TECHNOLOGY STACK
Tools chosen by evidence, not fashion.
Fifty-two technologies across six disciplines — each earning its place through production results. We fit your estate and your hiring market; we don't sell a stack.
AI / ML
We are model-pragmatic: frontier APIs where they win, open-weights where control or cost demands it, classic ML where it simply works better.
Claude / Anthropic API
Primary LLM for agentic systems and long-context document work; deployed via AWS Bedrock in regulated environments.
OpenAI / Azure OpenAI
GPT-family models under Azure BAA/Gov tenancies for healthcare and public-sector workloads.
PyTorch
Our default for custom model development — vision inspection, forecasting, and clinical NLP.
LangGraph
Orchestration for production agent workflows with checkpointing and human-in-the-loop gates.
XGBoost / LightGBM
Still the champions on tabular problems: fraud, risk, demand forecasting — fast, explainable, validated.
Hugging Face
Fine-tuning and serving open-weight models where data can't leave the tenancy.
MLflow
Model registry, experiment tracking, and the backbone of our retraining pipelines.
ONNX Runtime
Portable inference for edge deployments — plant floors, vehicles, review stations.
Vector search (pgvector / OpenSearch)
RAG retrieval layers sized to the problem — often Postgres is enough.
Frontend
Interfaces are where adoption is won. We build fast, accessible, WCAG-conformant frontends that operators, clinicians, and dispatchers actually choose to use.
React
The foundation of every web UI we ship, from portals to operational workbenches.
Next.js
Our default web framework: App Router, server components, edge rendering for global audiences.
TypeScript
Non-negotiable across every codebase — strict mode, end-to-end typed APIs.
Tailwind CSS
Design-system-driven styling that keeps 40-screen enterprise apps visually coherent.
React Native
Cross-platform mobile for field and store apps, with offline-first sync architectures.
Flutter
Chosen when a single team must ship polished iOS/Android apps with heavy custom UI.
Radix UI
Accessible primitives underneath our component systems — keyboard and screen-reader correct by default.
Playwright
End-to-end testing and accessibility audits wired into every CI pipeline.
Backend
Boring-on-purpose backends: typed, observable, horizontally scalable services matched to each client's existing estate and hiring market.
Node.js / NestJS
High-velocity API development with shared types across frontend and backend.
Python / FastAPI
The service layer around ML — inference APIs, data services, async pipelines.
Java / Spring Boot
Core-system integration in banking and insurance, where the estate speaks Java.
Go
Latency-critical services: fraud scoring, routing engines, high-throughput gateways.
.NET
Enterprise line-of-business systems and deep Microsoft-stack integrations.
PostgreSQL
Our default database — with PostGIS, pgvector, and partitioning before reaching for exotic stores.
Temporal
Durable execution for long-running business workflows that must survive anything.
GraphQL / gRPC
GraphQL at product edges, gRPC between internal services — chosen per boundary, not by fashion.
Cloud & Infra
Multi-cloud by client reality, not by slogan. Landing zones with compliance-as-code, then infrastructure that teams can operate after we leave.
AWS
Deepest bench: EKS, Bedrock, GovCloud for public sector, HealthLake for clinical data.
Microsoft Azure
First choice for Microsoft-estate enterprises; Azure Government and Health Data Services for regulated work.
Google Cloud
Preferred where BigQuery and Vertex AI anchor the data and ML strategy.
Kubernetes
Standard runtime across clouds — plus K3s at the edge in plants and vehicles.
Terraform
Every environment we build is code: reviewable, repeatable, destroyable.
Cloudflare
Edge delivery, WAF, and zero-trust access for public-facing platforms.
Kafka
The event backbone in most of our architectures — from factory telemetry to payment streams.
Redis
Online feature stores, caching, and sub-millisecond lookups in scoring paths.
Data
AI is downstream of data. We build lakehouse foundations with lineage, quality gates, and governance that survive audits and acquisitions.
Snowflake
Governed enterprise warehousing — our most common analytical foundation in finance and retail.
Databricks
Lakehouse platform for ML-heavy estates: feature engineering, training, and streaming in one place.
dbt
Transformation-as-code with tests and documentation — the contract layer of every data platform.
Apache Airflow
Batch orchestration standard; Dagster where asset-based lineage earns its keep.
Apache Flink
Stateful stream processing for real-time features: fraud aggregates, network state, telemetry.
BigQuery
Serverless analytics at retail scale — strongest where GCP anchors the stack.
TimescaleDB
Time-series workhorse for sensor and telemetry data at the edge and in the cloud.
Great Expectations
Data-quality gates in pipelines, because no model survives bad inputs.
DevOps & QA
Delivery infrastructure is a product too. We leave behind pipelines, observability, and quality gates that make the next change cheaper than the last.
GitHub Actions
CI/CD default: build, test, security-scan, and deploy from a single reviewed workflow.
ArgoCD
GitOps delivery to Kubernetes — the cluster state is always what the repo says.
Grafana / Prometheus
Metrics, dashboards, and alerting for everything we run, OT to LLM.
OpenTelemetry
Distributed tracing standard across services — and through agent pipelines.
Sentry
Error tracking wired to release health, so regressions surface in minutes.
SonarQube
Static analysis and coverage gates enforced in CI, not aspired to in wikis.
Playwright / k6
Functional and load testing — every peak-season system is tested at 10–20x baseline.
Snyk / Trivy
Dependency, container, and IaC scanning in the pipeline, mapped to client security policy.
HOW WE CHOOSE
Our selection principles
Technology choices outlive projects. These are the rules we apply before any tool enters a client architecture.
Fit the estate
We build in the languages and clouds your teams can hire for and operate — a perfect stack you can't staff is a liability.
Boring where possible
Postgres before exotic databases, proven queues before novel ones. Innovation budget goes where it differentiates.
Exit-friendly
Open standards, infrastructure as code, and documentation mean you're never locked into us or any vendor.
Compliance-aware
Every tool is vetted against the regulatory reality of the industry it serves before it enters the design.
Wondering if we work with your stack?
Almost certainly — and if we don't, we'll say so in the first call. Bring your architecture; our engineers will meet you at whiteboard depth.