Cases you can watch end to end.
See an agent catch a fraudulent bank change, a duplicate payment or a hidden owner on synthetic documents, with every finding quoted and a person deciding.
Watch the agents workWe design and build agentic AI systems on solid cloud and data foundations, with people approving the decisions that matter. Describe a problem from any field and get a proposed workflow to review: the specialist roles, the human checkpoints, and how a pilot would be measured.
The clearest evidence of how we work is working software. These run here, today, and you can use them.
See an agent catch a fraudulent bank change, a duplicate payment or a hidden owner on synthetic documents, with every finding quoted and a person deciding.
Watch the agents workTen stages find recent research, check sources, draft and review. Nothing publishes until a person approves it, and every source is kept for audit.
How the stages coordinateRequest quotas, daily spending budgets, duplicate-run protection and bounded provider calls keep the AI features predictable to operate.
Security & technical strategyEach agent reads the document, runs the checks you set, and stops for a person to decide. Illustrations on demo data.
Three-way match on every line, with holds when price or quantity drifts from the PO and goods receipt.
HoldPayment blocked pending buyer review
ProcureAI is Stellitron's live procurement product — six coordinated agents for intake, invoices, contracts, supplier risk, sourcing, and spend.
Upload a purchase order, contract, or invoice and watch Stellitron Intelligence walk the match: line items, tolerances, and the approval path your team would expect before money moves.
Start with a guided demo on synthetic data, or register for a short trial and run your own dummy document — or generate one from a plain-language brief.
FinanceAI is Stellitron's live finance product — agents for bank reconciliation, KYC onboarding, and month-end close, each with a visible decision trace.
Paste a bank statement, a KYC pack, or a trial balance. Rules run first; Stellitron Intelligence then explains each break or red flag and quotes the line it relies on.
Nothing posts on its own: journals, dispositions, and close sign-off wait for a person to approve them. Try the guided demo on synthetic data, or register and run a sample document of your own.
The animations show how each product explains a decision — not a recording of your systems. Live trials run on procurement.stellitron.com and finance.stellitron.com with registration; engineering engagements on this site still start from your brief and a scoped pilot.
Useful agents need more than a model. We connect orchestration, data, infrastructure, and evaluation to the outcome your team needs.
Design agents around the work your team needs done. Coordinate reasoning, tools, and specialist roles with evaluation, explicit permissions, and human oversight.
Explore the capabilityGive your product a foundation that is observable, maintainable, and designed for its actual workload. Make the tradeoffs around cost and scale explicit.
Explore the capabilityConnect fragmented information and make it useful. Design pipelines and integrations that move the right data to the right place.
Explore the capabilityMake security and ownership part of the architecture. Understand your risks, prioritize the work, and give your team a practical technical roadmap.
Explore the capabilityYou should always know what we’re solving, why a decision was made, and what comes next.
Inside our approachStart with the people, the workflow, and the business goal. Agree on what success means before choosing the technology.
Map the architecture, test the riskiest assumptions, and shape a scope that fits your constraints.
Work in visible increments. Review functioning software together and use the feedback to guide the next decision.
Make deployment, documentation, observability, and handover part of the work from the beginning.
The questions teams usually ask before a first conversation.
Indicative bands start with a paid pilot from $15,000 for one workflow, then from $40,000 a year per agent module or from $120,000 for a full ProcureAI or FinanceAI suite — all in USD, excluding taxes. After a demo we send a written proposal sized to your volume, agents, deployment model and connectors. How we work with enterprises
A clear view of the problem, the constraints that matter, and a proposed next step. If you watch a case first, tell us which one is closest to your work, so the conversation starts from your specifics.
No. Scoping works from your description, and the interactive samples use synthetic data. Access to real systems is designed during architecture, with explicit permissions agreed before anything connects.
Only where you decide they should. We design human checkpoints for consequential actions, so people review and approve the decisions that carry real risk.
Yes. The examples illustrate the approach; they are not a boundary. Tell us about your workflow and we will show you how an agent would handle it.
Deployment, documentation, observability and handover are part of the work from the start, so your team can run and extend the system without depending on us.
Explore research, business possibilities, and the work behind a useful pilot. Each story connects primary sources with a clearly labeled implementation proposal.
Explore all field notes12 field notes across industries. These examples don’t limit what we can explore together.
The research behind planning, tools, and reliable execution.
A practical way to choose between fixed workflows, tool-using agents, and specialist teams—starting with a decision your business can actually verify.
2 primary sourcesWhat Co-Scientist and ChemCrow suggest about tool-assisted research, and a proposed evidence workflow that preserves uncertainty and expert review.
2 primary sourcesNetwork paths, secrets, SSO, logging, data residency, and roll-back when ProcureAI or FinanceAI workers run in your account instead of a public trial.
2 primary sourcesFrom a real operational question to a pilot you can measure.
How to turn quotes, delivery constraints and approval policies into a traceable comparison—without letting a model invent the winning supplier.
1 primary sourceWhy available stock, reserved units and location matter more than a convincing sales reply—and how to design an exception workflow around them.
1 primary sourceA read-only incident-assistance blueprint that joins equipment signals with manuals and maintenance history, while keeping control boundaries clear.
2 primary sourcesA logistics blueprint that separates constraint solving from conversational coordination, so proposed plans remain feasible and inspectable.
1 primary sourceWhich Coupa objects to read first, where ProcureAI agents sit in the AP loop, what buyers see during a pilot, and how to scope read-only access before any write-back.
1 primary sourceHow to scope OData read access, stage sandbox mapping, run a batch pilot, and defer hold codes or invoice blocks until your AP organisation signs off.
2 primary sourcesExplore systems where verification and human control come first.
A careful administrative blueprint for proposed appointments, resource checks and staff confirmation, grounded in the FHIR scheduling model.
1 primary sourceWhat OpenVLA shows about learned robot policies, and why a first business pilot should separate task planning from physical execution.
2 primary sourcesHow hospital AP and shared services use ProcureAI on supplier invoices and POs today, what data stays out of clinical systems, and what a future clinical-adjacent agent would still refuse to do. HIPAA compliance is not offered by Stellitron today.
2 primary sourcesA GOOD PLACE TO START
A conversation about your goals, constraints, and next step.
Recent arXiv papers discovered by the research workflow. Preprints and author claims require evaluation.
Multimodal large language models (MLLMs) have made rapid progress on visual reasoning, yet many tasks require evidence that is difficult to obtain through internal…
Excerpt from the authors’ abstract.
Evaluate for your businessWhile LLM agents can generate visually plausible CAD assemblies, these designs often fail to fulfill their intended physical functions. We study functional CAD design,…
Excerpt from the authors’ abstract.
Evaluate for your businessWorld models enable embodied agents to anticipate action consequences, yet jointly modeling locomotion and manipulation across diverse embodiments remains challenging due to heterogeneous action…
Excerpt from the authors’ abstract.
Evaluate for your businessReinforcement learning enables mobile robots to make fast and scalable navigation decisions in constrained human-shared environments. However, directly extending single-robot approaches to multi-robot settings…
Excerpt from the authors’ abstract.
Evaluate for your businessIn this paper, we systematically investigate the mechanisms, scaling behavior, and generalization of physical Recursive self-improvement. We introduce PhysRSI-eval to quantify physical RSI through…
Excerpt from the authors’ abstract.
Evaluate for your businessLong-horizon agents must learn heterogeneous capabilities from interleaved trajectories. Updating shared parameters to learn new capabilities can degrade previously acquired ones, resulting in catastrophic…
Excerpt from the authors’ abstract.
Evaluate for your businessA seemingly reasonable push in Sokoban can make the puzzle unsolvable. To avoid such mistakes, an agent has to anticipate the consequences of its…
Excerpt from the authors’ abstract.
Evaluate for your businessThe goal of a world model is to capture the dynamics of an environment. This has the great appeal that it allows agents to…
Excerpt from the authors’ abstract.
Evaluate for your business