We define the target users, conversation goals, channels, business systems, approved data sources, security requirements, and success metrics.
We build a working GenAI chatbot using Amazon Bedrock and Amazon Lex, then test retrieval quality, response accuracy, guardrails, fallback behavior, escalation paths, latency, and projected operating costs.
We connect the chatbot to CRMs, ticketing systems, knowledge bases, internal APIs, and approved data sources, then deploy it across web, mobile, messaging, or voice channels with production security and monitoring in place.
We monitor task completion, escalation rates, response quality, latency, cost per conversation, and customer satisfaction, then improve prompts, knowledge sources, workflows, and integrations as usage grows.
Answer order-status, account, product, policy, and troubleshooting questions, and transfer sensitive or complex issues to the right team.
Collect required information and retrieve data from CRMs, ERPs, and internal systems, then route each request to the right team or workflow.
Use approved customer, product, account, or learning data to deliver relevant responses and recommendations across supported languages and channels.
Build chatbots for healthcare, financial services, insurance, and legal workflows where access controls, audit logs, encryption, data retention, and human review are required.
Pair your chatbot with adjacent conversational and advisory services to cover more of the customer journey:
Finance
Head of Support, ShopSmart
96 percent average intent match accuracy
50 percent lower support costs after go-live
Attorney Live’s small team fielded a growing stream of English and Spanish legal questions, which led to delayed responses, missed leads and no insight into unanswered queries.
Avahi built a three-week proof-of-concept that couples Amazon Bedrock with a vector database in an event-driven Lambda pipeline, instantly answering multilingual questions and flagging gaps for attorneys to review.
Secure, private subnet design with WAF and audit logs meets strict confidentiality needs
Prototype answered all test questions in two languages and identified unresolved queries for tuning
Production-ready architecture can scale from pilot to full client base without re-engineering
An AI chatbot uses large language models and business data to answer questions, complete approved tasks, and transfer conversations to people when required. We build GenAI chatbots on AWS using services such as Amazon Bedrock and Amazon Lex, with access, data, and governance controls configured for your use case.
The chatbot interprets the user's message, retrieves relevant information from approved sources, and generates a response. It can also call authorized systems to complete tasks. Retrieval, guardrails, testing, confidence thresholds, fallback responses, and human escalation help reduce inaccurate or inappropriate answers.
The chatbot can support web, mobile apps, SMS, voice, WhatsApp, Facebook Messenger, and custom channels through APIs and supported integrations. The exact channel setup depends on your AWS architecture and third-party platform requirements.
Yes. We can integrate the chatbot with Amazon Connect, Zendesk, and other customer-service platforms. The transfer can include conversation history, information already collected, and the reason the chatbot escalated the request.
We design the chatbot around your AWS security requirements. Controls may include encryption in transit and at rest, role-based access, private networking, audit logging, data-retention rules, and restrictions on which systems or documents the chatbot can access.
The cost depends on the chatbot's channels, data sources, integrations, security requirements, expected usage, and the actions it needs to perform. A chatbot that answers questions from one knowledge base requires a different scope from one that connects to customer records, processes transactions, and supports voice. Eligible companies may receive a funded PoC depending on the project.
A focused proof of concept can usually be delivered faster than a full production deployment. The final timeline depends on the number of channels, integrations, data sources, security requirements, and testing needed. We define the scope and delivery milestones during the discovery workshop.
Yes. We can assess an existing chatbot that is inaccurate, slow, expensive, difficult to maintain, or unable to support new integrations, then improve the current AWS architecture or redesign the solution using Amazon Bedrock, Amazon Lex, and other AWS services.
The chatbot is deployed within your AWS environment, giving your organization control over the configured AWS resources, connected data sources, access policies, and ongoing cloud usage. Ownership terms for custom code, third-party services, and ongoing support should be documented in the project agreement.
We can monitor performance, improve prompts and knowledge sources, add new workflows, manage integrations, optimize AWS costs, and expand the chatbot into new channels or languages. Ongoing support can also be provided through our managed AWS services.
