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Client Engagement Executive Advisory / AI

Multi-Model AI Platform: Notebooks to Production

How Tech Stack Playbook engineered a serverless, container-based multi-model AI pipeline — transforming Jupyter notebooks into a fully managed, production-grade AWS platform serving elite coaching clients.

Multi-Model
AI Pipeline
Serverless
Architecture
Zero
Idle Compute Cost
CDK
Fully Typed IaC
Executive Summary

Overview

Tech Stack Playbook was engaged by an elite executive advisory firm to solve a critical product engineering challenge: their proprietary AI models — custom-trained systems for analyzing coaching session transcripts — existed only as research artifacts in Google Colab Jupyter notebooks. There was no path to production.

We designed and built a fully managed, serverless multi-model AI pipeline on AWS — containerizing models into Lambda functions orchestrated by Step Functions, with DynamoDB for fast caching and a purpose-built application layer for consuming model outputs.

The Challenge

The Research-to-Production Gap

The firm had sophisticated, fine-tuned AI models capable of extracting performance patterns from private coaching sessions. The problem was not the models — it was everything around them.

  • All models lived in Google Colab — academic environments with no path to application integration
  • Workflows required manual execution by a single researcher; no one else could run them
  • Models ran sequentially with no orchestration, state management, or error handling
  • No infrastructure to serve outputs to an application, store results, or make them queryable
  • No IaC, no CI/CD, no environment management — the AI capability was trapped in a browser tab
  • Clients generating $100M+ annually expected institutional-grade analysis
What We Delivered

Serverless Multi-Model Architecture

The platform addressed every dimension of the research-to-production gap: containerization, orchestration, state management, data persistence, IAM governance, CI/CD, and application delivery.

01
Model Containerization
Docker containers with full dependency trees deployed to ECR and Lambda — same images for dev and prod.
02
Step Functions Orchestration
State machine orchestrating the progressive multi-model pipeline with structured I/O, error handling, and execution visibility.
03
DynamoDB Fast Caching
Table schemas optimized for fast read access patterns aligned to application query needs — serverless scaling for bursty workloads.
04
AWS CDK Infrastructure
Fully typed, programmatic infrastructure — explicitly rejecting LLM-generated IaC in favor of deterministic, auditable definitions.
05
GitHub Actions CI/CD
Automated build, deploy, and validation pipelines — any team member can deploy through standard PR workflow.
06
Application & Reporting Layer
Client-facing platform surfacing model outputs as structured coaching intelligence reports for executive consumption.
Key Insight
The AI models themselves produce probabilistic outputs, but the infrastructure that runs them must be entirely deterministic. This tension — managing probabilistic AI workloads through typed, auditable infrastructure code — was a core design consideration throughout the engagement.
Results

Outcomes & Business Impact

Research → Production Proprietary AI models now run as a managed, production-grade pipeline available to the entire team.
Scalable Orchestration Progressive multi-step AI pipeline with full state management, error handling, and execution visibility.
Zero Idle Cost Lambda-based execution means the firm pays only for actual model inference time.
Team Unblocked Research team focused on model improvement rather than fighting infrastructure.
Technical Stack

Technologies Used

AWS Lambda Docker Amazon ECR Step Functions DynamoDB AWS CDK GitHub Actions TypeScript Fine-Tuned AI Models