UniFlow provides an AI orchestration platform that reads and maps an organization’s workflows, then automatically selects and routes tasks to the most suitable large language models, classical solvers, or hybrid pipelines. Its Mozart AI engine continuously analyzes events, optimizes compute usage for cost, latency, and accuracy, and can auto‑generate solution blueprints such as smart list building or churn‑rate optimization, delivering up to 40% faster task completion.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises often rely on fragmented scripts, manual handoffs, and disparate AI models to automate workflows, leading to high latency, elevated costs, and suboptimal task routing. This complexity makes it difficult to achieve consistent performance and measurable ROI across business processes such as payroll, sales pipelines, and customer service.
Solution
UniFlow’s Mozart AI acts as an orchestration layer that first analyzes an organization’s live workflow map—ingesting events, logs, and data from systems like Xero. Based on this analysis, Mozart automatically selects the most appropriate solver, whether a large language model, specialized model, classical optimizer, or hybrid/quantum pipeline, optimizing for cost, latency, and accuracy. The platform then routes tasks to the chosen compute resources, continuously learning from outcomes to refine prompts, model choices, and routing rules. By providing a single API that abstracts all solvers, UniFlow enables enterprises to reduce AI spend, accelerate task completion, and improve key business metrics without rebuilding their existing tech stack.
Target Audience
Primary customers are mid‑to‑large enterprises that run complex, data‑driven workflows in finance, sales, customer support, and operations, and that need to orchestrate multiple AI models and solvers efficiently.
Features
- Live workflow ingestion that builds a dynamic map of events, logs, and data flows across connected SaaS tools
- Automatic model and solver selection across LLMs, SLMs, classical optimizers, and hybrid pipelines based on cost, latency, and accuracy targets
- Continuous performance tuning that adapts prompts, routing policies, and resource allocation to maximize ROI
- Unified API surface that standardizes calls to any AI or optimization engine, eliminating the need for custom integrations
- Real‑time monitoring of cost, latency, and business KPIs per workflow, with reporting for finance and operations teams
- Pre‑built library of enterprise‑ready agents and industry‑specific workflow templates for rapid deployment