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Engineering

How We Engineer

Senior engineers own the architecture, the code review, and the production system. AI tools do volume work inside that process. Engineers make the architecture, business logic, integration, and security decisions.

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Digital Scientist character pointing at the Discover, Experiment, Engineer, Optimize workflow

Every engagement follows our method: Discover → Experiment → Engineer → Optimize

Architecture

Architecture Before Code

We estimate a build against an architecture, not a feature list. That work is the core of a Blueprint, and it is how we avoid the change orders that come from pricing an incomplete spec.

C4 model

Our primary tool for architectural diagramming: context, containers, components, in a visual language the whole team shares.

UML diagrams

Activity, state, and sequence diagrams, and ERDs.

Deployment and security views

Architectural perspectives beyond the application: deployment, security, and more.

Requirements

Functional and non-functional requirements, and user stories.

Integrations mapped

Integration mapped before a line of production code.

Complexity identified early

Complexities identified proactively, before development.

AI in the Build

Where AI Does the Work, and Where It Doesn’t

The architects who scope the work lead the build and review every pull request.

Discover

AI maps what exists

Codebase analysis, dependency mapping, and recovering architecture documentation from legacy systems. Engineers interpret what it finds.

Experiment

AI scaffolds prototypes

Proof-of-concept code, fast. Engineers decide what to test and whether it worked.

Engineer

AI augments the build

Code and test generation, pull-request review assistance, documentation. Architecture, business logic, integration design, and security stay with engineers.

Optimize

AI watches production

Anomaly detection and performance analysis. Engineers decide what to change.

What goes wrong when that review is skipped: why AI-generated architecture slows startups down · the real cost of it · 25 AI architecture risks

Engineering Practice

What “Done” Means

Reviewed pull requests

Version control with Git and regular code reviews.

CI/CD

GitHub Actions pipelines build and deploy to AWS, with automated test suites in CI.

Versioned migrations

Database schema changes are versioned migrations (Flyway), reviewed like code.

Mobile pipelines

iOS and Android build-and-deploy pipelines.

DevOps

Infrastructure Is Code, and It Ships Like Code

Infrastructure as code

Terraform with Terragrunt: reusable modules, with separate configurations for staging and production.

Release pipelines

A pipeline per service, promoting from staging to production. Containers built to a private registry; web front ends deployed to CloudFront.

Runtime

Kubernetes on AWS EKS, deployed through GitOps with Helm, with autoscaling. Managed Postgres, Redis, and object storage; serverless where the workload fits.

Secrets and access

Secrets pulled from a secrets manager at runtime, never committed to code. IAM roles scoped per service, certificates in code, and a web application firewall on public endpoints.

Security & Compliance

Built for HIPAA and PHI From the First Sprint

Our own information security program is a written Information Security Plan mapped to NIST CSF 2.0, with a HIPAA Annex, PCI DSS mapping, a control register that traces every control to the requirement it satisfies, and an annual attestation from every employee.

Controls

AES-256 at rest, TLS 1.2+ in transit, role-based access with least privilege, and centralized audit logging, aligned to NIST and HIPAA.

Cloud

AWS, on HIPAA-eligible services under an AWS Business Associate Agreement. We execute a BAA with you before anyone touches PHI.

EHR integration

Production integrations with PointClickCare and Gehrimed on platforms we operate. Epic in an R&D environment. Cerner, MatrixCare, Elation, and others through HL7 FHIR APIs and ADT feeds.

Clinical data

PHI handling, data minimization, audit trails, and pre-submission CMS validation on systems that process real patient data.

Ownership

Taking Over a Codebase, and Handing One Back

Inheriting a system

We start with a code and architecture review, work inside your existing frameworks, and coordinate with the previous team where they are still around. Technical Advisory covers a review on its own.

What you own at the end

All of the code, documentation, and IP, in your repositories: architecture diagrams, API docs, data models, runbooks, the CI/CD pipeline, and structured handoff sessions. In build-operate-transfer, we also help you hire the team that takes it over.

Stack

We Choose the Stack That Fits the Problem

Cloud & infrastructure

AWS, Azure, GCP, Kubernetes (EKS), Docker, Helm, Terraform, Terragrunt, GitHub Actions

Application

React, React Native, Node.js, TypeScript, Angular, .NET, Ruby on Rails, Python, Swift, Kotlin

Data & AI

PostgreSQL, Redis, BigQuery, Power BI, Tableau, OpenAI, Anthropic, Hugging Face, TensorFlow, PyTorch

Common Questions

Engineering FAQ

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