Calibrtr provides end‑to‑end AI services for enterprises, designing, building, and operating custom machine‑learning models and data pipelines aligned with specific business KPIs. Their managed MLOps platform handles deployment, real‑time monitoring, drift detection, and ongoing optimization, delivering measurable impact without requiring in‑house AI expertise.
Funding
Funding not disclosed
Founders
Product
Problem
Many enterprises lack the expertise and infrastructure to develop, deploy, and maintain AI solutions that produce reliable, measurable business outcomes. This results in costly pilot projects, delayed time‑to‑value, and missed opportunities to automate or augment core processes.
Solution
Calibrtr offers an end‑to‑end service that designs, builds, and operates AI systems tailored to a client’s specific objectives. The company collaborates with business stakeholders to define clear performance metrics, then engineers custom models and data pipelines that align with those goals. Once developed, Calibrtrtr handles full‑stack deployment, continuous monitoring, and ongoing optimization to ensure the AI solution remains accurate and cost‑effective in production. By providing a managed AI platform, the firm removes the need for internal MLOps expertise and accelerates the delivery of quantifiable impact. Clients receive regular performance reports and can scale the solution across additional use cases as needed.
Target Audience
Primary customers are mid‑size to large enterprises and product teams that require AI capabilities but lack in‑house expertise to develop and operate them at scale.
Features
- Custom model development using state‑of‑the‑art machine‑learning techniques aligned with defined business KPIs
- End‑to‑end data engineering pipelines for ingestion, cleaning, labeling, and feature engineering
- Managed MLOps platform with automated training, CI/CD, versioning, and resource scaling
- Real‑time monitoring and drift detection dashboards to maintain model performance post‑deployment
- Integration services for embedding AI outputs into existing enterprise systems and workflows
- Ongoing model maintenance, retraining, and performance reporting as a managed service