Abstract
Enterprise AI is approaching a critical transition. The first generation of enterprise software recorded work. The next generation analyzed work. The current wave of AI is beginning to perform work.
This transition creates a new problem. If AI is going to perform work, route decisions, recommend actions, trigger workflows, or eventually automate business operations, it cannot rely only on language reasoning or historical reporting. It must understand the operating structure of the company itself.
A company is not just a collection of documents, dashboards, and workflows. It is a dynamic system of metrics, decisions, incentives, constraints, people, processes, events, and outcomes. Revenue, margin, working capital, quality, utilization, risk, customer experience, and enterprise value do not move independently. They interact through complex operational relationships and human decisions.
Traditional causal models provide an important foundation for understanding relationships between variables. But most causal approaches were not designed for the messy, incomplete, dynamic, and decision-heavy reality of enterprise operations. They often help explain what may have happened. Enterprises, however, increasingly need to know what should happen next.
CoModel AI is designed to address this gap. It creates a company-specific mathematical decision model that connects enterprise data, metrics, decisions, levers, objectives, constraints, workflows, risk, and outcomes. This model is designed to support relationship discovery, confidence-aware reasoning, scenario simulation, trade-off evaluation, constrained optimization, and safer automation.
This paper explains the enterprise limitations of existing causal models, what can be publicly shared about the mathematical architecture behind CoModel AI, and why a new company-specific decision layer is necessary for trusted enterprise AI.
1The Enterprise Problem with Existing Causal Models
Causal modeling is one of the most important foundations for trustworthy AI. The ability to reason beyond correlation is essential if an AI system is expected to recommend or automate decisions. In theory, causal models help answer a deeper question than conventional analytics: not merely what happened, but why it happened and what may happen if something changes.
That question matters enormously in the enterprise. A healthcare practice wants to know whether reducing no-shows will increase revenue or simply create more capacity problems. A manufacturer wants to know whether transferring inventory from one warehouse will improve fulfillment or damage working capital. An assisted living facility wants to know whether resident pricing reflects the true cost of care without compromising quality or compliance. A financial operator wants to know which operational changes will improve valuation without increasing risk.
These are not simple prediction problems. They are decision problems.
Most causal approaches, however, were developed for environments that are cleaner and more controlled than the average enterprise. They often assume reasonably structured data, stable variables, sufficient observations, and a well-defined analytical question. In many scientific, academic, or controlled business settings, those assumptions can be reasonable. But most enterprises do not operate like laboratories.
Companies are living systems. They are made of people, incentives, approvals, exceptions, policies, workarounds, software systems, incomplete records, changing market conditions, and constantly evolving constraints. The real signal behind enterprise outcomes is often distributed across multiple systems and buried inside operational events, audit trails, state changes, approvals, overrides, notes, and delayed downstream consequences.
This creates a fundamental gap between causal modeling as an analytical discipline and causal modeling as a foundation for enterprise decision automation.
The enterprise does not merely need to know that two variables are related. It needs to know whether the relationship can be trusted, whether it is stable, whether it is actionable, whether the business can control it, whether changing it will improve the target outcome, what second-order effects may appear, what constraints may be violated, and whether the decision should be automated or reviewed by a human.
That is the difference between causal discovery and decision-safe action.
2Why Enterprise Data Breaks Clean Causal Assumptions
In many organizations, the most important business data is fragmented before analysis even begins. Revenue may live in one system, workflow activity in another, customer behavior in another, approvals in another, and exceptions in spreadsheets or emails. Data may be incomplete, late, duplicated, manually corrected, or interpreted differently by different teams.
This is not an edge case. This is normal enterprise reality.
A healthcare practice may have claims data, EHR data, scheduling data, payer data, provider productivity data, and patient communication data, none of which perfectly agree. A manufacturer may have sales orders, production plans, BOM structures, purchase orders, warehouse stock, WIP inventory, quality holds, dispatch status, and vendor commitments distributed across different operational systems. An assisted living facility may have resident contracts, care plans, staffing logs, incident reports, medication support records, billing rules, and family communication history across multiple systems and formats.
A conventional causal model may struggle if the enterprise cannot present a clean dataset upfront. But in the enterprise context, the model must be able to work from imperfect operating evidence. The question is not whether the data is perfect. The question is whether there is enough structured evidence to support a responsible decision.
This is one of the reasons CoModel AI begins by converting enterprise data into a decision-ready structure. The goal is not to force every company into one generic schema of analysis. The goal is to represent the company through the objects that matter for decisioning: metrics, entities, events, decisions, levers, objectives, constraints, and outcomes.
This gives the model a more enterprise-native foundation. Instead of treating the business as a flat table of variables, CoModel AI treats the company as an operating system whose parts influence one another over time.
3Correlation Is Easy. Decision-Safe Causality Is Hard.
Modern analytics systems can find relationships between metrics. Many systems can show that two variables move together, that one tends to precede another, or that a pattern has appeared historically. This is useful, but it is not enough for enterprise AI.
The danger is that a relationship can look meaningful without being safe to act on.
For example, a model may find that revenue rises when provider utilization rises. But that does not automatically mean the company should push utilization higher. There may be burnout risk, quality risk, scheduling constraints, payer mix effects, or downstream denial issues. A model may discover that fulfillment improves when inventory is moved aggressively across warehouses. But that does not mean the transfer is always profitable, compliant, or operationally wise. A model may identify that resident profitability improves when care time is reduced. But in an assisted living context, that may be unacceptable, unsafe, or illegal.
Enterprise decisions are bounded by reality. They are constrained by capacity, compliance, quality, risk, labor, capital, service commitments, and human judgment.
This is why CoModel AI separates relationship evidence from decision-safe action. The existence of a relationship is not treated as permission to automate. A relationship must be evaluated in context. The model must understand not only whether variables are connected, but whether the connection is relevant, reliable, controllable, and safe to use in a decision.
This separation is central to the CoModel AI philosophy. The enterprise problem is not simply discovering relationships. The enterprise problem is deciding what should be done with those relationships.
4The Limits of Static Causal Graphs
Many causal approaches represent the world as a graph of relationships. That is a powerful idea. A graph can show how variables are connected, where influence may flow, and which variables may sit upstream or downstream of others.
But in an enterprise, the graph is rarely fixed.
Companies change constantly. Pricing changes. Policies change. Patient behavior changes. Demand shifts. Vendor performance changes. Managers override workflows. New systems are introduced. Incentives are modified. Regulatory requirements evolve. A relationship that was reliable last quarter may weaken or reverse under new operating conditions.
This dynamic nature matters because enterprise AI must often make decisions in the present, not explain a historical period after the fact.
A static graph can become dangerous if treated as permanently true. If the model does not update with new evidence, it may continue recommending actions based on outdated operating realities. That is especially risky in environments where margins, compliance, care quality, customer expectations, or capital allocation are at stake.
CoModel AI is designed around the idea that the company model must evolve. It treats the enterprise as a dynamic system whose relationships can strengthen, weaken, appear, disappear, or become unsafe depending on evidence, context, and constraints.
The model is not intended to be a one-time causal diagram. It is intended to become a living mathematical representation of the company.
5Enterprises Are Full of Feedback Loops
Classic causal analysis often prefers clean directional structures. In business, however, many of the most important relationships are not clean one-way chains. They are loops.
Better collections can improve cash flow. Better cash flow can allow more staffing. More staffing can improve service quality. Better service quality can improve retention. Better retention can improve revenue quality. Better revenue quality can improve valuation. Higher valuation can change access to capital, which then changes future operating decisions.
The same is true in healthcare operations, manufacturing, logistics, assisted living, financial services, and almost every complex enterprise environment. Decisions create outcomes, outcomes change constraints, constraints change future decisions, and future decisions reshape the operating system.
This is why enterprise decisioning requires more than a simple cause-and-effect chain. It requires a system view.
CoModel AI represents the company as an interconnected decision graph. This allows it to reason not only about direct effects, but also about indirect effects, downstream consequences, second-order trade-offs, and feedback behavior. The public mathematical idea is straightforward: the business is modeled as a network of interacting variables rather than a set of isolated metrics.
The proprietary details of how CoModel AI scores, weights, propagates, calibrates, and governs these relationships remain protected. But the public principle can be stated clearly: enterprise intelligence requires graph-based reasoning because enterprise outcomes are system outcomes.
6Controllability Is the Missing Enterprise Dimension
One of the biggest weaknesses of many analytical and causal approaches is that they identify variables that explain outcomes without distinguishing whether the company can actually change them.
This distinction is critical.
Macroeconomic demand may affect revenue, but most companies cannot directly control macroeconomic demand. Seasonality may affect utilization, but the company may not be able to change the season. A market trend may influence customer behavior, but that does not make it an actionable lever.
Enterprise AI must distinguish between variables that explain performance and variables that can be changed to improve performance.
This is where controllability becomes a central mathematical and operational concept. A decision model must know which variables are merely observed, which are predictive, which are constraints, which are objectives, and which are levers.
A lever is not just a variable. It is something the business can act on.
Examples include staffing levels, pricing policies, scheduling templates, approval thresholds, inventory transfer rules, denial follow-up workflows, purchasing decisions, production priorities, care review frequency, outreach cadence, discount rules, or escalation policies.
A model that cannot distinguish controllable levers from uncontrollable explanatory variables may produce recommendations that sound intelligent but cannot be executed. Worse, it may optimize attention around things the company cannot change while ignoring the operational levers that actually matter.
CoModel AI is designed to model controllability explicitly. This is one of the reasons it is positioned not merely as a causal model, but as a company-specific decision model.
7Constraints Are Not an Afterthought
In real enterprises, no important decision is made in isolation.
A healthcare practice cannot simply increase patient volume if provider capacity is limited. A manufacturer cannot simply accelerate fulfillment if inventory, production capacity, or quality controls are not ready. An assisted living facility cannot improve margin by reducing care below required levels. A financial services organization cannot optimize speed by violating compliance.
This is why constraints must be first-class objects in the model.
A constraint is not merely a note attached to a recommendation. It is part of the decision space itself. Cost, time, capacity, compliance, quality, SLA, risk, working capital, labor availability, contractual obligations, clinical standards, approval requirements, and customer or patient impact all shape what decisions are feasible.
Traditional causal models often focus on what affects what. Enterprises also need to know what is allowed, what is feasible, what is safe, and what is worth doing.
CoModel AI publicly can be described as constraint-aware. This means the model does not only estimate the possible effect of a change. It evaluates that change against the operating limits of the business.
This matters because enterprise AI should not recommend the theoretically highest-impact action if that action violates the business. It should identify the best available path under real-world constraints.
8From Causal Discovery to Enterprise Decision Intelligence
The full enterprise problem can be stated simply:
Given a business objective, the current company state, the available levers, operational constraints, uncertainty, and downstream trade-offs, what decision should be made now?
That question includes causality, but it is larger than causality.
It requires relationship discovery, but also controllability. It requires prediction, but also simulation. It requires optimization, but also constraints. It requires automation, but also governance. It requires mathematical reasoning, but also explainability.
This is why CoModel AI is better described as enterprise decision intelligence rather than a traditional causal modeling tool.
Causal modeling asks what influenced what. CoModel AI is designed to ask what the company should do next.
That distinction is fundamental.
Enterprise leaders do not buy causal graphs because they want graphs. They buy systems that help them improve margin, revenue, quality, risk, fulfillment, retention, care outcomes, working capital, and valuation. A mathematical model becomes valuable when it helps the organization make better decisions.
9What CoModel AI Can Publicly Reveal About the Math
CoModel AI’s internal mathematical methods are proprietary. The exact formulas, scoring functions, thresholds, detector fusion methods, confidence calibration, propagation logic, optimization functions, and decision-safety rules should remain confidential.
However, the high-level mathematical architecture can be described publicly.
CoModel AI combines several mathematical families into one enterprise decision layer. These include graph theory, probabilistic modeling, time-aware relationship discovery, confidence-weighted inference, decision geometry, uncertainty estimation, scenario simulation, and constraint-aware optimization.
The important point is not that CoModel AI uses a single mathematical trick. The important point is that it combines multiple mathematical layers to represent the company as a decision system.
At a high level, CoModel AI works through a sequence of modeling steps.
First, it structures enterprise data into decision-relevant objects. These include metrics, entities, events, decisions, levers, objectives, constraints, and outcomes. This allows the model to understand what the business measures, what it acts on, what it wants to improve, and what it cannot violate.
Second, it discovers relationships between variables. These relationships may be supported by time patterns, operational events, business rules, historical decisions, known workflows, or observed metric behavior. CoModel AI does not treat every relationship equally. Relationships are evaluated for strength, direction, confidence, stability, business plausibility, and decision relevance.
Third, it represents the company as a graph. In this graph, nodes represent business variables and decision objects, while edges represent relationships between them. This graph is not merely visual. It becomes the mathematical structure through which the system can reason about upstream drivers, downstream consequences, indirect effects, and trade-offs.
Fourth, CoModel AI evaluates which relationships are appropriate for simulation or decision support. This is a critical distinction. A relationship may be visible in the data but still not be reliable enough to automate. The model must distinguish between observed association, useful evidence, simulation-ready relationships, and decision-safe action.
Fifth, CoModel AI enables scenario simulation. A user or agent can ask what may happen if a controllable lever changes. The model evaluates expected outcome movement, downstream effects, uncertainty, constraints, and risks before action is taken.
Finally, CoModel AI supports optimization. It can help identify decision paths that improve an objective while respecting cost, time, capacity, risk, quality, compliance, and other enterprise constraints.
This public explanation reveals the category of the math without exposing the implementation.
The category is the company-specific decision model.
The secret is how CoModel AI builds, weights, calibrates, governs, and operationalizes that model.
10The Company as a Mathematical Decision Graph
The central public concept behind CoModel AI is the decision graph.
A company can be represented as a network of interacting variables. Some variables are metrics, such as revenue, margin, utilization, denial rate, inventory availability, fulfillment SLA, working capital, care quality, customer retention, or valuation. Some variables are decisions, such as whether to approve an order, transfer inventory, change pricing, escalate a claim, alter staffing, or adjust a care plan. Some variables are constraints, such as budget, capacity, compliance, risk tolerance, quality standards, or approval rules. Some variables are levers, meaning the business can actually change them.
This graph gives CoModel AI a structured way to reason about the business.
If the objective is to improve healthcare practice valuation, the model may need to understand how no-shows, denials, AR days, provider utilization, payer mix, patient retention, and operating margin interact. If the objective is manufacturing fulfillment, the model may need to understand how sales orders, BOM readiness, WIP, raw material availability, plant capacity, vendor reliability, margin, SLA risk, and working capital interact. If the objective is assisted living profitability, the model may need to understand how resident pricing, care consumption, staffing, service frequency, incidents, room type, discounts, quality, and compliance interact.
In each case, the model is not simply analyzing a static dataset. It is constructing a mathematical representation of how the company works.
This is the key distinction between CoModel AI and generic AI systems. A general-purpose language model can explain what denial rates are, what working capital means, or why pricing matters. CoModel AI is designed to model how those variables interact inside a specific company.
11The Concentric Circle Model
One way to publicly explain CoModel AI’s architecture is through the Concentric Circle Model.
The Concentric Circle Model organizes business variables according to their relationship to a target objective. For any outcome the company cares about, the model evaluates which metrics, decisions, and levers are closer to that outcome, which are farther away, which are upstream drivers, which are downstream consequences, which are controllable, and which are too uncertain to use safely.
This is important because enterprise decisioning requires prioritization.
Most companies have hundreds or thousands of metrics. Not all of them matter equally for a specific objective. Some metrics are direct drivers. Some are indirect signals. Some are symptoms. Some are constraints. Some are useful for explanation but not action. Some are actionable but risky. Some require human review.
The Concentric Circle Model gives the enterprise a way to organize this complexity.
If the target is valuation, the model may organize variables around revenue growth, revenue quality, margin, cash flow, retention, scalability, operational dependency, compliance exposure, and risk. If the target is fulfillment performance, the model may organize variables around inventory, production capacity, vendor reliability, order urgency, margin, working capital, SLA, and quality. If the target is practice growth, the model may organize variables around provider utilization, denials, no-shows, referral conversion, payer mix, patient retention, and revenue per visit.
The public concept is simple: not every metric sits at the same distance from the objective. CoModel AI builds a decision geometry around the outcome the company wants to improve.
The proprietary details of that geometry remain confidential. But the value can be clearly stated: CoModel AI helps enterprises identify which variables matter most, which levers are actionable, and which paths are safest to pursue.
12Simulation Before Automation
One of the most important principles behind CoModel AI is simulation before automation.
The current wave of enterprise AI is heavily focused on agents. Agents can draft emails, update systems, trigger workflows, classify requests, summarize information, and execute tasks. But the more capable agents become, the more dangerous poor reasoning becomes.
An agent that acts quickly without understanding business consequences can create operational damage at scale. It may optimize the wrong metric, violate constraints, ignore downstream effects, or execute a recommendation that sounds reasonable in language but fails in reality.
This is why enterprise AI needs a decision model beneath the agent layer.
CoModel AI is designed to evaluate possible decisions before they are executed. A proposed action can be simulated against the company model to estimate likely effects, risks, trade-offs, constraints, and confidence.
For example, before a healthcare practice automates a no-show reduction workflow, the model can evaluate whether reducing no-shows is likely to increase revenue, overload providers, affect patient experience, or shift bottlenecks elsewhere. Before a manufacturer chooses to transfer inventory, the model can evaluate margin impact, SLA improvement, working capital effect, and downstream shortage risk. Before an assisted living facility changes resident pricing, the model can evaluate care consumption, profitability, quality, compliance, and family communication risk.
The purpose of simulation is not to claim perfect prediction. The purpose is to improve decision quality before action is taken.
In enterprise AI, that difference matters.
13Uncertainty as a First-Class Feature
Enterprise models should not pretend to be more certain than the evidence allows.
Business data is noisy. Relationships change. Important variables may be missing. Some decisions are rare. Some outcomes appear only after a delay. Some teams override processes in ways that are not fully captured. Some external factors cannot be controlled.
A responsible decision model must therefore reason with uncertainty.
CoModel AI is designed to evaluate not only expected impact, but also confidence, risk, and data sufficiency. A high-impact recommendation with weak evidence should not be treated the same way as a high-impact recommendation with strong evidence. A low-risk decision with strong confidence may be suitable for automation. A high-risk decision with incomplete evidence may require human review. A decision with contradictory evidence may need more data before the model should recommend action.
This is essential for enterprise trust.
Executives do not simply need an answer. They need to know how much confidence to place in the answer, what evidence supports it, what assumptions are being made, what data is missing, and what could go wrong.
CoModel AI treats uncertainty not as a weakness to hide, but as a core part of decision governance.
14Optimization Under Real-World Constraints
Once a company has a mathematical decision model, it becomes possible to ask goal-seeking questions.
How do we improve margin without increasing risk? How do we increase revenue without overloading capacity? How do we reduce working capital without hurting fulfillment? How do we improve valuation without weakening quality? How do we increase profitability without compromising care?
These are optimization problems, but they are not simple maximization problems.
Enterprises do not want to maximize one metric at any cost. They want to improve the right objective while respecting the business constraints that define acceptable action.
This is where CoModel AI’s constraint-aware architecture matters. The model is designed to evaluate potential decision paths based not only on expected impact, but also cost, time, risk, feasibility, confidence, and constraint fit.
A recommendation is therefore not just a prediction. It is a path through the operating reality of the business.
This is a major distinction from dashboards and generic AI assistants. Dashboards show what happened. Assistants explain what might be relevant. CoModel AI is designed to help determine what action path is most likely to improve the desired outcome under the company’s actual constraints.
15Human-in-the-Loop Decision Governance
Trusted enterprise automation does not mean automating every decision.
Different decisions require different levels of governance. Some are low-risk and repeatable. Some are high-value but require approval. Some are sensitive because they involve compliance, care quality, pricing, capital allocation, or customer impact. Some should not be automated until the evidence is stronger.
CoModel AI is designed to support this spectrum.
At the lowest level, the model can explain relationships and surface evidence. At the next level, it can simulate possible decisions. Then it can recommend paths with confidence and trade-offs. It can route decisions to the right workflow or human approver. Finally, where confidence is sufficient and constraints allow, it can support automated execution.
This governance structure is important because enterprise AI must be both powerful and controlled.
The goal is not blind automation. The goal is decision-safe automation.
16Why LLMs Alone Are Not Enough
Large language models are extraordinary tools for language understanding and generation. They can read, summarize, draft, classify, explain, and converse. They are likely to become a central interface for enterprise software.
But language reasoning alone is not the same as company reasoning.
An LLM may understand the phrase “reduce denial rates to improve revenue.” But unless it has a mathematical model of the company, it does not automatically know which denial categories matter most, which payers are responsible, which workflows caused the denials, which staff interventions are available, how quickly cash flow will improve, what compliance risks exist, or whether the same intervention worked historically in similar conditions.
The same applies across industries. An LLM can explain inventory transfers, pricing changes, staffing decisions, or customer retention strategies. But enterprise decisions require knowledge of the company’s specific state, constraints, levers, relationships, and outcomes.
This is why CoModel AI should be understood as complementary to LLMs.
LLMs provide the language interface. CoModel AI provides the company-specific decision model.
Together, they can support a new kind of enterprise AI: systems that can communicate naturally while reasoning from the mathematical operating structure of the business.
17What Should Remain Proprietary
It is important to be precise about what can and cannot be revealed publicly.
CoModel AI can publicly describe the categories of mathematics it uses: graph theory, probabilistic modeling, time-aware relationship discovery, confidence-aware inference, decision geometry, simulation, uncertainty estimation, and constrained optimization.
It can also publicly describe the enterprise objects it models: metrics, entities, events, decisions, levers, objectives, constraints, workflows, risks, and outcomes.
It can explain that the company is represented as a dynamic decision graph. It can explain that relationships are evaluated for strength, direction, confidence, controllability, and decision relevance. It can explain that scenarios are simulated before action and that recommendations are evaluated against constraints.
But the proprietary implementation should remain confidential.
That includes exact scoring functions, detector fusion logic, internal weighting methods, thresholding rules, confidence calibration, propagation equations, optimization objective functions, sampling strategies, safety gates, and decision automation policies.
The public message should reveal the architecture, not the formula.
The category is public.
The secret sauce is how CoModel AI builds, calibrates, governs, and operationalizes the model.
18Enterprise Examples
Consider a healthcare practice trying to grow revenue and enterprise value. A dashboard may show that AR days are high, denial rates are high, no-shows are high, and provider utilization is low. A generic AI assistant may explain that each of these issues can affect revenue.
CoModel AI is designed to go further. It can model how those variables interact in that specific practice. It can evaluate which levers are controllable, which are closest to valuation, which interventions are likely to improve margin, which actions may create downstream risk, and which decisions should be automated versus reviewed.
In manufacturing, an urgent order may require decisions across finished goods inventory, WIP, raw materials, BOM readiness, production capacity, vendor reliability, quality risk, margin, SLA, and working capital. A report may show the data. CoModel AI is designed to evaluate the decision paths: transfer inventory, produce the remaining quantity, wait for open production, split fulfillment, escalate purchasing, or reprioritize production.
In assisted living, a facility may want to know whether each resident is priced correctly for the care they actually consume. This is not just a pricing calculation. It requires understanding care level, staff time, medication support, ADL assistance, incident history, room type, contracted rate, discounts, family communication, quality, margin, and compliance. CoModel AI is designed to model the relationship between care consumption, price, margin, quality, and risk so that pricing decisions are evidence-backed and operationally responsible.
Across these examples, the same principle holds: enterprises do not need AI that merely describes the business. They need AI that can reason about what should change.
19The Public Mathematical Claim
The public mathematical claim for CoModel AI should be disciplined and repeatable:
CoModel AI creates a company-specific mathematical decision model by connecting enterprise data, metrics, decisions, levers, objectives, constraints, workflows, risks, and outcomes into a confidence-aware graph that supports simulation and optimization.
This statement is strong without overexposing the implementation.
It positions CoModel AI clearly against dashboards, generic AI assistants, and conventional causal tools.
Dashboards report what happened. LLMs explain information through language. Traditional causal models help analyze relationships. CoModel AI is designed to model the company as a decision system.
That is the foundation for trusted enterprise automation.
20Conclusion
The enterprise problem is not causal discovery alone.
The enterprise problem is decision-safe action.
As AI moves from assisting work to performing work, companies will need a mathematical layer that understands their operating structure. Without such a layer, agents may execute tasks without understanding consequences, optimize the wrong metrics, violate constraints, or create downstream risk.
CoModel AI is designed to provide this missing layer.
It transforms enterprise data into a dynamic, confidence-aware decision graph that connects metrics, decisions, levers, objectives, constraints, workflows, risks, and outcomes. It is designed to discover relationships, evaluate confidence, simulate interventions, reason under uncertainty, optimize under constraints, and support human-governed automation.
Existing causal models help explain why things may have happened.
CoModel AI is designed to help enterprises decide what should happen next.
LLMs understand language. CoModel AI understands how your company works.