Turn your AI agents into experts

Most teams are up and running in minutes. Quotient integrates via a lightweight SDK and OpenTelemetry-based tracing, often requiring only a few lines of code. Once traces are flowing, detections, reports, and alerts can be enabled incrementally.

You instrument your agents with OpenTelemetry (via OpenInference or supported agent frameworks). Quotient ingests traces and automatically runs Limbic detections, reporting, and monitoring on top of them, with no changes to your agent logic required.

Yes. SaaS is the fastest way to get started. For stricter security or data residency requirements, we support private VPC and on-prem deployments using Kubernetes-based infrastructure.

Pricing is based on usage (logs, detections, training runs) and deployment model. Reinforcement learning features and custom reward models are available on higher tiers. See the pricing page or contact sales@quotientai.co for details.

Pilots typically focus on one or two production agents. We define reliability goals (e.g. tool-use accuracy, hallucination reduction, retrieval relevance), run Limbic detections on real traces, and demonstrate measurable improvement before expanding usage or enabling reinforcement learning.

Teams use Quotient to avoid building and maintaining custom tracing pipelines, evaluator models, reporting logic, and RL infrastructure. Limbic provides an end-to-end learning loop: from production signals to model improvement, right out of the box.

Quotient is model-agnostic and works with OpenAI, Anthropic, and open-source models (e.g. Qwen). We support common agent frameworks and function/tool-calling workflows, ingesting telemetry directly from your existing stack.

Limbic evaluates real agent behavior across full trajectories, including: Hallucinations, Retrieval relevance, Tool-use correctness, and Failure patterns across sessions.

Instead of static rules, Limbic uses learned evaluator models that understand context across multi-step interactions. This allows it to detect subtle failures humans and simple heuristics miss.

Limbic automatically extracts reward signals from agent traces by identifying successful and failed behaviors. These signals are used to fine-tune models without manual labeling or handcrafted reward functions.

Yes. For Evolve enterprise plans, we build domain-specific reward and detection models tailored to your use case, validated on your real production data before rollout.

Yes. You can use fine-tuned models via hosted inference endpoints or receive model weights for self-hosting, depending on your deployment setup.

Quotient supports everything from early production agents to systems generating millions of traces per day. SaaS minimizes operational overhead, while VPC/on-prem deployments provide full control at scale.

Enterprise deployments support strict isolation, private networking, and controlled data retention. Traces remain within your chosen environment, and access is governed by your infrastructure and security policies.