Sales Assistant by Loka

Niche industry sales teams, such as those in the semiconductor industry, often operate in one of the most complex pre-sales environments in the tech industry. Developing large-scale customer proposals requires gathering and synthesizing data from multiple disparate systems — including a CRM (often SFDC), ERP platforms, historical quote repositories, ZoomInfo, and external web sources. This fragmented process often takes weeks or even months to complete, slowing down the Quote-to-Cash cycle and limiting sales agility. To accelerate the Quote-to-Cash cycle by drastically shortening pre-sales scoping and quote generating times, Loka’s Sales Assistant, an AI-powered agentic system, consolidates all relevant sales data into a unified platform with a conversational interface, enabling natural-language interactions with corporate data. Agent Framework The initial version will be built targeting the quote generation use-case. Phase 2 implementation will be based around a supervisor multi-agent approach ensuring tailored and accurate results for the varying additional use-cases. Technologies: Stands Agents SDK, AgentCore Runtime, Memory, Identity and Observability AI/ML Models Claude Sonnet 4.5 Training approach: No fine-tuning or distilling will be performed. Future phases will include model selection depending on the task at hand, given prior evaluation of the most suitable model per use-case. Data Processing Pipeline Source data will be ingested via ETL pipelines, orchestrated through Step Functions, executed by AWS Batch. The data will be ingested through a mix of event-based triggers and scheduled batch processes. Integration Layer Source systems will be accessed via dedicated connectors. The system will be interfacing via a standard REST (and streaming for the agent interactions) interface with the UI components, providing easy access and extensibility for future phases. The AWS architecture for the Loka Sales Assistant leverages Fargate/ECS and Batch for containerized compute and ETL processing, S3 for storage (static files, ML models, logs), and RDS for relational data. AI capabilities are delivered through Bedrock and AgentCore, while security is managed via Cognito for authentication plus IAM and Secrets Manager for access control. The networking layer uses API Gateway as the REST API entry point and CloudFront for CDN content delivery, with CloudWatch providing monitoring across all environments.