Engineering
The stack we ship on.
From interface to inference, one team owns the whole stack. These are the tools we reach for when we build, run and secure production systems.
Expertise
Deep across the stack, not spread thin.
Grouped the way we reason about a platform: the interface, the services behind it, the intelligence layered on top, and the ground it all runs on.
Frontend
01Fast, accessible interfaces that hold up on every screen.
- React
- Next.js
- TypeScript
- Tailwind CSS
- shadcn/ui
- React Native
- Vite
Backend
02Services and data layers built to stay correct under load.
- Python (FastAPI, Django)
- Node.js (Express, NestJS)
- Go
- PostgreSQL
- MongoDB
- Redis
- Kafka
- RabbitMQ
- REST & GraphQL APIs
Agentic Workflows
03Agents that plan, call tools and finish real work.
- Pi Agent
- Hermes Agent
- OpenAI Agents SDK
- Claude Agent SDK
- LangGraph
- PydanticAI
- Letta
- Model Context Protocol
Knowledge & Retrieval
04Retrieval pipelines that ground models in your own data.
- LlamaIndex
- Haystack
- Qdrant
- Weaviate
- Milvus
- Chroma
- Unstructured
- Cohere Rerank
Model Routing
05One gateway across providers, with fallbacks and cost control.
- LiteLLM
- OpenRouter
- Portkey
- Requesty
- Helicone
- Groq
Evaluation & Observability
06Measure quality, trace every call, catch regressions early.
- Langfuse
- Arize Phoenix
- Ragas
- DeepEval
- Promptfoo
- Braintrust
- OpenTelemetry
- OpenInference
- ELK
- Datadog
- Dynatrace
- Graylog
- Grafana
- Prometheus
- Bezel
Safety & Guardrails
07Policy, PII handling and abuse controls around every model.
- NeMo Guardrails
- Guardrails AI
- Microsoft Presidio
- Llama Guard
- Prompt Guard
Models & Providers
08The right model for the task, frontier or open weight.
- OpenAI
- Anthropic
- Gemini
- Llama
- Mistral
- Qwen
- DeepSeek
- Cohere
Inference Engines
09Self-hosted serving tuned for throughput and latency.
- vLLM
- Ollama
- llama.cpp
- MLX
Containers & Orchestration
10Reproducible builds that scale from one node to a cluster.
- Docker
- containerd
- Docker Swarm
- Kubernetes
DevOps
11Pipelines and infrastructure as code, from commit to release.
- Git
- GitHub Actions
- Jenkins
- Terraform
- ArgoCD
- Ansible
Cloud
12Hyperscalers and GPU clouds, matched to workload and budget.
- AWS
- Azure
- GCP
- Cloudflare
- RunPod
- Vast.ai
- Cerebras
- Groq
Access & Networking
13Secure routing and private connectivity between services.
- Nginx
- Caddy
- Tailscale
- Pangolin
Security & Code Quality
14Static analysis and dependency scanning in the pipeline.
- SonarQube
- Snyk
- Prisma Cloud
How we engineer
Opinions we build into every system.
Tools change from project to project. These do not. They are the habits that keep a system correct, observable and cheap to change long after launch.
Type safe, end to end
Typed contracts run from the client through the API to a schema the database enforces. The compiler catches a whole class of bug before it ever reaches a user.
Tested where it earns its keep
We test the logic that would hurt to get wrong, not to chase a coverage number. Fast checks on the core, end to end coverage on the paths people actually take.
Observable from the first deploy
Logs, metrics and traces are wired in before launch, not after an incident. When something moves at 3am, we can see why without instrumenting under pressure.
Shipped behind a switch
Features roll out behind flags in small increments. A bad release is a toggle away from reverted rather than a redeploy away, so shipping stays calm.
Next step
Have a stack in mind, or none at all?
Either way, we will map the right tools to your problem and the constraints you are working under, then build it.