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Introducing Spenta
You run projections. You build spreadsheets. You rely on experience. What you don't have is a way to see what actually happens — across thousands of scenarios, with real operational complexity built in — before you commit.
Simulation has always been the right answer. It has never been accessible — until now.
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Model what happens to your supply chain if demand spikes, a supplier goes dark, or lead times double — before any of it happens. A calibrated digital twin turns uncertainty into a range of tested, quantified outcomes.
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Before committing capital to a new distribution center, network redesign, or fulfillment strategy — simulate it. Know the impact, the tradeoffs, and the edge cases before the contract is signed.
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Spreadsheets assume one thing leads to the next. Real supply chains don't work that way. A capacity call, a routing change, an inventory policy — Spenta shows you the operational and financial consequence at the same time.
The defining transition of enterprise AI · 2025–2026
The previous generation focused on assistance: autocomplete, suggestions, drafts. The new paradigm centers on agentic systems that execute tasks end-to-end. Spenta is that shift applied to simulation and digital twins.
AI agents have crossed a threshold. Not the threshold of being impressive — the threshold of being genuinely reliable on complex technical work. The kind of work that building a calibrated simulation model requires: reasoning about system structure, inferring parameters from operational data, iterating until outputs match observed reality.
This is the AI of end-to-end execution — systems that take a problem and return a solved answer. The unit of economic value has shifted from the software license to the outcome itself.
Spenta is the first application of this capability to industrial simulation and digital twins. The organizations that adopt it now will make better decisions, faster, than the ones that wait. That gap compounds with every quarter.
Before
Per-seat licenses. Specialists required to operate them. Months of model-building to answer one set of questions — then rebuild when the questions change. Outputs difficult to explain to anyone who wasn't in the room. A process so expensive and slow that most decisions get made without simulation at all.
Now
Describe the operational question. Spenta builds the model, calibrates it, runs the scenarios, and delivers the answer. No software license. No specialist team. No months of setup. Simulation at the speed of the decision is here — at a fraction of what it used to cost. The transition from selling software to selling work is not coming. It is here.
When simulation takes months and costs hundreds of thousands, you hesitate to use it across your operations for every critical decision. When it takes days and costs a fraction, you use it for every decision that carries real consequence. That is a different organization — and a different competitive position.
"What happens to our service levels if our primary supplier goes dark for six weeks?"
Map your exposure across disruption scenarios before you need to manage a crisis. Understand which nodes absorb the shock, what the recovery curve looks like, and what the P&L consequence is at each intervention level.
"What's the real ramp curve on this facility — and when do we actually break even?"
Before the capital is committed, simulate it. Understand the breakeven dynamics, the demand sensitivity, and the operational risk profile. The kind of analysis that changes what happens in the approval meeting.
"How much inventory should we carry under realistic demand variability — not a smoothed assumption?"
Build policy on tested models. Understand the service level achievable at each inventory position across your real demand distribution. Stop guessing and start knowing what the tradeoff actually costs.
Operations and the P&L on one graph.
Most tools simulate the operation or the financials. Spenta does both — so a capacity call, a routing change, or an inventory policy decision shows up in the KPI and the P&L at the same time. The operational consequence and the financial consequence, in the same output, with the same calibration evidence behind them.
This is the output that changes the capital approval meeting. Not because it's impressive — because it answers the question your CFO is going to ask before they ask it.
An AI-native expert, thinking in our methodology, running on our engine — building your digital twin.
Not an AI wrapper on legacy simulation software — three purpose-built layers that produce one artifact: a calibrated digital twin of your operation.
The agent you work with. Interprets your questions, builds your models, checks the output against real operational data, and returns findings your team can act on.
Our modeling language for complex adaptive systems. Agent-native from the ground up, not adapted from legacy tools.
Purpose-built simulation infrastructure. Designed from day-one to be operated by an AI agent — not adapted from legacy software.
How Heech, Mainyu, and Spenta work together — architecture, calibration, and what makes this genuinely different.
The entire simulation lifecycle — scoping, model construction, calibration, scenario analysis, reporting — is handled by Spenta, thinking in Mainyu and running on Heech. You describe the operational question. The rest happens inside the system.
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In plain language. What do you need to understand? What assumptions are you carrying that you can't afford to be wrong about? What's the decision on the other side of this analysis?
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Spenta constructs the model, calibrates it against your operational data, and runs the scenarios you need — plus the ones that surface once the model is behaving. You can follow the reasoning at every step.
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A structured report: model scope, assumptions, calibration accuracy, scenario results, and findings. Formatted for the decision-maker, not the modeler. Ready to go in front of your leadership.
We are accepting a limited number of early-access engagements right now — each one a first digital twin built together with your team. Every early-access team gets priority onboarding, a dedicated simulation expert, and a direct line into how Spenta evolves before the general release.
Now · Early access
Open today. A curated cohort of supply chain teams running Spenta on real engagements — guided onboarding, full calibration reporting, direct access to the Simuland team.
Q3 2026 · General availability
Open enrollment across supply chain and adjacent domains. Self-serve onboarding, API access, enterprise deployment paths.
Spenta, Mainyu, and Heech are our three-layer stack — the agent, the modeling language for complex adaptive systems, and the simulation engine. Years of research and engineering went into building each one. None of them is a wrapper.
The reasoning layer Spenta plugs into is modular by design. Today Spenta runs on Claude — Anthropic's frontier AI model — and Anthropic's investment in reasoning, reliability, and safety shapes how Spenta interprets operational context and constructs simulation logic.
OpenAI support is coming next. Other frontier LLMs after that. The three-layer stack stays the same — what changes is which reasoning layer is underneath. Your engagement isn't dependent on a single model vendor.
The result is an agent that reads an operations problem the way a senior analyst would, and builds a simulation model the way a specialist engineer would — built on our own engine, our own methodology, plugged into the best reasoning layer available.
Spenta is the first simulation and digital twin agent built for supply chain. Request early access — limited seats · general availability Q3 2026.
If your organization currently depends on AutoMod for supply chain or material handling simulation, a Spenta-powered engagement is the lowest-risk migration path. We scope in parallel with your existing work, validate the output against your AutoMod baseline, and give you a clear path to continuity well before the deadline.
Migrating under deadline pressure is expensive and high-risk. Starting now is not.
Spenta is an agent that compiles your system into executable models. It thinks in Mainyu — our proprietary modeling methodology for complex adaptive systems — and runs those models inside Heech, our simulation engine. One agent, one methodology, one runtime.
before → { simulation expertise, tools to learn, months of setup } ↓ after → Spenta You describe the operational question. Spenta figures out the rest — and returns an answer your leadership can act on.
Built for simulation, from the ground up
Spenta was built from the ground up for simulation and digital twin modeling. Domain knowledge, data structures, and calibration standards live in every layer — not bolted on after the fact.
Operates at enterprise grade
Industrial decisions carry real financial and operational consequence. Spenta calibrates against your historical data, verifies every finding against the agreed specification before reporting, and delivers work to senior-consulting standards.
Executes end to end — not assists
Spenta takes your operational question and returns a verified, calibrated, explainable answer. Scoping, model construction, calibration, scenario analysis, and reporting — all executed by Spenta. No specialist required as a prerequisite.
Every model ships with a structured report. Scope, assumptions, calibration evidence, scenario results, findings — a deliverable built to senior-consulting standards, with each model Spenta produces. And every twin is inspectable and editable by human modelers — an auditable digital-twin asset, not a sealed output.
Spenta does this with a new methodology we invented (Mainyu) and a simulation platform we built (Heech).Simulation has always been the only method that lets you test a decision against the full complexity of your real operation before you commit — before the capital moves, before the process changes, before the supply chain is staked on an assumption. It has always worked.
But accessing it required tens of thousands of dollars in software license and a long-term commitment, a specialist team that takes months to build a model, and outputs that are difficult to explain to anyone outside the room. So most critical decisions get made without simulation — on intuition, on spreadsheets, on the assumption that the next investment will behave roughly like the last one.
That assumption is wrong more often than anyone wants to admit.
What you model is not one kind of object. It is a spectrum: parts and relationships, nonlinear interactions, adaptive behavior, and finally independent systems coupled into a larger whole.
Each step up the ladder adds a new manifestation of complexity: more parts, more relationships, more feedback, more autonomy, and more cross-system coordination. Past a certain point, a human modeler cannot hold all the configurations in their head. That is the problem we are automating away.
The last rung is a system of systems. Once independent systems are coupled, the next question is coordination: who sets the purpose, who keeps autonomy, and how much central control exists.
Directed
Central authority manages the integrated whole — a factory floor with sequenced stations.
Acknowledged
Shared SoS objectives, but constituent systems keep ownership and goals — a supply chain with SLAs.
Collaborative
Systems coordinate voluntarily around agreed purposes — trucks sharing a loading dock.
Virtual
No central management or agreed purpose; behavior emerges through interaction — market dynamics.
One supply-chain model can be a mix of all four: a directed factory floor inside an acknowledged logistics network where carriers collaborate for dock access, all operating in a virtual market.
Simulation is the method of moving a system from a state to a future state through computation. Given a state at time t, simulation computes the state at t + Δt.
Repeat this step enough times and you stop describing what the system does; you start anticipating what it will do.
Spenta is The Expert you work with. Mainyu is the method Spenta thinks in. Heech is the tool Spenta runs on. The methodology and the tool are needed — but you are working with The Expert.
The math we invented — for the agent to use.
Mainyu is a new methodology for modeling complex adaptive systems — graph-theoretic, MBSE-based, meta-testable. Entities, relationships, behaviors, and state all coexist in one formal structure — built from the ground up for an AI agent to reason over. One structure. One truth.
The tool Spenta uses — our code, our engine.
Heech runs the simulation forward in time. Only events modify the world — every state change is traceable. GPU-native · distributed · federated · agent-visible.
The same digital twin can be pointed at time in three different ways — each asking a different question about movement through state.
A digital twin is the living synthesis: historical records from your systems of record, live telemetry from reality, and simulation projecting forward. All three streams, one graph artifact — updated continuously.
Spenta is a new category — built from the ground up for simulation and digital twins, powered by Anthropic's Claude and NVIDIA's compute stack, and grounded in decades of practitioner experience. There is nothing else like it.
Simulation models inform decisions that move capital, change operations, and carry real consequences. We know that.
What follows is the path from your domain knowledge to a running digital twin — how Spenta works, how it's verified and calibrated, and the mechanisms that keep you and your leadership confident in the output.
The end is one app — your simulation control tower, where you visualize, experiment, and converse. The process below is how we get there.
Every engagement starts with a question. "What happens to service levels if we consolidate two DCs?" "How robust is this plan against a 20% demand shock?" "What's the real cost of running at 95% utilisation instead of 85?" A single sentence from a leadership meeting — and the entire chain of reasoning that follows.
That question is the spark. Spenta takes it from there — drawing on its domain library, picking up your data where you let it, and holding the original intent through every step between the question and an evidence-backed answer. Nothing moves without that intent behind it.
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We learn the schema of your data — the entities you care about, their relationships, the fields you already track. Your data never leaves your environment. Only the schema travels.
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Choose what the simulation should reason about: the past (replay + counterfactuals), the present (live state), the future (scenarios) — or the whole continuum.
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A data model is set up inside your systems — on your side — that surfaces the entities and behaviors the simulation needs. Information stays where it lives.
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You choose how simulation shows up in your work: a what-if tool, a live control tower, a forecasting surface, counterfactual replay — or more than one at once.
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Spenta authors the model in Mainyu and delivers it through Heech — verified, calibrated, reasoning-visible. The build trail is auditable any time.
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Your simulation control tower in one app. Three verbs govern every interaction — visualize, experiment, converse.
Inside the app, the work unfolds as a natural arc — explain the context · configure the scenarios · watch them play · run the experiments · read the results · analyze what they mean · decide what to do · and when ready · act on the real system.
The standard for an AI agent informing operational decisions isn't "impressive." It's "correct — and provably so." Six guarantees stand behind every finding — so your team doesn't just receive the result, they interrogate it, defend it, and own it.
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The model obeys the same constraints your operation does. A truck with no fuel does not move. A dock with no slot does not load. Findings reflect real-world limits — not idealised physics.
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When the model says an action is possible, it actually is. When it says blocked, it actually is. The scenarios you compare are all real possibilities — not hallucinations.
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Same inputs, same outputs — every time. That is what lets a finding be defended in front of your board, stress-tested in a review, or handed to an auditor without a translation layer. A result you cannot re-run is a result you cannot explain.
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Ask "what happened?" at any point and get a complete, correct answer. Every number in the final report traces back to the moment its pattern started — minute by minute, cause by cause. This is where insight lives.
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Your experts understand the decision space. The model opens under the same tools your analysts already use — sensitivity sweeps, what-if analysis, parameter importance. You can ask the model questions the scenarios did not.
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Your supply-chain model plugged into your grid model is still trustworthy. Growing the digital twin across domains does not require rebuilding trust from scratch. Trust scales with the model.
No black box.
Explain the model. Explain the outcome. See the model. Run experiments. See the future.
Five steps · from your input to a report your leadership can defend without us in the room. Each step is visible to your team as it happens — no handoff, no waiting on a finished artifact.
You describe your simulation project in plain language — the operational context, the questions you need answered, the decisions on the other side of the analysis, and the data you can provide. There are no forms to fill, no configuration files, no software to learn. What goes into the model comes from you, in your own words, and Spenta confirms its understanding before anything is built. If something is missing or ambiguous, it asks. Nothing is assumed without your knowledge.
Example input
"We operate a regional distribution network across seven nodes. We're evaluating whether to consolidate two of our Southeast DCs into a single larger facility. I need to understand the service level impact under current demand, a 20% demand increase scenario, and what happens if our primary carrier reduces capacity by 30% during the transition. Our historical throughput and order data goes back 18 months."
As Spenta constructs the simulation model, you can follow every step — the assumptions it's making, the parameters it's setting, and the logic behind each choice. This isn't a log file for engineers. It's a readable account of how the model is being built (in real-time), written for someone who understands the operation, not the software. At any point before calibration begins, you can question an assumption, correct an input, or redirect the model structure. The build process is not a black box you hand off and wait on.
Before a single scenario is executed, Spenta verifies the model structure against the agreed specification and calibrates it against your historical operational data. You see the accuracy numbers directly — the gap between what the model would have predicted and what actually happened, period by period, across the metrics that matter to your operation. Calibration thresholds must be met before analysis begins. If they aren't, Spenta iterates. No finding enters your report until the model has demonstrated it can reproduce your past accurately enough to be trusted with your future.
Every scenario run is recorded with its full input set — the assumptions applied, the parameters used, the conditions defined. If a finding is questioned six months after the engagement closes, you can trace it back to the exact model state and scenario configuration that produced it. There are no undocumented runs, no results that exist only in memory. The analytical trail is complete, and it belongs to you.
Every model concludes with a structured report built to senior-consulting standards — not an AI-generated summary. It documents the model scope, the assumptions made and why, the calibration accuracy achieved, the scenario definitions, and the findings with their supporting evidence. A reader who had no involvement in the engagement can pick it up, follow the logic, and defend the conclusions. That is the standard we hold ourselves to, because we know where this report ends up: in front of your leadership, without us there to explain it.
For organizations that want to validate Spenta against their own judgment before relying on it for a live decision, we offer a structured side-by-side onboarding protocol — with defined timelines and explicit success criteria agreed upfront.
Spenta runs in parallel with your existing simulation process or internal analysis. You compare outputs, question assumptions, and build confidence in the calibration before Spenta's findings carry decision weight.
What shadow mode includes
The report is a structured deliverable that documents everything — model structure, data inputs, calibration evidence, scenario definitions, and findings. It is written to stand on its own in front of your leadership, your board, or your CFO. You own it entirely.
Simulation Model Report — Spenta
Confidential · Client deliverableSection 1
The operational system modeled, the boundaries set, the entities included, and the logic governing their behavior. Documented so any practitioner can reconstruct the model.
Section 2
Every assumption made during model construction, with its source and the reasoning behind it. Every data input, with provenance. Nothing undocumented.
Section 3
The gap between what the model predicted and what actually happened, period by period, across the KPIs that matter. Includes the calibration threshold met before scenarios began.
Section 4
Every scenario run, with its full input set: conditions defined, parameters applied, assumptions modified. Each run is logged and traceable to the model state that produced it.
Section 5
What the scenarios show, what it means for the operational decision, and where the uncertainty lives. Written for the decision-maker, not the modeler.
Section 6
Full model documentation for your records. You own the model and the underlying documentation completely — no dependency on Simuland to interpret or re-run it.
Spenta works against an abstraction over your systems — never your raw databases. Model logic runs inside a confidential compute enclave. Your data never leaves your environment; Spenta sees the shape of your operation, not its contents.
Spenta never connects to your raw databases or operational systems directly. It works with a structured description of your system — one you control, define, and that contains only what you choose to share.
Model logic and scenario analysis run inside a confidential compute environment. The shape of your operation informs the model. The contents of your systems do not leave your environment.
The simulation model, the calibration record, and the final report are yours entirely. No ongoing dependency on Simuland to access, re-run, or interpret your own analysis.
If you're a simulation modeler, consultant, or practitioner reading this page — we want to be direct with you.
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Spenta automates the parts of simulation and digital twin work that consume time without requiring expertise: model scaffolding, parameter configuration, calibration iteration, and report formatting.
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It does not automate judgment, problem framing, or the kind of domain insight that separates a good simulation from a technically correct but useless one. That remains human — and remains central to how every engagement runs.
Every model Spenta builds is inspectable and editable by your modelers — an auditable asset, not a sealed output. You can open it, extend it, correct it, specialize it. Your team keeps the keys.
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Many simulation professionals find that Spenta expands what they can deliver — more engagements, faster turnaround, and more time spent on the work that actually requires them. If you're interested in exploring what that looks like in practice, we'd be glad to talk.
Describe the operational decision you're trying to make. We'll confirm whether Spenta is the right fit and what an early-access engagement would look like.
Engagements are accepted on a selective basis during Spenta's early access period.
We offer two modes of working together. During Spenta's early access period, our practitioners run every engagement directly — Spenta as the engine, Simuland as the team. After general availability in Q3 2026, we also offer independent simulation modeling and digital twin consulting across supply chain, logistics, and operations.
Either way: the same practitioners, the same calibration standard, the same deliverable.
During Spenta's early access period, Simuland's simulation team works directly with a select number of organizations — using Spenta as the delivery engine, with our practitioners guiding the engagement from problem definition through to the final report.
You get the rigor of experienced simulation specialists and the speed of an AI agent — operated by the team that built Spenta, on real engagements they're accountable for. The deliverable is the engagement, not a beta test.
How an engagement runs
Weeks, not months. Fixed deadline. Fixed fee. One report.
Engagement timeline
Fixed scope · Fixed fee · Fixed deadline · Agreed before we start
Scoping call & written proposal
We confirm the operational question, the data you can share, the scenarios that matter, and the success criteria. You receive a written scope with fixed fee and dates before anything else starts. No surprises, no scope creep.
Model build & calibration
Spenta constructs the model; our team oversees assumptions and structure. Calibration runs against your historical data until the accuracy threshold is met. You see, line by line, where the model agrees with your data and where it doesn't.
Scenario analysis
Scenario analysis begins while calibration closes. We run the scenarios you defined and the ones that surface once the model is behaving. Every run is logged and traceable. We pause for a midway review with you once the core scenarios have landed — before continuing into the secondary set.
Report & handoff
Final structured report: model scope, assumptions, calibration accuracy, scenario definitions, and findings. We walk your team through it live, then hand it over. You own the report and the underlying model documentation completely — no dependency on Simuland to interpret it.
Every engagement starts with a real operational question — not a software capability. These are some of the questions we have answered for our clients. If your question isn't here, tell us what it is. We'll tell you whether simulation can answer it.
"What happens to our service levels if our primary supplier goes dark for six weeks?"
Map your exposure across disruption scenarios before you need to manage a crisis. Understand which nodes absorb the shock, which don't, and what the recovery curve looks like under each intervention option.
"What's the actual ceiling on our throughput — and what would it cost to raise it by 20%?"
Find the constraints spreadsheets miss. Identify the binding bottleneck, model the intervention, and understand the ramp curve and return before committing capital to a capacity expansion.
"What's the real ramp curve on this new facility — and when do we actually break even?"
Before the capital is committed, simulate it. Understand the breakeven dynamics, the demand sensitivity, and the operational risk profile — with calibration accuracy your CFO can cite in the approval document.
"How much inventory should we carry, and where — under realistic demand variability?"
Build inventory and service level policy on tested models, not rules of thumb. Understand the service level achievable at each inventory position across your real demand distribution — not a smoothed assumption.
What makes Spenta different
Operations and the P&L on one graph.
Most tools simulate the operation or the financials. Spenta does both — so a capacity call, a routing change, or an inventory policy decision shows up in the KPI and the P&L at the same time. The operational consequence and the financial consequence, in the same output, with the same calibration evidence behind them.
This is the answer to the question every operations leader eventually faces: "My simulation shows we should do X — but what does that actually mean for the business?" With Spenta, that question is built into the model from the start.
Simuland's practitioners offer independent simulation modeling and digital twin consulting. The same team. The same calibration discipline. The same practitioner-grade deliverable — delivered through our traditional engagement model for projects where a fully custom approach is required.
This is the work we've been doing for decades. Spenta was built to scale it. When you need the human-led version — for complexity, sensitivity, or regulatory reasons — it's available.
Discrete-event simulation
Full-scope simulation model design, build, verification, and calibration. Supply chain networks, material handling systems, logistics operations, and manufacturing flows. Any complexity. Any fidelity requirement.
Digital twin development
Living models of your operational system that update as your operation changes. Used for ongoing scenario analysis, capacity planning, and operational decision support — not one-time analysis.
Legacy model migration
Migration of existing simulation models — including AutoMod — to modern platforms with full baseline comparison against your original model's behavior. Continuity documented before the old model is retired.
Simulation program design
For organizations building an internal simulation capability: program architecture, tool selection, practitioner training, model governance, and the standards that keep simulation outputs trusted over time.
Operational decision analysis
One-off analytical engagements for a specific decision — network redesign, facility investment, inventory policy, or disruption response. Fixed scope, structured deliverable.
Tell us the problem
If your project doesn't fit a standard category — the scale, the sensitivity, or the system — describe it. We'll tell you whether simulation is the right tool and what an engagement would look like.
Describe your project →If your organization currently depends on AutoMod for supply chain or material handling simulation, a Spenta-powered engagement is the lowest-risk migration path. We scope in parallel with your existing work, validate the output against your AutoMod baseline, and give you a clear path to continuity well before the deadline.
Migrating under deadline pressure is expensive and high-risk. Starting now is not.
Describe the operational decision you're trying to make — or the problem you're trying to understand. We'll confirm whether simulation is the right tool, what an engagement would look like, and whether Spenta or our general consulting model is the better fit for your context.
Engagements are accepted on a selective basis during Spenta's early access period. We take on a limited number of clients per quarter to ensure quality.
Request a scoping call
We respond within one business day
Simuland was founded by PhD researchers and practitioners who spent years doing simulation work the hard way — building models, running analyses, and helping organizations navigate decisions that carried real operational and financial consequence. The work produced real value. The process produced real frustration.
The technical foundation carries doctoral-level expertise in simulation and decades of building and validating large-scale models and digital twins across supply chain, manufacturing, and logistics. The methodological rigor behind how Spenta constructs, calibrates, and validates models comes directly from that depth of practice — not from a team that learned simulation to build a software product, but from a team that built a product because they had spent years building, calibrating, and defending real simulation models, and knew exactly what was broken.
Doctoral-level simulation expertise.
Published work in simulation methodology, model calibration, and digital twin validation underpins every design decision in Heech, Mainyu, and Spenta.
Decades of real engagements.
Supply chain, manufacturing, logistics. The patterns, failure modes, and calibration standards encoded in the platform come from hundreds of actual models built for real decisions.
Both sides of the problem.
Team members who have built simulation models for enterprise decisions and been responsible for the decisions those models informed — inside the room when the model is right, and when it isn't.
The organizations that use simulation rigorously make better decisions — they invest in the right places, avoid expensive mistakes, and manage operational complexity before it becomes a crisis. Yet most organizations don't. Not because the methodology doesn't work, but because it was buried under thirty years of tools, licenses, and specialists that most organizations could never justify.
The original thesis for Simuland was never really about software. It was about removing the barriers that keep simulation locked away from the people and organizations that need it most. Expensive legacy tools. Steep learning curves. Simulation projects that take months of overhead before they deliver any value — if they ever do.
We believe simulation is dramatically underused — not because the methodology was wrong, but because the cost of entry was simply too high for most organizations to clear — in software, in expertise, and in time. So we fixed it. We built a three-layer technology stack to do it.
Purpose-built simulation infrastructure designed from the ground up to be operated by an AI agent — not adapted from legacy software built for human operators navigating a GUI.
The simulation methodology layer — encoding decades of practitioner knowledge about how models are scoped, calibrated, validated, and made trustworthy enough to inform real decisions.
The agent that brings all of it to any operational question — without software to learn, without a learning curve, without months of setup before any value is delivered.
There's no software to learn.
The agent is the interface. You describe the question — Spenta handles everything required to answer it.
There's no learning curve.
You describe the problem, not the model. Domain knowledge is the input. Simulation expertise is not a prerequisite.
Time to value goes from months to days.
The scoping, model construction, calibration, and scenario analysis that used to take months now happens inside a single structured engagement — measured in weeks, not months.
The overhead that used to kill projects before they started is gone.
No license procurement. No specialist hiring. No configuration. The barriers that prevented most organizations from using simulation at all have been removed.
First, AI agents became capable of doing genuine cognitive work — not just searching or summarizing, but reasoning, building, and iterating on complex problems. The kind of work that building a simulation model requires.
Second, enterprise buyers shifted. The organizations we work with are no longer asking "what software should we buy?" They're asking "what outcome do I need, and how do I get it?" The appetite for tools that require internal expertise to operate is shrinking. The appetite for agents that deliver outcomes is growing.
Those two shifts create a specific opportunity: replace the entire simulation software stack — the licensing, the learning curve, the specialist dependency, the months of model-building — with an agent that takes your operational question and delivers a verified, calibrated, explainable answer. That's what Spenta is.
Spenta is built by practitioners who have spent careers on the other side of this — building models, defending outputs, and knowing exactly what it costs when a simulation is wrong and no one catches it until the decision has already been made.
That experience defines the standard. Calibrated against real operational data. Tested across real operational contexts. Reviewed by simulation practitioners whose job is to find what breaks — not confirm what works.
For simulation teams, Spenta removes the work that consumes time without requiring expertise: model scaffolding, parameter configuration, calibration iteration, report formatting. What it doesn't touch is judgment — the problem framing, the domain insight, the interpretation that separates a useful simulation from a technically correct one. That remains human and remains central to every engagement.
The goal is simple: simulation that any operations leader can commission, any simulation practitioner can trust, and any leadership team can act on.
Simuland was founded by practitioners whose published research helped define how simulation models are built, calibrated, and validated for enterprise operations — and who then spent decades doing exactly that work, for real organizations, on decisions that carried real consequence.
The business and technology leadership brings experience on the other side of the same problem: not building the models but being responsible for the decisions those models were meant to inform. That perspective — what it takes for a simulation output to actually change a decision — is built into how Spenta is designed.
Simuland's advisory council brings together a deliberately selected group of senior leaders across industry, academia, and enterprise practice.
The council includes senior operations and supply chain executives from Fortune 500 organizations who have managed the kinds of decisions Spenta is designed to inform — capital allocation, network design, disruption response, and capacity planning at the largest scale.
It includes leading researchers and faculty from top academic institutions in simulation, operations research, and industrial engineering — whose published work and institutional standing represent the highest standard in the field.
And it includes seasoned industry practitioners with decades of simulation consulting, digital twin development, and enterprise technology implementation — whose experience spans exactly the problems Spenta is designed to solve.
And far more reliable. Every operations leader managing a complex supply chain, distribution network, or manufacturing system should be able to ask hard questions and get tested, explainable answers — without needing a specialist, a six-figure software budget, or months to wait.
That's what we have built. It matters — for every operations leader who has been told that simulation is too expensive, too slow, or too specialized to bring to the decisions that actually shape their business.
Early access · Open now
General availability opens Q3 2026.
Tell us the operational question you're trying to answer.
We'll confirm fit within the week.
Request early access
Tell us the operational question.