Ship AI features that hold up in production.
LLM applications, RAG pipelines, and ML integrations, taken from prototype to production with evaluations and guardrails that keep them reliable.
Start a projectProblems we solve
You've probably lived at least one of these. Here's exactly how we take it off your plate.
The situation
Your AI prototype demos well but hallucinates or breaks once real users touch it.
What we do
We wrap it in an evaluation harness and guardrails, so quality is measured and failures are caught before customers see them.
How we'd run it
- Discover
We gather the real failure cases and define what 'good' means for your use case.
- Define
An eval set and quality bar agreed in writing: the number we're actually moving.
- Develop
Guardrails, retries, and fallbacks added; every change scored against the evals.
- Deliver
Shipped with the eval harness running in CI, so quality can't silently regress.
The situation
You want to add an LLM or ML feature but don't have AI engineering depth in-house.
What we do
We design and build the feature end to end, covering model choice, retrieval, and integration, then hand it off documented so your team owns it.
How we'd run it
- Discover
We pin down where AI actually adds value, and where a simpler approach wins.
- Define
Scope, model strategy, and success metrics agreed before we build.
- Develop
A working feature wired into your stack, with evals and cost controls from day one.
- Deliver
Handed off with docs and a runbook so your engineers maintain it confidently.
The situation
Your RAG or search returns answers that are wrong, stale, or miss the obvious.
What we do
We rebuild the retrieval layer, from chunking to embeddings to reranking, and measure answer quality instead of guessing at it.
How we'd run it
- Discover
We trace where retrieval goes wrong on your real queries.
- Define
A retrieval eval set and the target quality, written down.
- Develop
Better chunking, embeddings, and reranking, with each change scored against the evals.
- Deliver
A retrieval pipeline you can trust, with the eval set to keep it honest.
The situation
AI costs and latency are climbing and hard to predict as usage grows.
What we do
We add caching, model routing, and smaller-model fallbacks so you pay for the quality you need, not the biggest model on every call.
How we'd run it
- Discover
We profile where tokens, latency, and spend actually go.
- Define
A routing and caching plan with explicit cost and latency targets.
- Develop
Cheaper models where they suffice, caching for repeats, guardrails on spend.
- Deliver
Observability on cost and latency so surprises show up early, not on the invoice.
The situation
You can't tell whether your model is getting better or worse over time.
What we do
We instrument the system with evals, tracing, and feedback loops so quality is a number you watch, not a feeling.
How we'd run it
- Discover
We define the quality signals that actually matter for your users.
- Define
An eval and monitoring plan — what's tracked, and what triggers action.
- Develop
Tracing, online evals, and user-feedback capture wired into the system.
- Deliver
A dashboard and alerts so regressions surface before users complain.
Toolkit
The stack behind it
The tools we reach for. We pick per project, not per fashion, and these are what we run in production today.
Languages
- Python
- TypeScript
- Rust
- C++
LLM Providers
- OpenAI
- Anthropic
- Gemini
- Mistral
- Llama
- Hugging Face
Frameworks & Orchestration
- PyTorch
- TensorFlow
- Keras
- scikit-learn
- LangChain
- LlamaIndex
- NumPy
- Pandas
- Jupyter
- OpenCV
Vector & Retrieval
- pgvector
- Qdrant
- Pinecone
- Weaviate
- Chroma
- Elasticsearch
- Redis
Training & Fine-tuning
- Ray
- Weights & Biases
- MLflow
- DVC
Serving & Inference
- NVIDIA
- ONNX
- Ollama
- vLLM
- Replicate
- Modal
Data & Pipelines
- Airflow
- Spark
- dbt
- Kafka
- Snowflake
- Databricks
Eval & Observability
- LangSmith
- Langfuse
- Ragas
- Pydantic
- Sentry
- Grafana
- Prometheus
- OpenTelemetry
Cloud & MLOps
- AWS
- Google Cloud
- Azure
- Docker
- Kubernetes
- Terraform
Who this is for
- Teams with a promising AI prototype that keeps breaking or hallucinating in real use
- Teams who want to add LLM or ML features but lack in-house AI engineering depth
- Companies that need evals, guardrails, and monitoring before they can trust AI in production