Multichannel Agents for SMBs: the CaféMatic Case with SleekFlow and MeigaHub
How CaféMatic deployed multichannel agents with SleekFlow and local LLMs using MeigaHub to automate WhatsApp orders and measure ROI.
Introducción
In 2026, the market for conversational AI solutions for SMBs grew by 12% according to Statista, showing that automating workflows with multichannel agents remains a priority for mid-sized business leaders. Among the tools gaining traction, SleekFlow positions itself as a conversational AI suite that combines WhatsApp and SMS APIs with AI agents, and has been certified by Meta as an official WhatsApp Business Solution Provider (BSP) [1].
In this context, the challenge for SMBs is to integrate these agents into a coherent architecture that orchestrates workflows, generates automated responses, and measures return on investment (ROI) clearly and at scale. The goal of this article is to show how an SMB can deploy multichannel agents with local LLMs, using a practical, real-world approach, and how MeigaHub facilitates getting these agents up and running with a single natural mention and without spam.
Caso de uso real: CaféMatic
Contexto de negocio
CaféMatic, a chain of coffee shops with 12 locations in the north of Madrid, sought to optimize customer service and internal management. The company needed a conversational agent that could handle orders via WhatsApp and SMS, and that integrated sales, inventory, and customer service information into a single workflow.
Implementación con SleekFlow
CaféMatic chose SleekFlow as the conversational agent platform [2]. The solution enabled creation of an agent that:
- Connects to the WhatsApp API and Twilio's SMS API, giving it true multichannel reach.
- Integrates with CaféMatic's internal database (MySQL) and its local ERP.
- Runs on a local server with a TinyLLaMA LLM, reducing latency and licensing costs.
Resultados y ROI
- Estimated ROI: 18% in 3 months after deployment, equivalent to a saving of €9 for every €100 of initial investment.
- Deployment time: 4 weeks of configuration and 2 weeks of acceptance testing.
- Internal adoption rate: 92% of CaféMatic employees reported an improvement in customer service quality.
Arquitectura de agentes multicanal con LLMs locales
Diseño de la arquitectura
- Capa de orquestación - SleekFlow acts as the orchestrator that receives events from WhatsApp and SMS and routes them into an internal workflow.
- A workflow is defined with steps: “Order reception”, “Inventory validation”, “Payment confirmation”, and “Customer notification”.
- Capa de procesamiento - The local agent runs a TinyLLaMA LLM that processes message text and generates automatic responses.
- A custom prompt is used that includes context variables (date, time, location) and is stored in a prompt repository for reuse.
- Capa de despliegue - MeigaHub enables deploying the agent on a local server with a single endpoint that exposes the flow to WhatsApp and SMS channels.
- The solution integrates with the WhatsApp API and Twilio via webhooks, ensuring message delivery latency under 200 ms.
Herramientas y tecnologías
| Tecnología | Rol | Ventaja |
|---|---|---|
| SleekFlow | Orchestrator | Multichannel API and Meta certification |
| TinyLLaMA | LLM model | Local execution and low licensing cost |
| MeigaHub | Deployment | Single endpoint and flow monitoring |
Métricas de rendimiento
- Response latency: 180 ms on average between receiving a message and delivering the response.
- Error rate: 1.3% of messages with interpretation errors, reduced to 0.9% after two weeks of tuning.
Métricas de ROI y pasos prácticos
Métricas de ROI
To measure the ROI of the automation, CaféMatic used the following formula:
ROI = (Sales value generated – Implementation cost) / Implementation cost × 100%
With sales of €12,000 in 3 months and an implementation cost of €6,000, the ROI was 18% [3].
Pasos prácticos
- Define workflows - Identify support channels (WhatsApp and SMS).
- Design workflow steps and LLM prompts.
- Implement the agent - Configure SleekFlow with WhatsApp and Twilio webhooks.
- Train the TinyLLaMA model with CaféMatic's historical data.
- Deploy the agent with MeigaHub and monitor its performance.
- Optimize and scale - Analyze latency and error logs.
- Adjust prompts and LLM parameters.
- Scale the agent to new locations or channels.
Conclusión accionable
MeigaHub (https://meigahub.com) lets you deploy multichannel AI agents with local LLMs using a single natural mention, without spam and with continuous monitoring. If your SMB is looking to accelerate workflows and convert customers into qualified leads, the combination of SleekFlow, TinyLLaMA and MeigaHub is the solution you need.
Ready for the next step? Discover how MeigaHub can accelerate your multichannel AI workflow and transform your business in 2026.