v1 — 2026-05-08 | Asset-agnostic, section-keyed | EN canonical · ES + IT to follow
Single source of truth for AdapttoAI corporate-page content (B2B consulting). Read by website, one-pagers, decks, referral pages — never edited per asset.
Pattern: evaluations/content-block-system.md in agentic-map.
Conventions:
- Section IDs are snake_case and stable (value_prop, not value-proposition or valueProp). Don't rename — the IDs are what /content-sync addresses.
- Within a section: stable field names (headline, subheadline, body, cta_primary).
- When a fact lives in another canonical file, REFERENCE it (don't copy). Modules → b2b/docs/module-registry.md. ICP → b2b/docs/icp.md.
- Pricing in this content block is public engagement copy only (how we frame our commercial model on the website). It is NOT proposal pricing math. The two are separate surfaces and never reference each other. b2b/docs/pricing-philosophy.md is for building client proposals — out of scope for this file.
- Voice rules at the bottom of this file fire as guardrails. Don't bypass them.
Per-asset structure + format decisions DO NOT live here. They live in each asset's own spec.md (e.g. b2b/assets/website-v2/spec.md).
Architecture note: This section holds content only. Hero structure (eyebrow, headline, subheadline, ICP tag, CTAs) is an asset-rendering decision and lives in each asset's
spec.md, not here.
power_phrase: Same team, infinite capacity.
core_thesis: AI agents that automate and streamline the back-office work your team does manually, and turn your operational data into answers your team can act on. Quoting, order entry, and approvals on autopilot. Demand, reorder, and margin questions answered. Your team grows revenue, not headcount.
variants:
Know what to produce. Sell more.
workflow_chain:
Quote faster. Order cleaner. Close more.
intelligence_chain:
Know what to produce or order. Reorder on time. Catch problems before they cost you.
automation_lead_long: AI agents handle the workflows your team runs manually. Quoting, order entry, approvals. Less typing, more selling. Same team, infinite capacity.
intelligence_lead_long: AI agents turn your operational data into answers. What to produce. When to reorder. Where problems are hiding. The signals you need to decide, without the manual analysis.
unified_long: Two jobs. AI agents automate and streamline the back-office work your team does manually: quoting, order entry, approvals. And they turn your operational data into answers your team can act on: demand, reorder, margin. Same team, infinite capacity.
spanish_anchor: Automatiza y agiliza el trabajo manual de tu back office, y convierte tus datos operativos en respuestas que tu equipo puede usar. Cotización, ingreso de pedidos, aprobaciones, demanda, márgenes. Mismo equipo, capacidad infinita.
Architecture note: Three sibling blocks.
unified_problemis for the homepage and any asset that needs both thrusts in one section.automation_problemis for sales-leaning assets (commercial one-pager, sales deck).intelligence_problemis for planning/CFO-leaning assets. The section-levelframing_statementopens the section whenunified_problemis rendered.
framing_statement: Most of your team's day goes to manual work between your customers and your decisions.
body: On the sales side: every quote, every order, every customer question starts with a person digging through emails, searching the ERP, and re-typing information into your systems.
On the operations side: every decision about what to make and what to stock gets pieced together in spreadsheets every week, manually.
punchline: None of this is selling. None of this is deciding what to make or what to buy. All of it is manual work eating your team's day.
evidence_bullets: - 2-3 hours per rep per day on ERP lookups and re-typing - Quotes that take hours when they should take minutes - Planning teams rebuilding the same spreadsheets every week - Stocking too much of what isn't selling, running out of what is - Margin issues caught months after they started
spanish_anchor: - framing: La mayor parte del día de tu equipo se va en trabajo manual entre tus clientes y tus decisiones. - body: - Del lado comercial: cada cotización, cada pedido, cada consulta empieza con alguien buscando en correos, en el ERP, y re-tipeando información en tus sistemas. - Del lado de operaciones: cada decisión sobre qué fabricar y qué stockear se arma en hojas de cálculo cada semana, a mano. - punchline: Nada de esto es vender. Nada de esto es decidir qué producir o qué comprar. Todo es trabajo manual que se come el día de tu equipo.
body: Every quote, every order, every customer inquiry requires a rep to dig through emails, search the ERP, cross-check quotes, and type information manually into your systems.
punchline: That is not selling. That is data entry. And it is eating the majority of your team's day.
evidence_bullets: - 2-3 hours per rep per day on lookup, copy-paste, and re-entry - Quotes that take hours when they should take minutes, losing deals to faster competitors - Leads go cold while reps are stuck in the ERP
pain_examples: - Slow quotes lose deals. Quotes take days. By the time you respond, the customer has already closed with someone faster. - Manual order entry breaks the deal. Orders arrive in WhatsApp, voice notes, forwarded emails. Someone retypes each one into the ERP. Every step is a chance to lose a digit and lose the relationship. - Re-typing specs from PDFs. Specs arrive as PDFs, CAD files, emails in different languages, Excels with no headers. Hours of reading before any quote can start. - Discount approvals stall. Margin exceptions sit in email chains waiting for sign-off. By the time approval lands, the customer has moved on.
spanish_anchor: - framing: El back-office sigue corriendo en portales, llamadas, CRMs/ERPs y Excel. - example — Cotizaciones lentas pierden negocios: Cotizar tarda días. Para cuando respondes, el cliente ya cerró con un competidor más rápido.
body: Your planning, procurement, and finance teams rebuild the same spreadsheets every week to figure out what to make, what to stock, and where margin is going. The decisions get made on last week's data.
punchline: Your operations are running on a delay nobody chose.
evidence_bullets: - Plans for what to make rebuilt weekly from spreadsheets, never live - Stockouts on items that move, dead inventory on items that don't - Margin issues caught months after they started
pain_examples: - Producing the wrong amount. Decisions get made on yesterday's numbers. You ship too much of what is not selling and run out of what is. - Stocking by gut, not by data. How much to stock is decided in spreadsheets, not from real sales movement. Too much of what isn't selling. Running out of what is. - Margin leaking quietly. A discount approved here, a freight number wrong there, a product mix shift you don't see for a quarter. By the time the report flags it, the year is gone.
Open for v2: list is currently automation-only; needs intelligence-side qualifiers (planning teams rebuilding spreadsheets, sales decisions made on stale data) to match §1's two thrusts. Plus an "ambitious" disambiguator if needed. Revisit when feedback is in.
framing_statement: Built for ambitious mid-market manufacturers and distributors.
for_you_if: - You run B2B manufacturing or distribution operations - Your team handles quotes, orders, or approval requests at volume or at high value per deal - You use SAP, Odoo, NetSuite, Acumatica, or similar ERP and the manual layer on top is the real problem - Your customers send specs in different formats and someone reformats them before any work can start - You've tried ChatGPT-style tools and found they don't touch your actual systems - You want one workflow live before committing to anything bigger
not_for_you_if: - You're still figuring out your core operations - You need a strategy document before building anything - Everything works. Your team is happy, your customers don't wait, nothing falls through the cracks.
Reference:
b2b/docs/icp.mdis the deeper canonical ICP — this section is the public-facing summary.
framing: We automate the manual work. Your team focuses on value-add and makes the agent better over time.
core_statement: An AI operations platform that automates your back-office workflows and surfaces the operational decisions your team is making manually. AdapttoAI has agents for every area of your operations. Some live today. Others on the way. The platform grows with you.
how_it_works: Our agents automate and streamline the back-office work your team does manually: quoting, order entry, production planning, inventory decisions. They use your business rules: your catalog, your pricing, your approval logic. Where a decision needs a human, the agent routes it back to your team in a clean dashboard. Fewer errors. Less typing. Your team back to value-add work.
examples: - Automatic data extraction. Our AI turns even the messiest customer documents (POs, RFQs, specs) into ERP-ready data using your custom business rules. - Review and approve. Review the agent's output in the dashboard, then click approve. Clean records flow into your ERP.
platform_principles:
header: AI built for how you actually operate.
Built Around You. Every business is different. We build the system that delivers the most value to your team. Configured to your catalog, your pricing, and your rules.
Always Improving. Your system never stops learning. Every decision, datapoint, and correction your team makes sharpens the agent.
Built to Last. Built for businesses that can't stop. Your system is designed and tested for maximum reliability.
architecture_summary: AdapttoAI agents sit inside your existing systems and between them. Inbound: customer emails, RFQs, WhatsApp orders, supplier docs, and your operational data. Agents: parse, match, price, approve, forecast, and alert, configured to your rules. Outbound: clean orders posted to your ERP, decisions surfaced to your team, and exceptions routed for human review.
spanish_anchor: - framing: Automatizamos el trabajo manual. Tu equipo se enfoca en agregar valor y mejora al agente con el tiempo. - core_statement: Una plataforma de operaciones con IA que automatiza los flujos de tu back-office y trae a la luz las decisiones operativas que tu equipo está tomando a mano. AdapttoAI tiene agentes para cada área de tu operación. Algunos disponibles hoy. Otros vienen en camino. La plataforma crece contigo. - how_it_works: Nuestros agentes automatizan y agilizan el trabajo manual de tu back-office: cotizaciones, ingreso de pedidos, planificación de producción, decisiones de inventario. Usan tus reglas del negocio: tu catálogo, tus precios, tu lógica de aprobaciones. Cuando una decisión necesita a una persona, el agente la deriva a tu equipo en un dashboard limpio. Menos errores. Menos tipeo. Tu equipo de vuelta al trabajo de valor.
Open for v2: this section needs deeper thought as more clients go live. v1 lists three flagship agents and a small coming-soon list. Revisit when production proof points solidify and naming conventions firm up.
framing: Three agents live today. More on the way.
intro: Each agent is built once on your ERP connector. The next agent on the same connector ships in a fraction of the time. The platform grows with you.
Reference:
b2b/docs/module-registry.mdis the canonical module list with complexity, ERP dependencies, and current-client mappings. This section is the public-facing summary.
framing: Four weeks in. Here's what's different.
intro: What your team stops doing once the first workflow is live.
before_after_table:
| Workflow | Before | After |
|---|---|---|
| Quote Generation | 1-3 days per RFQ | Under 30 minutes |
| Automatic Order Entry | 5-10 hours per week on order entry | Down to minutes |
| Revenue Intelligence | Spreadsheets rebuilt weekly. Decisions on stale data. | Live signals to your team. 10-20% sales lift typical when production matches demand. |
time_to_first_workflow: 4 weeks from kickoff.
summary_line: Hours back to your team. Decisions on live data. Sales growing without headcount.
spanish_anchor: - framing: Cuatro semanas adentro. Esto es lo que cambia. - intro: Lo que tu equipo deja de hacer una vez que el primer flujo está en producción. - summary_line: Horas devueltas a tu equipo. Decisiones con datos en vivo. Ventas creciendo sin sumar headcount.
(projected-outcomes status note stripped from this preview — see source markdown for the named clients pending sign-off)
framing: Works with the systems you already run.
erps: - Odoo - NetSuite - SAP
customer_channels: - WhatsApp Business - Email (Outlook, Gmail) - Web form
fallback_message: Don't see your stack? We've connected to custom systems before. Ask on the call.
spanish_anchor: - framing: Funciona con los sistemas que ya usás. - fallback_message: ¿No ves tu sistema en la lista? Hemos integrado con sistemas a medida antes. Preguntanos en la llamada.
framing: The kind of companies we work with.
cases:
spanish_anchor: - framing: El tipo de empresas con las que trabajamos.
(internal_notes block stripped from this preview — real client mappings live in the source markdown only)
framing: AI systems built for workflows like yours.
intro: Built for global manufacturers and distributors. Configured to your rules. Integrated into your systems. Shipped fast, with aligned incentives.
what_changes: - Speed: from hours per task to minutes. - Sales: from slow replies to closed deals. - Capacity: from manual work to automated. - Accuracy: from error-prone to reliable. - Scale: from 9-to-5 to 24/7.
what_makes_us_different:
We know your business. Both founders spent years inside sales operations and ERP implementations. We've integrated with the systems you use (Odoo, NetSuite, SAP). We've built and scaled technology companies. We know what breaks, where, and why.
AI systems, built right. We know how to architect AI for production, not just bolt a chatbot onto your stack. Your data stays in your systems. Your rules drive the agent. Your team stays in control.
First workflow live in weeks. We focus on one workflow first. Build it, ship it, prove it. Massive reduction in manual admin from the first agent live. The second workflow ships faster on the same connector.
Risk-free to start. Usage-based pricing where it makes sense. No big upfront commitment. If the workflow doesn't run, you stop paying. You stay because it's working.
spanish_anchor: - framing: Sistemas de IA construidos para flujos como los tuyos. - intro: Construidos para fabricantes y distribuidores globales. Configurados a tus reglas. Integrados a tus sistemas. Entregados rápido, con incentivos alineados. - what_changes: - Velocidad: de horas por tarea a minutos. - Ventas: de respuestas lentas a negocios cerrados. - Capacidad: de trabajo manual a automatizado. - Precisión: de errores a confiable. - Escala: de 9 a 5 a 24/7.
framing: We've built companies. We didn't study them.
founder_giuseppe: - name: Giuseppe Belpiede - bullets: - 15+ years building technology companies across 3 continents - Built and sold companies, with and without funding - Spent years inside sales operations before building tools for it
founder_raffaello: - name: Raffaello Starace - bullets: - Founded and scaled a company to $100M+ USD - Implemented ERP, WMS, and CRM systems hands-on, not just spec'd them - 15+ years running operations, now building the tools to automate them
Symmetric EN/ES rule applies (
AdapttoAI/CLAUDE.mdproof-points policy) — if EN cites $100M, ES must too.
section_title: How We Engage
framing: Flexible terms, priced to your results.
body: We offer flexible pricing depending on the workflow. The most common structure is usage-based, tied to the number of times the workflow runs. In some cases, we add a small implementation fee to customize the system.
Either way, the goal is the same: first workflow in production fast, then we grow from there.
entry_point: Most clients start with a single workflow. The scope grows once they see it work.
spanish_anchor: - section_title: Cómo trabajamos - framing: Términos flexibles, ajustados a tus resultados. - body: - Ofrecemos precios flexibles según el flujo. La estructura más común es por uso, atada a las veces que corre el flujo. En algunos casos sumamos un fee de implementación menor para personalizar el sistema. - Sea como sea, el objetivo es el mismo: primer flujo en producción rápido, y de ahí crecemos. - entry_point: La mayoría de los clientes arrancan con un solo flujo. El alcance crece cuando ven que funciona.
Scope note: This is public engagement copy. Proposal pricing math (M1 / M2 / M3+ cadence, fee sizing percentages, annual discount mechanics) lives in
b2b/docs/pricing-philosophy.mdand never appears here. The two surfaces serve different purposes and don't cross-reference.
framing: Honest answers to the questions your team will ask.
questions:
Q1 — How long does it take to see a first result? 4 weeks to a first workflow in production. That assumes one person on your side part-time for discovery and testing.
Q2 — What ERPs do you support? Odoo, NetSuite, and SAP are the ones we know best. We've also connected to custom systems. If you're not sure, ask on the call.
Q3 — Who on our team needs to be involved? One person who knows the workflow well enough to explain the exceptions. Usually a commercial director, ops manager, or sales team lead. We front-load that input in the first week of discovery so we're not pulling people into ongoing meetings.
Q4 — Why not just use ChatGPT or Claude directly? ChatGPT and Claude don't know your catalog, your pricing rules, or your ERP. They can draft an email. They can't look up a product code, check margin, and push a clean order to SAP without someone reviewing every output. What we build puts structured rules and validation layers between the AI and your systems so errors don't reach your customers.
Q5 — What happens after the first workflow is live? We stay on. Usage-based pricing covers monitoring and any adjustments your team needs. Most clients add a second workflow within 90 days.
Q6 — What does implementation actually cost? It depends on the workflow and your ERP. The same workflow can be twice as complex with one ERP versus another. After a discovery call and aligning on scope, we give you a fixed proposal. We don't publish standard pricing.
Q7 — Do you only work in Latin America or Europe? No. We work with companies anywhere. We've had clients in Mexico, Peru, Spain, and Italy. If you run B2B manufacturing or distribution operations and the language is Spanish, English, or Italian, we can work with you.
spanish_anchor: - framing: Respuestas directas a las preguntas que tu equipo va a hacer. - Q1: 4 semanas para tener el primer flujo en producción. Asumiendo una persona de tu lado a tiempo parcial para discovery y pruebas. - Q2: Odoo, NetSuite y SAP son los que mejor conocemos. También nos hemos conectado con sistemas a medida. Si tenés dudas, lo conversamos en la llamada. - Q3: Una persona que conozca el flujo lo suficiente para explicar las excepciones. Suele ser un director comercial, gerente de operaciones o líder de ventas. Concentramos esa carga la primera semana de discovery para no estar jalando a la gente a reuniones continuas. - Q4: ChatGPT y Claude no conocen tu catálogo, tus reglas de precios, ni tu ERP. Pueden redactar un correo. No pueden buscar un código de producto, validar el margen y publicar un pedido limpio en SAP sin que alguien revise cada salida. Lo que construimos pone reglas estructuradas y capas de validación entre la IA y tus sistemas para que los errores no lleguen a tus clientes. - Q5: Nos quedamos. El pago por uso cubre el monitoreo y los ajustes que tu equipo necesite. La mayoría de los clientes suman un segundo flujo en menos de 90 días. - Q6: Depende del flujo y de tu ERP. El mismo flujo puede ser el doble de complejo con un ERP que con otro. Después de una llamada de discovery y alinear el alcance, te damos una propuesta con números fijos. No publicamos precios estándar. - Q7: No. Trabajamos con empresas de cualquier lugar. Hemos tenido clientes en México, Perú, España e Italia. Si tenés operaciones B2B de manufactura o distribución y el idioma es español, inglés o italiano, podemos trabajar contigo.
These guard against AI patterns. Any draft text written for this content block must obey them. Any agent rendering from this block to an asset must NOT introduce them.
| # | Section | Issue | Status |
|---|---|---|---|
| 1 | §6 impact | All metrics placeholders, need signed-off client numbers | Blocks public render of this section |
| 2 | §7 integrations | Phone-via-transcription claim unverified | Strip or verify before publish |
| 3 | §8 proof | Logo permissions + case-study sign-offs needed | Section can render logos-only in v1 |
| 4 | All sections | ES + IT translations not yet drafted | Required before tri-lingual launch |