Conversational AI Development: Shopping Assistants, Voice Agents & Agentic Commerce
We build autonomous agentic workflows and multi-turn conversational systems that run predictably and deterministically. RAG frameworks and Model Context Protocol (MCP) integrations are core to every build, not bolted on later.
Beyond basic prompts: agentic workflows with deterministic execution
Our conversational AI development services go past off-the-shelf chatbots that collapse under real business logic. Pento builds conversational AI architectures with structured reasoning layers, deterministic fallback paths, and RAG-grounded responses that hold up across edge cases and high-volume traffic.
RAG frameworks and Model Context Protocol built into every architecture
Pento wires RAG retrieval directly into the conversation loop and exposes tools through the Model Context Protocol (MCP). Agents get controlled access to your internal systems, with no unpredictable free-form tool calls. You end up with a language-aware architecture that handles complex queries, multi-step workflows, and sensitive business logic, and every decision stays auditable.
How we build and deploy conversational AI systems
Strategic assessment
We start by evaluating your communication workflows, customer interactions, internal support needs, and existing systems.
That work surfaces where automation helps and pinpoints the conversational patterns that matter most.
Conversational AI roadmap and design
Next, we design a roadmap that outlines the most impactful use cases, required NLP components, training data requirements, and architecture for chat, voice, or multimodal systems.
Pilot and real-world validation
Before scaling, we test the conversational system with real interactions.
Pilots evaluate intent accuracy, response quality, containment rates, escalation performance, and overall usability.
Implementation and deployment
After validation, Pento supports full deployment across your preferred channels, integrating with CRMs, ERPs, support tools, knowledge bases, and internal platforms.
AI shopping assistants for conversational commerce
Agentic commerce means your customer doesn't browse. They ask, and the assistant does the work: finding products, comparing options, customizing the cart, and completing checkout inside one conversation.Pento builds AI shopping assistants that handle natural-language product discovery across your full catalog, interactive cart building through chat, and seamless handoff to checkout. The assistant stays grounded in your real inventory, pricing, and business rules through RAG, so it never invents a product or promises a discount that doesn't exist.We originally built this technology for a food delivery platform, where customers order complex, customizable products entirely in conversation. The same architecture adapts to any catalog: fashion, electronics, groceries, marketplaces.
What a shopping assistant engagement includes:
AI voice agents for hands-free commerce and support
Voice is where conversational AI meets operational reality: phone ordering, support lines, in-store assistance, and hands-free workflows for your team.Pento builds AI voice agents with the same deterministic architecture as our text systems: structured reasoning, RAG grounding, and controlled tool access through MCP. We integrate with telephony and speech stacks (Twilio, ElevenLabs, and your existing IVR) so the agent handles real calls, escalates cleanly to humans, and logs every decision for audit.
Typical voice agent use cases:
Conversational AI architecture, NLP, and integration
Pento blends NLP expertise, machine learning engineering, and scalable system design to build conversational AI solutions that earn their keep in production.
Clients choose Pento because we offer:
Frequently Asked Questions
Contact us
Ready to build conversational AI that sells and supports?
If your organization needs shopping assistants, voice agents, or agentic workflows that handle complex business logic reliably, book a scoping call. We'll map your workflow requirements, RAG data sources, and integration constraints before designing the architecture.