About project

The Challenge

Kwiksave’s delivery network grew, so did the volume of valuable insights buried in their delivery data – insights that could translate into commercial opportunities. Turning these signals into actionable leads, however, was far from simple. The company faced several key challenges:

• Complex and Inconsistent Data: The logistics data itself posed major hurdles. Metadata structures varied across different clients, recipient information was often incomplete, and delivery records were primarily designed for operational use.
• Risks with Early AI Exploration : Initial experiments with fully autonomous AI agents revealed additional challenges. While promising in theory, these agents sometimes generated low-confidence or even fabricated data, which could not be trusted for commercial decision-making. Moreover, operating costs were unpredictable, introducing financial risks that Kwiksave could not afford to take on at scale
• Need for a Balanced, Pragmatic Solution: Kwiksave needed a solution that would meaningfully reduce the manual workload while ensuring data accuracy and reliability. Any approach had to offer predictable costs, integrate seamlessly with CRM workflows, and align with marketing best practices.
• Time-Intensive Lead Qualification: Teams had to identify the correct recipient, validate the company, discover the appropriate role, and enter all relevant details into the CRM. This repetitive process consumed significant resources and slowed the sales pipeline, making it difficult to capitalize on opportunities at scale.

Operationally, the company was at a crossroads: the platform needed to scale while remaining compliant, stable, and cost-efficient. Strategically, the inability to migrate smoothly and deliver updates faster risked client trust and limited growth opportunities in the competitive healthcare SaaS market.

Technology & Approach

Dedicatted designed and implemented a secure, serverless, AI-enabled platform on AWS, built around an event-driven architecture to ensure transparency, scalability, and strong cost governance. The solution was created to support real-world logistics workflows, streamline enrichment processes, and enable controlled scaling without introducing infrastructure complexity or disrupting existing operational models.

The architecture combines orchestration, automation, and AI-driven enrichment, fully aligned with AWS best practices for reliability, observability, and operational efficiency. Key components of the solution:

Serverless Orchestration Layer (AWS Step Functions)

Multi-stage workflows are coordinated through AWS Step Functions, enabling structured data processing, enrichment, and validation. This ensures full traceability across each step while maintaining predictable execution and cost control.

Event-Driven Compute (AWS Lambda)

Lambda components provide fine-grained control over execution logic, processing time, and AI service invocation. This modular setup supports rapid scaling as delivery volumes grow while keeping operations efficient and resilient.

Logistics-Specific Data Enrichment & Normalization

The platform incorporates advanced normalization of delivery metadata and configurable filtering logic. These mechanisms exclude existing customers, remove undesired vehicle types, and ensure only relevant, high-quality opportunities enter downstream workflows.

Lead Quality & Duplication Safeguards

Automated validation layers prevent duplicate or low-quality leads from entering campaigns, improving data reliability and operational outcomes for logistics teams.

Administrative Control & Governance Interface

A secure administrative environment provides visibility into enrichment flows, filtering logic, and approval decisions. Teams can monitor operations, adjust parameters, and maintain governance without modifying core infrastructure.

A core design principle of the platform is cost-aware AI usage. By applying GenAI only after deterministic validation steps, the system minimizes unnecessary inference calls and prevents low-confidence data from entering business systems.

This hybrid approach ensures that AI costs remain proportional to realized business value, while maintaining governance, auditability, and operational trust — a critical requirement for production deployment at scale.

Business impact

The implemented platform materially reduced the operational burden of lead qualification. What previously required up to 45 minutes of manual research per lead was transformed into a short, structured human review process, allowing sales teams to focus on decision-making rather than data gathering.

Structured Lead Readiness for Campaign Activation: Integrated controls ensure that only verified, relevant, and campaign-ready leads move forward into HubSpot and Mailchimp. This improves targeting precision, supports more consistent outreach execution, and strengthens confidence in downstream marketing and sales activities.

Governed Data Quality & Process Consistency: Embedded checks for CRM hygiene, duplication prevention, and AI-assisted validation introduced a more standardized approach to lead handling. Teams now operate within a controlled framework that reduces variability, limits manual errors, and maintains long-term data integrity.

Foundation for Continuous Optimization: The organization now has the tools and visibility needed to refine filtering logic, enrichment criteria, and campaign readiness over time. What began as a manual, resource-intensive process has evolved into a structured, AI-supported operational capability—positioning Kwiksave to scale outreach efforts and continuously improve performance without reengineering core workflows.