Agentic Solutions
Our services

Designing & building LLM-powered experiences
Our teams help you discover and deploy AI capabilities quickly and efficiently by leveraging the right technology to meet your business needs. We specialize in designing and building experiences for large language models (LLMs).
See how we can help
Ensuring model consistency
While it is often difficult to manage the inherent probabilistic nature of LLMs, our teams have deep experience applying a variety of foundational techniques to manage around this inconsistency and increase model performance in an effort to reduce latency, improve accuracy, and reduce costs.
See how we can help
Evaluating LLM performance
The lack of evaluations has been a key challenge for deploying AI models to production. By treating model evaluations as unit tests for LLM and pairing prompting with evaluations, our experts can transform ambiguous dialogues into quantifiable experiments, making AI a more manageable software product.
See how we can helpFoundations
Learn the ways in which AI is creating Smart Experiences in digital products today.

We built Nike’s first AI PoC in 3 weeks
Learn howRelated expertise

Agentic Product Engineering
Agentic Product Engineering
AI-augmented engineering workflows that ship production systems in weeks, not quarters.
See how we help
New Product Development
Product Development
Launch new products with a traction-first approach to discovery, delivery, and growth.
See how we helpRelated case studies
Three weeks to launch a scalable AI-powered marketplace solution with secure governance
In just three weeks, we built and deployed a secure human-in-the-loop matchmaking system for a service marketplace platform. Using generative AI and an open-source AI governance platform, the solution minimizes operational costs, accelerates lead response times, and scales without increasing headcount, with oversight, traceability, and control at its core.
Case Study
Babbly
An AI-enabled demo from concept to functional model in just 2 weeks
Babbly's founder needed a machine learning algorithm in a few weeks to close a pre-seed investment round.
Case Study



