Driving 10x engineering throughput with AI-operated SDLC

Overview

Industry

Location

Biotech and Healthcare

Various countries

Technology Used

Amazon Cognito

API GateWay

Aurora pgvector

AWS Agent Core

AWS Bedrock

AWS CloudWatch

AWS CodePipeline

AWS IAM

AWS Open/Search Serverless

AWS Secrets Manager

Claude Sonnet

CouldTrail

ECS Fargate

GitHub Actions

Lambda

LangGraph

MCP protocol

Python

Python 3.11+

REST/GraphQL adapters

Terrafrom

VPC

X-Ray

Project overview

The client wanted AI implemented across the business. An AI consulting team delivered exactly that and solved the data problem underneath it first, so the AI actually worked. Outcome-based, not time-and-materials, this project provided executive ROI visibility throughout.

Client background

One of the world’s leading health and security risk services company, our client specializes in helping organizations manage the safety and wellbeing of their global workforce, particularly those travelling or working in remote and high-risk environments.

Goals

Our client wanted to put AI to work across the business, fast. What they did not realize was that they had a problem sitting one layer down: their data. Fragmented systems and sub-threshold profile matching meant that even the best AI agents, built on that foundation, would produce unreliable output and increase risks. We delivered an AI consulting team and solved both problems. First, we rebuilt the data foundation – a core data intelligence layer with multi-source identity resolution that lifted people-profile matching past 90%, the accuracy threshold the AI vision depended on. Then we delivered the AI the client wanted: human-led, AI-driven pods which deployed agents for threat intelligence and a conversational risk co-pilot, alongside AI-operated SDLC pods that took features from backlog to tested, deployed code. Every pod was governed against outcomes – not billed by the hour – with executive ROI reviews that gave our client's leadership direct visibility from day one.

Results

6 weeks

Time from signing contract to a live, operational AI-operated SDLC platform in our client's environment.

100% automation

Covering backlog trigger, AI planning, human approval gates, code generation, test generation, deployment and quality gates.

10x end-to-end throughput

Achieved within six months meant feature development in days, not weeks.

90%+ matching accuracy

The data-foundation breakthrough that unlocked everything: people-profile matching lifted past 90%, turning unreliable risk data into a trustworthy base for AI agents.

Executive ROI visibility

Quarterly board-level ROI reviews and live delivery dashboards gave leadership direct line of sight to outcomes throughout, not just at the end.

Days, not weeks

Reducing lead time for change from weeks to days, with deployment frequency rising as the AI-operated pipeline continuously took features from backlog to production.

Fewer defects, less rework

Automated AI code review and security scanning on every change drove down defect escape and rework rates - quality measured against the client's own pre-AI engineering baseline, not a vendor claim.