Generic playgrounds are fine for a demo. Shipping an LLM feature means testing prompts against real data, comparing models on cost and latency, and deploying with monitoring — not juggling five disconnected tools. We build the internal platform that makes that workflow yours.
Public playgrounds are built for exploration, not for shipping. A custom LLM development platform is built around your prompts, your data, and your release process.
Run the same prompt across providers and model versions against your actual use cases, not generic benchmark tasks.
Accuracy, latency, and cost tracked per model version, so a prompt change or model swap doesn't silently degrade quality.
Every prompt edit is tracked with history and rollback, instead of living in a shared doc or someone's local file.
Token usage and cost per model are visible per project and per environment, not buried in a monthly invoice.
Promote a tested prompt and model pair straight to staging or production from the same interface you evaluated it in.
No per-seat pricing on your internal tooling, and no vendor lock-in when you want to add a provider or a custom metric.
We build the modules your AI team needs to move from prototype to production with confidence.
Side-by-side prompt editing and live model response preview, with variables and templates.
Run one prompt across multiple models and providers to compare output quality, latency, and cost.
Automated and human-reviewed evaluations scored against your own test cases and datasets.
Promote prompt and model configurations through staging and production with version control.
Per-model, per-project tracking of token spend, response time, and request volume.
Shared prompts, evaluation results, and deployment history so the whole team works from one source of truth.
We build the integration layer around the providers you already use — including OpenAI, Anthropic, and open-weight models you self-host — so you can compare and switch without rewriting your application code.
Yes. We typically consolidate ad hoc playground testing and manual spreadsheet scoring into one system with versioned prompts, repeatable evaluations, and a shared history the whole team can see.
Evaluations combine automated scoring against test cases with latency and cost per request, plus optional human review for outputs that are hard to grade automatically — so a model isn't chosen on accuracy alone.
Even with a single provider, versioned prompts, regression evaluations, and cost tracking prevent silent quality drops when you update a prompt or the provider updates a model. Multi-model support can be added later without rework.
Tell us how your team currently tests and ships prompts — we'll scope a platform that fits your models and your release process.