Overview
ImagineX partnered with CollectivIQ to transform their internally built AI application from a part time effort into a production-grade product. ImagineX took ownership of development, design, and delivery — professionalizing the app and accelerating its roadmap for expansion to a far broader audience.
Problem
App was not production-grade, lacking the tooling and observability needed to support and monitor it reliably
No visibility into performance or how the app would hold up under a rapid increase in users
Delivery velocity was too slow for the product's ambitions
No real roadmap, just a loose collection of ideas from a part-time team
Solution
ImagineX used Anthropic's Claude to accelerate delivery — writing code, exploring designs, automating integration and E2E testing, and shaping requirements. The team professionalized the AWS-hosted app into a cloud-native, AI-native product: building a 2-phase concurrency pipeline for low-latency parallel model streaming, an automated context management system handling an 80,000-token prompt budget and 40,000-token conversation history via prompt caching and Gemini-based summarization, Amazon Bedrock RAG, multiple model vendors (Claude, Llama 4, Gemini, Grok, GPT), and 100% observability coverage through OpenTelemetry structured logging in Datadog.
Outcome
7x reduction in per-request execution cost. A tiered Claude model routing strategy cut plain-text file generation from ~$0.065 to ~$0.009 per request — routing lighter tasks to Claude Haiku 4.5 while reserving Claude Sonnet 4.6 for binary code-execution work — permanently lowering CollectivIQ's unit cost of inference at production scale.
Automated test coverage from 0% to 80%+ across all critical user flows, replacing manual-only testing with a Playwright suite orchestrated by a 9-agent AI pipeline with self-healing locators.
Full regression suite runs in under 10 minutes (smoke tests under 5), enabling automated CI checks on every pull request — not previously possible.
Sub-90-second response guarantee at production scale, with real-time streaming fan-out across 5 LLM engines delivered under a strict SLA.
4x faster time to market. CollectivIQ reached enterprise-grade production in approximately 3 months against a 12+ month conventional estimate, with 30+ major production features delivered in that window.
9 major production releases in the first 90 days, including multiple zero-downtime hotfixes.
Services Delivered
Enterprise Cloud-Native Engineering
Data & AI Innovation
Quality Engineering & Automation,
Product Management & Design
Agile Delivery & Transformation
Engagement Team
Engineering Lead
Staff Engineers
Senior Engineers
Designer
Program Manager
Technologies Used
Angular
Vite
Python
FastAPI
OpenTelemetry
AWS Bedrock
AWS ECS
AWS RDS
Claude Models and Cloud Sandboxes
Datadog
Fullstory
