Specialized AI Agents That Run Your Workflows

Agents today operate inside real workflows — querying databases, running Python, calling APIs — yet the models behind them aren't trained on those workflows. They make avoidable mistakes, behave inconsistently, and rack up enormous inference bills. And because teams lack an environment that mirrors their product or a reliable reward signal, there's no predictable way to improve these agents over time.

Why now?

Open-source models have matured to the point where, if trained in the right setting, they can rival frontier-model performance at a fraction of the cost. Meanwhile, agent workloads have become standardized: SQL + Python + retrieval + tools is now common across applied AI products. Combined with modern inference and RL frameworks, teams finally have the ingredients needed to build product-specific models that are cheaper, more accurate, and able to improve continuously.

Our solution

Analytical Engine gives you the missing infrastructure: an RL training and inference platform that recreates your real environment, trains a model directly on your tasks with your SME-defined reward signal, and deploys it on an optimized inference engine. Your agent becomes a specialist — tuned to your product, your tools, and your workflows — and continues to improve as usage grows.

We work with your SME to build a task dataset, synthetic production environment, and reward model that captures what "good" looks like for your workflow. Instead of training on generic benchmarks, the model learns from your real tasks, tools, and evaluation criteria.

How we do it

We benchmark your current agent (or a strong open-source baseline), refine the reward model with your SME, and run reinforcement learning using our asynchronous training system. Once the specialized model is ready, we deploy it through an optimized inference engine tuned for your workflow — enabling lower latency, fewer unnecessary tool calls, and far lower cost per task.

When the agent runs in production, we continuously collect trajectories, surface failure cases, refine rewards, and retrain. Your model gets better every month.

Why it's better

Our platform closes the gap between expensive frontier models and cheap but inaccurate open-source models by training them directly in your environment. For example, with TextQL's Ana, we reduced their LLM spend from ~$50k/month to ~$25k/month while improving performance on their most costly workflow. No fine-tuning API or inference provider offers environment-specific RL, SME-aligned reward modeling, and continual improvement in a single system.

Your agents become more accurate, more reliable, and dramatically more cost-efficient — with a training loop that compounds value over time.

Analytical Engine was founded by Computer Science students from the University of Waterloo who have spent years working on long-horizon agents, reinforcement learning, and production AI systems. We're a small, deeply technical team focused on one mission: building agents you can actually trust in real enterprise environments.