Build frontier AI products
Testing non-deterministic systems is hard. We make it easy.
Evaluations, A/B testing, and Live Monitoring for AI products.
The stack behind every successful AI product:
Evaluations
Confidence before deployment
Test any AI system against your data. Get definitive answers on quality, speed, and cost. Deploy knowing exactly what works.
A/B Testing
Real users, real insights
Split traffic between models. Let real user interactions reveal the winner—not synthetic benchmarks. Get statistical significance automatically.
Model Gateway
Every model, one interface
Drop-in replacement for any API. Access 100+ models through one endpoint. Track costs, latency, and errors in one place.
Monitoring
See what users actually experience
Complete visibility into every user interaction. Latency, throughput, errors, logs, and more. Debug your system with full context.
Instantly works with the frameworks you already use.
Recognize any of these?
RAG systems still hallucinate, no way to detect in production
"We have our own data, good retrieval, but the model still makes stuff up. How do we even detect this?"
Your agent invents facts, misses details or contradicts your documents. Without detection, you only discover it when users report obviously wrong answers.
Built-in hallucination detection plus quality signals in production. Catch issues live, then fix them during your prompt/model experimentation phase.
No reliable way to measure quality
"I tweaked a prompt and had no idea if I broke things"
No formal test cases, no baselines, no way to measure if your system actually works. Every prompt tweak, model swap, or RAG change is a gamble on user trust.
Build your 'golden set' of core use cases once. From then on, automatically test any new model, prompt, or RAG change against your benchmark in minutes.
Offline evals don't capture real user preference
Our eval pass rate is 95%, but users still complain that the new model 'feels worse'.
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While you build your offline test sets, run head-to-head model tests with real users. Let their feedback decide the winner, not just evals scores.
Multiple providers hell
"We want to use OpenAI, Anthropic, Gemini, and Deepseek. How do we do this without spending so much time?"
Every provider has different API, auth, request formats, error handling, and rate limits. You need separate fallback logic, retry mechanisms, and monitoring for each. Building a unified interface becomes its own full-time engineering project.
One OpenAI-compatible endpoint for 100+ models. We handle the auth, rate limits, and schema differences so your team can focus on product, not provider-specific glue code. Streaming, function calls, structured outputs – we handle all the complexity. Zero downtime, zero rate limits.
I can't debug agent failures
"A user reported a tool call failure. I have no idea what the inputs were, what the model's reasoning was, or why it failed. Impossible to fix."
No session replay, no reasoning traces, no intermediate steps. Just frustrated users and you playing detective with zero evidence.
Deep session replay with reasoning traces, tool call inputs/outputs, database queries, RAG retrieval steps - everything traced. It's like having a debugger for your agent.
Costs scattered across providers, impossible to track
"Our AI spend is split between five vendors. Which team burned through budget? No clue"
OpenAI for chat, Anthropic for analysis, Google for vision. Each provider has different dashboards, billing cycles, and cost breakdowns. Impossible to track spend by team, feature, or project.
Unified cost tracking across all providers with tag-based attribution. See exactly how much each team, feature, or individual user sessions cost.
My agent is slow and I have no idea why
"A 15-second response time is unacceptable. Is it the vector search, the 4 LLM calls, or the tool use?"
When latency spikes or errors occur, it takes hours instead of seconds to debug. No reproducibility, no visibility into the agent's decision flow.
Complete execution tracing with session replay. See your agent execution graph, pinpoint latency bottlenecks and optimize. Plus replay LLM calls with different models to test if switching improves outcomes.
Sound familiar? You're not alone.
Get expert guidance for your AI system
Book a free 30-minute consultation with our LLM specialists who've helped hundreds of teams ship successful AI products.
LLM Evaluations Expert
Get personalized evaluation strategies for your specific use case. We'll analyze your system and recommend the right metrics, datasets, and testing approaches.
LLM Observability Expert
Learn how to monitor your AI systems effectively. We'll show you what metrics matter, how to detect failures early, and build confidence in production.
RAG Specialist
We'll review your RAG pipeline and recommend improvements for reliability, performance, and maintainability.
What you'll get:
No sales pitch, just expert advice tailored to your needs
Ready to ship successful AI products?
Minimal setup. See results in 5 minutes.