Nivalis - AI product engineering studio
Ship AI features that hold up in production.
Nivalis builds AI-enabled products for startups and e-commerce teams - and open-sources the tooling we rely on: evaluation, orchestration, and reliability.
- Entity
- Nivalis - AI product engineering studio
- Focus
- LLM features, retrieval, evals, orchestration, production reliability
- Proof
- Open-source libraries maintained by Nivalis
- Engage
- Ship an AI feature or adopt our OSS
Principles
We build AI systems like software systems: explicit constraints, measurable behavior, and clear ownership.
Outcomes over prompts.
Prompts are cheap; measurable behavior is not.
Evals are the product spec.
If you can't score it, you can't improve it.
Reliability beats cleverness.
Guardrails, fallbacks, and clear failure modes.
Latency is UX.
Tokens, calls, and retrieval have a cost - optimize end-to-end.
Data is leverage.
Use real conversations, real edge cases, real feedback loops.
Secure by default.
PII discipline, least privilege, vendor boundaries.
Open source when it compounds.
Publish reusable primitives, not glue.
What we build
AI product features
Assistants, copilots, semantic search, automated workflows. Designed to be useful, not magical.
AI infrastructure (the boring parts that matter)
Retrieval pipelines, tool/function calling, queues, caching, rate limits, and cost controls.
Quality systems
Offline evals, online monitoring, dataset curation, red-teaming, regression tests, human feedback loops.
Fast iteration, hard reliability.
Open source
We publish the components we use to build reliable AI systems - small, documented, opinionated.
Featured library
<LIB_NAME>
<what it enables in one sentence>
Designed for: <evals | orchestration | memory | RAG | monitoring | etc.>
Why it exists: <recurring pain removed>
Install
npm i <package-name>How we work
We optimize for time to reliable- not time to demo.
Define success
We turn the feature into an eval suite + acceptance thresholds.
Build the system
Retrieval/tools/flows with guardrails, caching, and fallbacks.
Close the loop
Ship, measure, collect failures, improve the dataset, repeat.
Selected outcomes
- Lowered hallucination rates on critical intents via targeted evals + retrieval constraints.
- Reduced latency and cost through caching, batching, and prompt/token budgets.
- Increased conversion on high-intent flows by integrating AI assistance with deterministic UX.
FAQ
Do you build "agents"?
Yes - when they're bounded. We prefer narrow tools with explicit permissions and measurable outcomes.
How do you ensure quality?
We treat evals as a first-class artifact: offline regression + online monitoring + human review on edge cases.
Which models do you use?
We're model-agnostic. We pick based on quality/latency/cost constraints and add fallbacks.
Can we use your open-source libraries commercially?
Yes, subject to each repository license.
Work with Nivalis
If you want AI features that ship with evals, observability, and sane failure modes: