa turns AI from experimentation into governed operations.
| Without Cortexa | With Cortexa |
|---|---|
| AI usage is fragmented Apps, agents, chatbots, and models operate without a common control layer. |
AI usage is coordinated Cortexa provides a common inline harness across enterprise AI workflows. |
| Governance is manual Policies are applied inconsistently or reviewed only after execution. |
Governance is enforced inline Policy, PII, approvals, and guardrails are applied in the AI request path. |
| Costs are difficult to predict Token usage, model calls, retries, and agent loops are not clearly attributed. |
Tokenomics are transparent Cost attribution is visible by workflow, model, project, department, and user. |
| Decision logic is opaque Teams cannot easily explain context, evidence, model selection, or reasoning path. |
Decision lineage is preserved Context, model selection, evidence, reasoning path, and outcome are traceable. |
| Agentic workflows can create uncontrolled loops Retries, tool usage, and escalation are difficult to control. |
Agentic workflows are governed Cortexa controls retries, tools, state, approvals, escalation, and cost. |
| Business, IT, compliance, and finance see different views Each team works with partial visibility. |
Teams get a shared operational view Cortexa gives visibility into AI usage, risk, quality, and cost. |
Start where visibility, governance, and cost matter most.
Cortexa pilots should focus on workflows where AI is already being used or planned, but where leadership needs better control, transparency, and measurable value.
AI Chatbot Governance
Wrap an existing enterprise chatbot with Cortexa.
- Common user intents
- Responses requiring governance
- Models being used
- Token cost
- Low-confidence outputs
- Hallucination risk
- Human review needs
Agentic Workflow Control
Place Cortexa inline with an agentic workflow.
- Tool usage
- Approval gates
- Retry limits
- Context boundaries
- Sensitive data exposure
- Cost escalation
- Decision records
Enterprise RAG Observability
Monitor and improve an existing RAG system.
- Retrieval quality
- Citation quality
- Context relevance
- Answer groundedness
- Token consumption
- Latency
- Quality-cost tradeoff
AI Cost and Tokenomics Control
Use Cortexa Signal to understand AI spend.
- Cost by model
- Cost by workflow
- Cost by department
- Cost by project
- Cost by user group
- Cost by request type
- Cost versus quality score
Better control, stronger visibility, and faster AI scale-up.
Smarter decisions
Understand not only the AI output, but also the evidence, context, cost, and governance behind it.
Better AI governance
Apply policy directly into execution rather than leaving governance outside the workflow.
Reduced AI risk
Detect sensitive data exposure, weak grounding, risky outputs, approval needs, and uncontrolled agent behavior.
Cost visibility
Make AI cost measurable by tokens, models, workflows, users, tenants, and business purpose.
Model flexibility
Use multiple models and select the right model for the right task without lock-in.
Faster AI scale-up
Move from isolated AI pilots to controlled, observable, and reusable AI operations.
Designed for organizations adopting AI at enterprise scale.
Cortexa is best suited for organizations that need better control, visibility, accountability, and cost transparency across AI workflows.
Best-fit customers
Best-fit buyers
A control and intelligence layer for enterprise AI operations.
AI governance harness
Bring policy, guardrails, approvals, and risk controls directly into AI execution.
AI observability layer
Track requests, context, model calls, tokens, latency, quality, cost, and outcomes.
Agentic workflow control
Control tool usage, retries, state, approvals, escalation, and execution boundaries.
Model routing and optimization
Select the right model, tool, or workflow path based on intent, complexity, risk, and cost.
Decision lineage layer
Preserve context, evidence, model selection, reasoning path, review actions, and final outcomes.
RAG quality and grounding
Monitor retrieval quality, citation strength, groundedness, hallucination risk, and context relevance.
Bring one AI workflow. We will show what is happening inside it.
Start with one chatbot, one RAG system, one agentic workflow, or one document-heavy AI process. Cortexa helps leadership, technology, governance, and finance teams see how AI is being used, whether it is governed, what it costs, and whether it is producing trustworthy outcomes.