Conversational agents that resolve, not just respond
We build chatbots and voice assistants with persistent memory, business context and personality. Deployed on WhatsApp, Slack, your website or any channel where your customers live.
Most chatbots fail the same way: they forget what was said two messages ago, they answer with the same five options regardless of context, and they escalate to a human the moment the question gets slightly specific. We build conversational agents with genuine memory — session history, user profile, past interactions — so the agent can pick up a conversation where it was left three days ago.
Channel integration is not a detail — it is a design constraint. WhatsApp, Slack, web or voice each have different interaction patterns, message length limits and user expectations. We design the conversation architecture for the channel, not despite it.
What is a production conversational agent?
A production conversational agent is not a decision tree wrapped in a chat widget. It is an AI system with: a language model for natural language understanding and generation, persistent memory to maintain context across sessions, a tool layer to query your systems and APIs in real time, a personality and tone layer calibrated to your brand, and a graceful escalation path that hands off to a human agent with full conversation history when the AI reaches its limits. In Adoredev we build agents for three interaction modes: customer-facing support, internal knowledge assistants, and automated outreach workflows.
Our capabilities
Conversational AI across every channel where your customers are
WhatsApp Business
Meta API + TwilioAgents deployed on WhatsApp with media handling, template messages, contact management and GDPR-compliant opt-out flows.
Learn moreSlack Bots
Slack PlatformInternal assistants with slash commands, modal interactions, App Home and integration with your internal tools and databases.
Learn moreWeb Widget
EmbeddableFully custom embeddable chat widget with persistent sessions, conversation history and seamless handoff to live agents.
Learn moreVoice Assistants
Twilio VoiceVoice-enabled agents with speech-to-text, intent classification and dynamic response generation for phone support automation.
Learn morePersistent Memory
Context ArchitectureMulti-layer memory: session context, user profile, interaction history and semantic search over past conversations.
Learn moreHuman Escalation
Handoff ProtocolIntelligent escalation with full conversation context, sentiment detection and configurable escalation triggers per use case.
Learn moreResponses vs. Resolutions
A chatbot that responds is not the same as a chatbot that resolves. The difference is context: knowing who the user is, what they asked before, and what they actually need — not just what they typed in the last message. We measure resolution rate, not response rate. Our agents are designed to reduce the escalation-to-human ratio. If your chatbot is escalating more than 30% of conversations to humans, the architecture is wrong.
Good fit
- Customer support with 50+ repetitive inquiries per day
- Internal teams spending hours answering the same process questions
- Businesses with established channels (WhatsApp, Slack) and active user bases
- Companies with internal knowledge bases that are poorly accessible
Not a good fit
- Products with no existing user interaction patterns to analyze
- Use cases requiring real-time data the business cannot provide via API
- Projects expecting the agent to replace all human contact overnight
- Organizations unwilling to invest in an initial conversation design phase
Our conversational agent methodology
From conversation design to production in 6 weeks
Conversation Design
We map your most frequent user journeys, define persona, tone and escalation rules — before writing any integration code.
Channel Integration
API setup for your target channel (WhatsApp, Slack, web, voice) with test environments and webhook reliability configurations.
Memory & Context Build
Persistent session storage, user profile management and semantic retrieval from your knowledge base connected to the agent.
Production & Monitoring
Go-live with resolution rate tracking, escalation dashboards and continuous prompt optimization based on real conversation data.
Frequently asked questions
Common questions about building conversational agents in production
WhatsApp Business API (via Meta direct or Twilio), Slack (Bolt SDK), web (custom widget or integration with Intercom/Zendesk), and voice (Twilio Voice + speech-to-text). We can also build for custom channels with webhook-based APIs. Our architecture is channel-agnostic by design — the agent logic is separate from the channel adapter.
We implement end-to-end data minimization: we store only the conversation data required for the agent to function, with configurable retention windows. WhatsApp integrations include mandatory opt-out handling. All persistence layers use encryption at rest. If you operate under GDPR or similar regulation, we document data flows and implement the required controls from the start — not as an afterthought.
We build a knowledge layer specific to your business: a combination of structured data from your databases (via API), unstructured documentation (via RAG on Bedrock Knowledge Bases), and a curated set of conversation examples that define expected behavior. The agent does not improvise — it retrieves and applies your actual business knowledge.
Yes, and this is a critical design component. We implement configurable escalation triggers — sentiment threshold, explicit user request, topic out of scope — and pass the full conversation history to the human agent in whatever format your support tool expects (Zendesk ticket, Slack message, email). The handoff is invisible to the user.
The agent is designed to fail gracefully: it acknowledges the limitation, offers to connect the user with a human, and logs the unresolved query for content improvement. We never deploy an agent that fabricates answers to cover gaps — we prefer explicit fallback over a confident hallucination.
Free conversation design session.
We map your most frequent user journeys and tell you what an agent can and cannot resolve.
We evaluate your current interaction patterns and design a conversation architecture with realistic resolution rate projections.
Talk to an architect