AI Agent Build & Automation
AI Agents That Resolve Work, Not Just Answer Questions
Customer-facing and agent-assist AI built on Amazon Bedrock, Amazon Lex, and your own knowledge and systems — with retrieval, guardrails, escalation, and the monitoring to know whether it is actually working.
- Self-service AI
- Agent assist
- RAG
- Guardrails
From intent recognition to resolved outcome
An AI agent that answers well but cannot complete the task simply moves the work. We build for resolution.
Customer Self-Service Agents
Voice and chat agents that authenticate, understand intent, retrieve the right information, and complete transactions in your systems.
Agent Assist
Real-time suggested responses, knowledge surfacing, next-best-action, and automatic summarization for the humans handling complex work.
Knowledge & Retrieval
Retrieval-augmented generation over your documentation, policies, and product data — so answers are grounded in your content rather than the model’s recollection.
Workflow Automation
Connecting the agent to the systems where work is actually completed: CRM, order management, billing, and ticketing.
Escalation & Handoff
Explicit escalation criteria and clean handoff to a human with full context — including the cases where the agent should not attempt to help at all.
Guardrails & Quality Monitoring
Content and topic guardrails, evaluation sets, and ongoing quality monitoring so regressions are caught before customers find them.
AI agent capabilities
- Customer-service AI agents
- Self-service voice and chat agents
- Agent assist and suggested responses
- Knowledge retrieval and RAG pipelines
- Amazon Bedrock implementation
- Amazon Lex conversational design
- Generative AI application development
- Intent recognition and disambiguation
- Workflow and transaction automation
- Escalation design and human handoff
- AI quality monitoring and evaluation
- Conversation analytics
- Content and safety guardrails
- Prompt and retrieval optimization
How we build AI agents
- 1
Scope
We identify which intents are worth automating based on volume, complexity, and the systems required to actually resolve them.
- 2
Ground
Knowledge sources are prepared, retrieval is built and tested, and we establish what the agent must never do.
- 3
Build & Evaluate
Iterative build against an evaluation set, with measured accuracy and containment rather than anecdotal testing.
- 4
Launch & Monitor
Controlled rollout with monitoring, quality review, and a tuning loop that continues after launch.
Frequently asked questions
How do you stop an AI agent from giving wrong answers?
Grounding, scope, and evaluation. Answers are retrieved from your approved content rather than generated from model memory; the agent has an explicit scope and refuses outside it; and we maintain an evaluation set that runs against changes so quality regressions surface before customers do. No approach eliminates error entirely, which is why escalation design matters as much as accuracy.
Should we start with self-service or agent assist?
Agent assist is often the lower-risk starting point: a human stays in the loop, and you learn where the model is reliable before exposing it to customers. That said, if you have high-volume, well-bounded intents with clean system integration, self-service can deliver value faster. We look at your contact mix before recommending either.
Which models do you use?
We most often build on Amazon Bedrock, which provides access to a range of foundation models, and Amazon Lex where deterministic intent handling fits better. Model choice is a design decision driven by latency, cost, accuracy, and data-residency requirements — not a fixed preference.
Ready to Transform Your Customer Experience?
Let’s build smarter, more intelligent experiences—together.