What was the challenge?
The customer had no intelligent interface to query campaign performance, attribution, or marketing metrics across its enterprise client base, so any dynamic analysis required manual analyst effort and significant turnaround time. Consultants spent significant time manually preparing recurring QBR, MBR, and BPO reports, and needed faster access to structured knowledge across client project repositories, all while maintaining strict data isolation between client accounts.
Manual Reporting
Consultants prepared QBR, MBR, and BPO reports by hand for every client cycle.
Slow Analysis
Dynamic analysis of marketing data required manual analyst effort and long turnaround times.
Fragmented Knowledge
No intelligent interface existed to query marketing performance, attribution, and analytical studies across clients.
Data Isolation
Maintaining strict separation across multiple client environments constrained how teams could work, with nothing at the infrastructure level guaranteeing one client's data stayed separate from another's.
How did Needl.ai help?
Needl.ai's forward deployed engineering team built AskNeedl, a natural-language interface over the customer's marketing data sources, white-labeled for the customer's consulting workflows and deployed inside the customer's own AWS environment. The solution runs on Amazon EKS with EC2 Auto Scaling node groups, with Amazon RDS for PostgreSQL and Amazon ElastiCache (Redis) handling data and session state, Amazon S3 for storage, and a multi-VPC setup with public and private subnets, security groups, and a bastion host for secure access. Amazon Cognito handles authentication, with Route 53, Certificate Manager, and a Network Load Balancer fronting the deployment, and CloudWatch and CloudTrail providing monitoring and audit logging.
Conversational Metrics Interface: Consultants query campaign performance, channel attribution, and marketing metrics across client accounts in natural language.
Dynamic Analytics Engine: Runs live analysis on marketing data on demand, including channel contribution and spend versus returns from the latest data.
Automated QBR Generation: First-draft Quarterly Business Review reports are generated from live engagement data, chatbot insights, and internal documentation, cutting multi-hour report assembly down to minutes.
Role-Based Access Controls: Client-level data isolation is enforced across every solution, with per-client separation built into the AWS infrastructure, aligning with enterprise security and confidentiality requirements.
What changed?
AskNeedl reduced manual reporting effort, shortened delivery cycles, and allowed consulting teams to take on more client engagements without a proportional increase in workload, all while running on AWS infrastructure dedicated to the customer.
70%+ reduction in manual report preparation effort
Faster access to marketing metrics without waiting for analyst pull
Improved turnaround time for client deliverables and reporting cycles
Client data isolation enforced at the AWS infrastructure layer, not just the application tier
“Preparing a quarterly review used to take our consultants the better part of a week. The first draft now arrives ready to review, and the team spends that time on the client conversation instead.”
Credit reports in under an hour
US$250K in a single trading day