We've embedded with high-stakes engineering teams across regulated, real-time and scale-sensitive industries. Six verticals, one delivery system, the same people running both human judgment and AI.
We embedded a 6-person pod, stabilized the regression suite, rebuilt the deploy pipeline around AI-driven risk scoring, and trained their team into ownership over 14 months.
Reproducible eval, ground-truth versioning and adversarial test generation so the team can update model weights without holding the release for a month.
Strangler-fig migration pattern, dual-write integration tests, and an on-call rotation across two time zones during cutover.
Pod assembled in twelve days. We rebuilt the data pipeline, set up the SRE rota, and stayed on long enough to coach four internal hires through promotion.
Co-designed with their clinical team, with guardrails, escalation paths and full conversation audit. The model is theirs, not ours.
Shadow-deployed the new engine alongside the legacy path, diffed every order, then cut over instrument-by-instrument over six weeks.
We took over the regression suite, wrote the chaos-engineering rota, and put our applied-AI team on flake triage. The exchange hasn't missed a market open since.
Built on their data, their model choice, their compliance review. We provided the eval harness, the guardrails and the on-call.
Product engineering pod, full stack from FastAPI to React Aria. Shipped a working v1 in week six and the team still uses it daily.
Embedded with their team for eighteen months. The product launched on schedule and three of their COBOL engineers are now writing Rust.
Next.js on the front, a Shopify backbone, our SRE team running the on-call rota through the four-day weekend.
Custom recommendation system built on their catalog, trained on the brand's own copywriting style, evaluated against human merchandisers weekly. Lifted AOV without anyone calling it creepy.
Stripped a 4MB bundle to 380KB, refactored the cart for offline-first, and instrumented every step so the team can tell what's actually slow.
Real-time replication from POS to a unified inventory layer, with a forecasting model that runs locally per store.
Rebuilt the billing pipeline, added reconciliation tests against three downstream systems, plugged the leaks they hadn't realized were costing them six figures a month.
We took the spec, the design system and a half-built prototype, and turned it into a live product their customers pay for. Quadrant's own engineers ran the architecture; we ran the build.
Isolation by row, by schema, by namespace, depending on what the customer needed. Migrated 240 self-hosted customers without a single ticket.
Tuned against their detection corpus, with a feedback loop the SOC team owns. Cut analyst burnout before it cut analyst headcount.
Stream processing, vector store, eval harness, observability. Boring on the surface. The features they ship on top are not.
Better docs, better errors, a CLI that scaffolds the first integration in 90 seconds. Quiet work, loud results.
Re-routed origin traffic, rebuilt the manifest service, brought P95 buffer events down to single digits per million streams.
Joint roadmap with their content team, eval harness aligned to retention not CTR, full transparency surface so editors can see why a title is or isn't getting a push.
Kubernetes on bare metal, a render queue we wrote from scratch, and a control plane that artists actually use.
Inference pipeline running on the stream itself, with editorial overrides, brand-safety guardrails, and a buffered queue for the unexpected.
Headless, typographically opinionated, with a built-in audit log so the legal team stops emailing screenshots.
Optimizer in production with a transparent score for every decision. Dispatchers override when they should, and the model learns when they do.
Rebuilt the planning layer, plugged in the gate, the rail and the yard. Same team, same equipment, dramatically more throughput. The vessels that used to wait now don't.
Built with five drivers in the room every Friday. Voice navigation, offline routing and a one-tap exception flow.
Modernized one DC, then the next, with a playbook the operations team could read on their own and run on their own.
Edge model on the device, anomaly detection in the cloud, and an SLA the food customers actually believe.