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AI-Native Urban DMS
Distribución que AI Native DMS
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AI Native DMS for CPG distributors that automates order processing, billing, inventory, collections, reporting, and customer support with AI agents. Reduce manual work, eliminate operator dependency. del 30% al 50% del tiempo operativo diario. Integración nativa con Tally y Zoho Books.
4X Faster
Garantía de devolución de dinero
30% Clics
Configuración autoservicio
23+ Agentes
Para el flujo de trabajo del DMS
NO DMS
operador necesario
DMS Comparison
Traditional DMS vs AI Native DMS NextGen DMS
Traditional DMS records transactions. AI Native DMS automates distributor operations, helping businesses run faster, smarter, and with less manual work.
DMS estándar
Arquitectura monolítica con flujos de trabajo complejos
Implementación, personalización y mantenimiento continuo de alto costo.
UI/UX compleja y anticuada
Alto costo de capacitación. Baja adopción. Los distribuidores se resisten.
Alto costo de operación
La dependencia de un operador hace que el DMS sea inviable para distribuidores pequeños.
No se comunica con los sistemas contables
Trabajo duplicado entre el DMS y la contabilidad. Dolores de conciliación.
NextGen DMS
Arquitectura componible, stack NextGen
Implementación rápida y de bajo costo. Mínima sobrecarga de mantenimiento.
Diseño centrado en el usuario, interfaz intuitiva
No requiere capacitación. La adopción más rápida de la categoría.
No requiere operador de DMS. Por primera vez.
Los avisos e indicaciones de IA reemplazan por completo al operador.
Integración nativa con Tally y Zoho Books
Una única fuente de verdad. Compatible con operaciones multiempresa.
AI Native DMS Capabilities
AI Native DMS with Industry-First Capabilities
Más allá de cada módulo central del DMS, AI DaaS agrega capacidades que no encontrarás en ningún otro lugar, en los cuatro resultados que impulsan tu negocio.
Sales & Distribution
Aumento
AI that predicts demand, prevents stock-outs, and recommends every order so sell-out keeps climbing.
Predicción de Pedidos Secundarios
Recomendación de Pedidos Primarios
Recomendación de Carga
Predicción y Alertas de Quiebre de Stock
Pedido Remoto
3%
Minimum sales uplift, contractually guaranteed
AI Agents for Distributor Management AI agent for every
AI agent for every
DMS workflow
Plug autonomous agents into your DMS to place orders, take orders, bill, and collect. Each one owns a specific part of the distribution workflow, so your team runs the exceptions, not the busywork.
Just Talk to Your DMS
Speak in natural language. Assign Tasks like a Human. Get Instant Action. No Operator/Training Required.
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How the enterprise month runs
Enterprise DMS, from the primary order to the ledger entry
Urban distribution in CPG is enterprise distribution: high volume, high SKU count, multi-company books.
Enterprise distributors running Salescode NextGen DMS reach faster claim settlement within of go-live.
1
Primary order and GRN
- AI primary order: built from predicted secondary, stock level, order cycle and fulfilment time.
- GRN: real-time tracking, verification and approval, with invoice management.
Stock lands verified
2
Inventory and reconciliation
- Stock control: batches, expiry, saleable and non-saleable stock, market returns.
- Reconciliation: automated, with an audit trail and approval on every adjustment.
Books stay straight
3
Secondary order and invoicing
- Order management: AI-predicted order, then confirm, edit or partial confirm.
- Compliance: bulk invoicing with GST, e-invoicing and e-way bills.
Invoiced, GST clean
4
Settlement, ledger and reporting
- Collections: multi-mode payment, credit adjustment, real-time settlement status.
- Books: outlet and distributor ledger, advanced MDM validation reports.
Cash reconciled
Industry First
No Dedicated
No Dedicated
DMS Operator Required
A self-guided AI Native DMS that automates routine distributor operations, reducing dependency on dedicated DMS operators and extensive training.
Accounting Software Integrations
Integrate AI Native DMS with Business Ecosystem
Conecta tus sistemas contables y gestiona múltiples entidades comerciales en una sola plataforma. Los pedidos, las facturas y los libros contables permanecen conciliados en tiempo real, sin doble registro.
AI Native DMS DeploymentLive in 3 Hours.
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Implementación de Aplicación Estándar
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Implementación de Recom. ML
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Implementación de Aplicación Empresarial
Questions About AI Native DMS
AI Native DMS is distributor management software built AI-first at the architecture level — not a legacy DMS with AI features added on top.
"AI Native" describes how the platform is built:A composable micro-services architecture assembled from modules and plug-ins A GenAI/LLM-ready data structure that streams live distribution data into memory for models to act on An agentic AI orchestration layer above it
Intelligence is a core layer of the platform itself. Order prediction, stock-out prediction, billing probability, and credit scoring sit in the Intelligence Core above business logic — not in a bolt-on analytics module.
That architecture is exactly what a bolt-on cannot replicate, and it's why the same platform deploys 5x faster, customizes 3x faster, and lets you own your customizations as plug-ins and modules. The result is a DMS that predicts, recommends, and executes distribution work in real time instead of recording it after the fact.
"AI Native" describes how the platform is built:
Intelligence is a core layer of the platform itself. Order prediction, stock-out prediction, billing probability, and credit scoring sit in the Intelligence Core above business logic — not in a bolt-on analytics module.
That architecture is exactly what a bolt-on cannot replicate, and it's why the same platform deploys 5x faster, customizes 3x faster, and lets you own your customizations as plug-ins and modules. The result is a DMS that predicts, recommends, and executes distribution work in real time instead of recording it after the fact.
A traditional DMS records transactions after operators do the work; an AI Native DMS predicts, recommends, and executes it. The difference is architectural, not feature-level.
Traditional platforms are monolithic — which is why they:
Those four failure points are why category adoption has stayed low for twenty years.
AI Native DMS answers each at the architecture layer: composable micro-services replace the monolith so modules deploy and change independently, a GenAI/LLM-ready data structure puts intelligence in the platform core rather than an add-on, and the AI Native Integration engine connects to existing ERP and accounting systems 5x faster. What buyers experience is a DMS that deploys 5x faster, customizes 3x faster, and runs without an operator.
Yes. The operator exists in a traditional DMS because the software cannot decide anything on its own — so someone has to key in orders, chase invoices, and reconcile stock.
In an AI Native DMS, the Intelligence Core is a platform layer. Order prediction, stock-out prediction, billing probability, and credit scoring run continuously, and the agentic orchestration layer turns those outputs into completed work: orders raised, invoices generated, collections reconciled, reports produced.
This is a structural consequence of the architecture, not a simpler screen. And it's the cost line that matters most: operator salary is what has made DMS unviable for small distributors, so removing it changes the economics of digitizing the long tail of your network — not just the convenience of running it.
In an AI Native DMS, the Intelligence Core is a platform layer. Order prediction, stock-out prediction, billing probability, and credit scoring run continuously, and the agentic orchestration layer turns those outputs into completed work: orders raised, invoices generated, collections reconciled, reports produced.
This is a structural consequence of the architecture, not a simpler screen. And it's the cost line that matters most: operator salary is what has made DMS unviable for small distributors, so removing it changes the economics of digitizing the long tail of your network — not just the convenience of running it.
Yes. AI Native DMS integrates with Tally, Zoho Books, SAP, Oracle, Microsoft Dynamics, and custom enterprise applications.
Integration is a dedicated layer of the architecture, not a connector bolted to the edge. MDM bulk ingestion, a GenAI AutoMapper that maps incoming master data automatically, a webhook engine, and ERP/Tally connect all sit in the integration layer — which is why the AI Native Integration engine stands integrations up 5x faster than a traditional DMS implementation.
That matters commercially because the two biggest reasons distributors resist a DMS are being asked to keep books twice and being asked to run one system per brand. Here they keep their existing accounting workflow, and multi-company support lets a single distributor run several brands on the same platform.
Integration is a dedicated layer of the architecture, not a connector bolted to the edge. MDM bulk ingestion, a GenAI AutoMapper that maps incoming master data automatically, a webhook engine, and ERP/Tally connect all sit in the integration layer — which is why the AI Native Integration engine stands integrations up 5x faster than a traditional DMS implementation.
That matters commercially because the two biggest reasons distributors resist a DMS are being asked to keep books twice and being asked to run one system per brand. Here they keep their existing accounting workflow, and multi-company support lets a single distributor run several brands on the same platform.
Distributor resistance has three consistent causes:Running a separate system per brand is impractical Distributors won't abandon the accounting books their tax filings depend on A traditional DMS adds operator workload while returning nothing to the distributor
Each is answered in the architecture, not in change management. No operator is needed because intelligence and orchestration are platform layers. The integration layer keeps existing accounting systems in place with multi-company support. And the Intelligence Core runs credit scorecards and billing probability that work on the distributor's own cash position, not just the brand's reporting.
Add faster claim settlement and one-click access to channel financing through natively connected fintech partners, and the distributor gets a system that improves their working capital. Adoption follows because the platform was built to serve both sides — not because the rollout was managed harder.
Each is answered in the architecture, not in change management. No operator is needed because intelligence and orchestration are platform layers. The integration layer keeps existing accounting systems in place with multi-company support. And the Intelligence Core runs credit scorecards and billing probability that work on the distributor's own cash position, not just the brand's reporting.
Add faster claim settlement and one-click access to channel financing through natively connected fintech partners, and the distributor gets a system that improves their working capital. Adoption follows because the platform was built to serve both sides — not because the rollout was managed harder.
Agents are one layer of an AI Native platform, not the whole of it. Beneath them, the Intelligence Core runs the named models: order prediction, stock-out prediction, billing probability, credit scorecards, AI recommendations, and the AI target engine.
The agentic orchestration layer turns those model outputs into completed work through specialized agents for order management, billing, collections, inventory, distributor support, and reporting.
Because every agent and model reads from one GenAI-ready data spine, a prediction in one module becomes an action in another: a stock-out alert shapes the next primary order recommendation, and a credit score changes what an outlet can be billed. That chaining is what an AI feature list bolted to a monolith cannot reproduce.
The agentic orchestration layer turns those model outputs into completed work through specialized agents for order management, billing, collections, inventory, distributor support, and reporting.
Because every agent and model reads from one GenAI-ready data spine, a prediction in one module becomes an action in another: a stock-out alert shapes the next primary order recommendation, and a credit score changes what an outlet can be billed. That chaining is what an AI feature list bolted to a monolith cannot reproduce.
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