The Challenge

Translating an inspirational GenAI session into a production agent serving a multi-tenant marketplace introduced several significant challenges:

• Domain-Specific Knowledge Volume: HyperGuest’s knowledge base spans distribution-system documentation, mapping codes, supplier specifications, hotelier onboarding playbooks, and operational runbooks. Any agent answering partner questions had to reason across a long-context corpus and remain anchored to that corpus  free-form chat completions would not be acceptable for a partner-facing surface.
• Multi-Tenant Commercial-Data Isolation: Hoteliers and travel sellers must never see each other’s commercial data. Per-tenant identity, session scoping, and downstream tool authorization had to be enforced from the chat surface all the way through to the underlying booking-system APIs  with zero tolerance for cross-tenant leakage through prompt context, logs, or retrieved documents.
• Mission-Critical Connectivity APIs: The same upstream booking and supplier APIs that power live revenue flows would now also be invoked by an autonomous chat agent. Any agent-driven query spike risked impacting partner bookings, so rate-shaping and isolation between conversational and transactional traffic had to be designed in from day one.
• Hallucination Risk on Commercial Decisions: An incorrect or fabricated answer about availability rules, cancellation policies, or distribution mappings could directly influence partner commercial decisions. Grounding fidelity, topic-bounding, and PII protection had to be production-grade rather than aspirational.
• Predictable Cost Economics: HyperGuest needed conversation-level cost economics that could be baselined against the prior support-ticket cost  with confidence that traffic spikes would not produce surprise infrastructure bills or require capacity planning.
• Lean Engineering Team: HyperGuest’s engineering team is deliberately lean. Any new architecture had to minimize the operational surface area  cluster sizing, model-serving stacks, and bespoke runtime plumbing were all explicitly off the table.

The Solution

Directeam engaged with HyperGuest’s product and engineering leadership through structured discovery to map partner query taxonomy, latency budgets, and answer-quality thresholds before selecting models, frameworks, and infrastructure.

• Comprehensive Bedrock Model and Framework Assessment: Directeam evaluated the Anthropic Claude family on Amazon Bedrock against the chat workload’s tool-use reliability, long-context handling, and latency profile. Anthropic Claude Sonnet was selected as the reasoning engine, paired with Amazon Titan Text Embeddings for the RAG index over connectivity manuals and supplier specifications.
• Agent Implementation on Bedrock-Native Primitives: The agent was implemented using Amazon Bedrock Agents with Action Groups for booking-system lookups and a Bedrock Knowledge Base over the documentation corpus, enabling fluid movement between conversational answers and structured tool calls without bespoke orchestration code.
• Per-Tenant Identity Boundary: Each chat session is bound to an authenticated tenant principal that propagates as a session attribute into the Bedrock Agent invocation. The session attribute scopes downstream Action Group calls through IAM session policies and tenant-aware row filters in the Knowledge Base.
• Tool-Server Interoperability and API Protection: Tool-server interactions follow MCP-style contracts so HyperGuest’s existing internal connectivity APIs remain the system of record. Token-bucket rate limits per tenant protect upstream supplier APIs from agent-driven query spikes.
• Responsible-AI Controls Tuned to the Hospitality Domain: Amazon Bedrock Guardrails were configured with hospitality-domain content filtering, topic-denial policies for off-topic prompts (legal advice, competitor commentary, pricing speculation), grounding checks anchoring every factual answer to a retrieved Knowledge Base passage, and PII redaction.
• Continuous Evaluation Loop: Directeam instrumented a continuous evaluation loop using a curated set of HyperGuest’s real partner queries, scoring grounding fidelity and tool-use correctness in CloudWatch dashboards. Low-confidence answers feed a human-in-the-loop reviewer queue.
• Serverless Compute Footprint: Bedrock Agents host the agent runtime, with Action Groups on AWS Lambda — yielding serverless scale-to-zero economics. The RAG index runs on Amazon OpenSearch Serverless; document ingestion runs on a scheduled ECS Fargate task. Amazon CloudFront sits in front of the chat surface with a PPA-modeled cost structure built specifically for HyperGuest.

Quantitative Outcomes

The engagement is measured against the following quantitative outcomes:

• Support-Ticket Deflection Rate: ≈55–70% of routine partner inquiries deflected from the human ticket queue to the autonomous chatbot  measured via CloudWatch conversation counts vs. legacy ticket volume.
• Per-Conversation Cost vs. Prior Ticket Cost: Per-conversation infrastructure cost runs at an order-of-magnitude lower than the blended cost of a human-handled support ticket on the legacy flow.
• Grounding Fidelity: ≥95% of factual answers anchored to a retrieved Knowledge Base passage (target: >90%)  measured continuously against a curated partner-query evaluation set in CloudWatch.
• Tool-Use Correctness: ≥98% Action Group call success rate per tenant on the bookable surface (mapping resolution, content lookup), with rate-limit-induced rejections explicitly excluded.
• Tenant Isolation Incidents: Zero cross-tenant data exposure events recorded since launch  enforced by per-tenant Cognito identity, IAM session policies, and Knowledge Base row filtering.

The Results

The engagement between Directeam and HyperGuest moved a workshop concept into a production-grade, partner-facing agent that materially changes how the platform delivers operational support. Key outcomes include:

• Replaced Ticket-Heavy Support Flows with an Autonomous Agent: Routine partner inquiries are now handled by a grounded chat agent operating over HyperGuest’s connectivity knowledge base, freeing the support team for higher-complexity escalations.
• Predictable Per-Conversation Cost Economics: Serverless infrastructure and model selection produced a per-conversation cost profile that compares favorably with the prior support-ticket cost, with no manual capacity planning required as traffic scales.
• Tenant Boundary Preserved End-to-End: Per-tenant identity propagates from the chat surface through the Bedrock Agent invocation into Action Group calls and Knowledge Base retrieval, closing cross-tenant data exposure paths.
• Continuous Quality Loop Improving Grounding Fidelity: Production telemetry, guardrail interventions, and reviewer-queue feedback feed an iterative tuning process — producing measurable improvements in grounding fidelity and tool-use correctness over time.
• Removed Self-Managed Inference Infrastructure: Bedrock Agents, OpenSearch Serverless, Lambda, and Fargate together remove every self-managed inference and indexing component from HyperGuest’s footprint.
• Directeam’s Ongoing Support: Directeam continues to serve as HyperGuest’s technical partner across the AWS estate, including the dedicated CloudFront PPA calculator built for HyperGuest’s specific traffic shape.