How we pick what to use (and what to avoid).
Start with the handoff
The team that maintains this system after we leave matters more than the team building it now. We choose technologies your people can hire for, train on, and debug without calling us back.
Boring is often better
We'll pick a proven database with a 20-year track record over a trendy one with a 20-month runway. The best technology choice is the one that's still working five years from now with minimal fuss.
Best athlete wins
We don't mandate a single AI model, IDE, or cloud provider. Our Conductors use the best tool for each task. When AI models shift, and they will, we shift with them.
Don't build what you can buy
Custom software is expensive to build and expensive to maintain. We'll tell you when a SaaS product or managed service covers 90% of what you need, and only build the 10% that doesn't exist yet.
The foundations we build on.
We don't have a "house language." We have six that we know deeply and deploy based on what the project actually needs. Each one was earned through years of production work, not a weekend tutorial.
Ruby
Our longest-running language. Elegant and expressive, ideal for rapid application development and the backbone of many production systems we maintain.
JavaScript
The lingua franca of the web. We use it across the full stack, from interactive front-ends to server-side Node.js applications.
TypeScript
Type-safe JavaScript for teams that want fewer 3 a.m. surprises. Our default choice for any serious front-end or Node.js project.
Python
The workhorse of data engineering and AI. From ETL pipelines to ML model training to agentic workflows, Python is where most of our data and intelligence work lives.
C#
Enterprise-grade and battle-tested. We use C# for .NET applications, Azure integrations, and the kind of large-scale systems where type safety pays for itself ten times over.
SQL
Not glamorous. Absolutely essential. Every data engineering engagement starts with solid SQL, and we write a lot of it by hand because ORMs only get you so far.
Where code meets architecture.
Frameworks shape how fast you ship, how maintainable the code stays, and how easily your team can take over after we leave. We pick them for those reasons, in that order.
Ruby on Rails
Convention over configuration, done right. We've shipped dozens of Rails applications since 2011. It's still one of the fastest paths from concept to production for web applications.
Django
Python's answer to full-stack web development. We reach for Django when a project needs Python's data ecosystem and a production-grade web framework in the same codebase.
React
Our go-to for complex front-end applications. Component-based architecture that scales from a settings panel to a full enterprise dashboard.
Next.js
React with batteries included. Server-side rendering, static generation, API routes, and deployment that doesn't require a dedicated DevOps team.
Vue.js
Approachable without being simplistic. We use Vue for applications where the client's internal team needs to maintain and extend the codebase after we leave.
Node.js
Server-side JavaScript for real-time applications, API servers, and microservices. Particularly effective when the front-end and back-end share a language.
ASP.NET Core
Microsoft's modern, cross-platform web framework. We use it for enterprise applications, especially in Azure-heavy environments where the .NET ecosystem is already established.
React Native
Cross-platform mobile development that shares code between iOS and Android. The right call when you need a mobile app and don't want to maintain two separate codebases.
Native where native earns its keep.
Cross-platform covers most needs, and we ship plenty of it. But when an app has to feel like it truly belongs on the device, we build native for Apple platforms with Swift and SwiftUI.
Swift
Apple's modern language for iOS, iPadOS, and macOS. Fast, safe, and the right call when a client needs a genuinely native app, not a web view in a wrapper.
SwiftUI
Apple's declarative UI framework for building native interfaces across iPhone, iPad, and Mac from a single codebase. We reach for it when the experience has to feel native, not just cross-platform.
Where the data lives and how it moves.
Most AI projects fail because the data isn't ready. Most data projects fail because the pipeline was never designed for what comes next. We think about both at the same time.
PostgreSQL
Our default relational database. Rock-solid, extensible, and capable of handling everything from small SaaS apps to multi-terabyte analytical workloads with pgvector for AI.
SQL Server
The enterprise database that runs a huge chunk of mid-market America. We're fluent in it, including the legacy stored procedure tangles we're often hired to untangle.
MongoDB
Document storage for when your data doesn't fit neatly into rows and columns. We use it selectively, for the use cases where schema flexibility is a genuine advantage.
Redis
In-memory data store for caching, session management, real-time leaderboards, and anything else that needs sub-millisecond response times.
Snowflake
Cloud data warehouse for analytical workloads. Separates compute from storage, scales on demand, and plays well with dbt for transformation pipelines.
Apache Kafka
Distributed event streaming for real-time data pipelines. When systems need to communicate asynchronously at scale, Kafka is usually the answer.
dbt
SQL-based data transformation that brings software engineering practices to analytics. Version-controlled, testable, and auditable data models.
Apache Airflow
Workflow orchestration for data pipelines. Schedules, monitors, and retries complex ETL jobs so your data team isn't babysitting cron jobs at midnight.
Apache Spark
Distributed data processing for datasets that outgrow a single machine. Batch processing, streaming, and ML workloads at scale.
Elasticsearch
Full-text search and analytics engine. We deploy it for application search, log analysis, and the kind of complex querying that relational databases struggle with.
The stuff that keeps everything running at 3 a.m.
Infrastructure is built during the project, not bolted on after launch. Every engagement includes CI/CD, monitoring, and deployment automation. Your team inherits a system that deploys itself, not a runbook.
Terraform
Infrastructure as code that works across every major cloud provider. We define infrastructure in version-controlled config files, not in a console someone forgot to document.
AWS
The cloud platform with the broadest service catalog. We deploy to AWS regularly and hold deep experience with EC2, RDS, Lambda, S3, ECS, and dozens of other services.
Google Cloud
Strong data and ML capabilities. We reach for GCP when BigQuery, Vertex AI, or Google's data ecosystem is the best fit for the client's analytical workloads.
Microsoft Azure
The natural home for .NET and enterprise Microsoft shops. We deploy to Azure for clients whose organizations already run on the Microsoft stack.
Docker
Containerization that makes "it works on my machine" a solved problem. Every project we ship includes containerized environments for consistent development and deployment.
Kubernetes
Container orchestration for production workloads that need to scale, self-heal, and manage complex deployments. We use it when the complexity is justified; not every app needs it.
GitHub Actions
CI/CD automation built into the repository. Automated testing, linting, security scanning, and deployment pipelines triggered on every commit.
Cloudflare
Edge computing, CDN, DNS, and security for projects that need edge controls, traffic protection, and global delivery.
The models, frameworks, and tools behind production AI.
We're model-agnostic by design. The right model depends on the task, the data sensitivity, the latency requirements, and whether the client needs API-hosted or self-hosted inference. We've shipped with all of them.
OpenAI
GPT models for text generation, embeddings, and function calling. We use the API tier with enterprise data protections for client work, never consumer plans.
Anthropic
Our primary reasoning and coding model. Claude excels at architectural thinking, large codebase analysis, and the kind of nuanced judgment calls that separate good AI work from parlor tricks.
Google Gemini
Multimodal AI with strong integration into Google Cloud's ecosystem. Particularly useful for vision tasks, document understanding, and Vertex AI deployments.
LangChain
Framework for building LLM-powered applications. Chains, agents, retrieval, and memory. We use it to build the orchestration layer between models and production systems.
LlamaIndex
Data framework for connecting LLMs to private data sources. RAG pipelines, document parsing, and structured data extraction that actually works at scale.
Hugging Face
The open-source ML hub. Model hosting, fine-tuning, and deployment. When a client needs a specialized model or wants to self-host for data sovereignty, Hugging Face is the starting point.
PyTorch
Deep learning framework for custom model development. We use PyTorch for fine-tuning, custom training pipelines, and the research-to-production path for ML systems.
scikit-learn
Classical machine learning that still solves most real-world problems. Classification, regression, clustering, and feature engineering. Not everything needs a neural network.
Pinecone
Managed vector database for similarity search and RAG applications. When you need to find semantically similar documents across millions of embeddings, fast.