We design and deploy AI agents that understand customer intent, use your company's data, and trigger the right processes - sales, RFQs, product advisory, order handling, complaints and field service.
🏭 Looking for a serial casting supplier.
To route this properly - which type of castings are you after?
One real RFQ scenario broken down by layer. What happens between the customer's first message and a qualified lead in the CRM - including every system the agent touches along the way.
The customer emails: "Looking for a serial supplier of cast iron parts for the automotive industry."
Intent: request for quote (RFQ) · 96%. Industry: automotive. Language: EN. Urgency: medium.
Checks customer history and available serial casting standards.
New customer → qualification mode. Volume > 1000 pcs/mo → routed to export sales rep.
Creates a CRM lead with full context, draft reply, and industry tag.
The sales rep receives a notification with the complete picture - no need to chase the customer for more info.
The customer emails: "Looking for a serial supplier of cast iron parts for the automotive industry."
Intent: request for quote (RFQ) · 96%. Industry: automotive. Language: EN. Urgency: medium.
Checks customer history and available serial casting standards.
New customer → qualification mode. Volume > 1000 pcs/mo → routed to export sales rep.
Creates a CRM lead with full context, draft reply, and industry tag.
The sales rep receives a notification with the complete picture - no need to chase the customer for more info.
A chatbot answers questions. An AI agent moves the case forward. It detects customer intent, collects missing data, checks your company systems and triggers the next action - a lead, ticket, RFQ, draft reply, recommendation or a handoff to a human with full context.
Where is my order #48201?
Shipped yesterday via DPD, delivery today by 4pm. Want the tracking link?
Every AMP agent operates in six layers. Without that architecture, an agent is just a chat widget.
The customer asks or acts. The agent detects what they actually need.
The agent asks for what's needed so the case is operationally viable.
Looks up info in ERP, CRM, PIM, shop, documentation, WMS.
Applies rules: who, what, when, for whom, under which conditions.
Creates a lead, ticket, draft, RFQ, recommendation, or hands off to a human.
Tracks intents, outcomes, escalation points and optimizes the process.
Each AMP agent solves a specific operational problem - from the first B2B touch, through product advisory, to field service and product data cleanup.
B2B sales & RFQ handling
More complete inquiries. Less sales-rep time wasted on triage.
Product advisory & selection
Customer describes the need. The agent picks the solution.
Orders, returns, complaints
Fewer repetitive questions. Faster service. Full case context.
Service tickets & maintenance
An AI agent that intakes a service request and hands the technician a complete case.
PIM, docs, company knowledge
Product data ready for sales, service and AI.
We work with companies where answering the customer isn't enough - a process needs to be set in motion.
B2B with RFQs, OEM, technical distribution.
Large stores with products that require advice.
Rich catalogs, PIM, product documentation.
Equipment manufacturers, warranties, complaints, service tickets.
High volume of repetitive customer questions.
PIM, PDFs, datasheets, manuals, technical docs.
We only show deployments that are actually live. Industry scenarios are flagged separately - never presented as case studies.
Odlewnia Żeliwa Simiński-Ordon S.K.A.
AI RFQ Agent for the export campaign. Users pick intent, the agent asks about industry, material, standard, volume and documentation; the sales rep receives a contextual lead.
AI assistant in the product catalog
AI assistant supporting sales and customer service. Experience with a real store, product questions and support along the buyer's journey.
Storefront assistant
Real deployment practice on storefronts and product-catalog work. Tests of customer intent and handling of product questions.
We integrate agents via API, webhooks, product feeds, data exports or dedicated connectors. We don't promise out-of-the-box connectors for everything.
The best deployments start with one process that has the biggest impact on sales, customer service or field service.
A chatbot answers questions. An AI agent detects intent, pulls data from your company systems, applies business rules and triggers a concrete action - a lead, ticket, RFQ, draft reply, recommendation or handoff to a human.
Yes. That's the foundation of our architecture. We connect the agent to company systems via API, webhooks or dedicated connectors.
Not necessarily. We usually start in draft / human-in-the-loop mode, where the agent prepares the reply and a human approves it.
Depends on scope and integrations. An MVP agent ships in a few weeks; a full deployment with integrations in 2–4 months.
Yes. Language models support multilingual operation - important for B2B exporters in particular.
Yes. The agent works across input channels - website chat, forms, email, customer service panel.
Yes. That's one of the core goals of an operational agent. It creates objects in your CRM, helpdesk or custom system.
We track intents, escalation points, data completeness, conversion to lead/ticket and case-handling time. Not just chat volume.
30 minutes. We pick the area where an AI agent will deliver value fastest - and we leave with a concrete recommendation.
