Automating Outreach at Scale with AWS-Native GenAI

In high-volume industries like real estate, every call, response, and hesitation holds business value if you can capture it. For one enterprise client, the challenge wasn’t a lack of data. The manual effort required to summarize, analyze, and learn from it was daunting.

A real estate broking leader in the North American region, our client is redefining real estate lead qualification solutions, helping businesses connect with high-intent buyers efficiently. The company’s outreach-heavy business depended on fast decision-making, accurate campaign reporting, and effective agent performance tracking. However, time-consuming call reviews, subjective feedback loops, and dashboards that lagged behind the real world hindered their workflow.

Coditas orchestrated a secure, AWS-native GenAI platform to automate the entire outreach lifecycle, turning voice data into real-time, searchable insight.

The platform ingests outbound call recordings and processes them through an AI pipeline built entirely on AWS. Call transcripts are summarized, classified, tagged, and made searchable, delivering fast, actionable insights to agents, supervisors, and managers.

The AWS stack behind the scenes:

• Amazon S3 for ingesting and storing audio data

• Amazon Transcribe for accurate, timestamped speech-to-text

• Amazon SageMaker for classification, tagging, and Nova Micro LLM inference

• Amazon Bedrock (Claude v2.1) for summarization and retrieval-augmented generation (RAG)

• Amazon Aurora and OpenSearch to store summaries, embeddings, and support semantic search

• CloudWatch for monitoring, WAF and VPC for secure routing, and ECS clusters for container orchestration

• React frontend deployed globally via CloudFront for low-latency user access

Before the platform, campaign insights were trapped in raw recordings and manual notes. Agents waited hours, sometimes days, for feedback, while supervisors combed through calls one by one. After launch, teams gained an AI-powered control center that delivered real-time visibility, summaries, and trend alerts. The result? Higher conversions. Faster campaigns. Zero reporting backlog. No infrastructure drag.

• Reporting time reduced by 80%
  → Cut campaign analysis from 4 hours to under 40 minutes per cycle

• Decision-making latency dropped from days to seconds
  → Teams adjusted campaigns mid-flight instead of post-mortem

• 15–20% uplift in appointment conversions
  → Agents responded faster with real-time insights and objection summaries

• Agent ramp-up time reduced by ~30%
  → Coaching became consistent, data-backed, and tied to performance trends

• 10,000+ calls processed weekly with zero added headcount
  → Operations scaled without scaling headcount

• <3s feedback loop after each call
  → Agents received summaries while still in the follow-up context

Our team approached this as an engineering problem first rather than a mere GenAI overlay. We designed, deployed, and optimized every component using AWS-native tools that balanced performance with operational cost.

• Dual-model setup (Claude + Nova Micro) for better accuracy and fallback flexibility

• Prompt-based RAG strategy tuned for objection handling, intent detection, and semantic grouping

• SageMaker-driven inference pipelines backed by model monitoring and endpoint orchestration

• IAM-based access, encrypted storage, and VPC endpoints to meet strict compliance goals

• Containerized deployment using ECS and secure CI/CD through GitHub and CodeBuild

When GenAI is applied with the right guardrails and architecture, it can change how real-world teams work, turning unstructured voice data into actionable, searchable insight. And by building entirely on AWS-native services, the solution remains secure, cost-effective, and ready to scale.