Services

Model development is the heart of applied AI/ML. With experience training hundreds of models in both industry and lab settings, Entropic provides model R&D leading to problem-tailored solutions. We will curate model architectures, external corpora, and internal datasets based on the business objective at hand, and we will iterate until we're confident the limits of performance have been hit.

Although Entropic is excited to train state-of-the-art models for your needs, many problems can be effectively solved with approaches leveraging pretrained models or commercially-available APIs. Therefore, we commit to advise you when bespoke model development is not cost-effective. In such cases, we will work with you to assess and implement alternative solutions involving API-based inference, finetuning approaches, few-shot learning, or other suitable method.

Many AI/ML problem settings require frequent model updates for inference to remain performant. Entropic constructs automated training pipelines for clients when economies of scale favor doing so. By systematizing the processes of dataset construction, model retraining, and deployment, we curtail the busywork and potential for error involved in manually refreshing models.

Entropic will also collaborate with you to determine the best model serving approach. We will help you make pivotal decisions such as server-side vs. client-side inference, commercial cloud vs. private infrastructure, and choice of model serving stack. Our experiential knowledge of the hardware capabilities and network architectures available on most major cloud platforms will protect you from common pitfalls and sources of cost overruns.

We believe the lines between backend and data engineering are rapidly blurring. As latency constraints tighten, and as hardware accelerators become increasingly crucial to run even everyday models, we're confident that an integrated approach to data engineering and backend architecture will best position our clients to support their AI/ML footprint.

Entropic can wrangle most common databases and data warehouses, including Databricks, Snowflake, and most traditional SQL databases. However, we've found that colocation of data and backend compute is often indispensable for low-latency inference, especially for use cases that require alternating phases of retrieval and model evaluation. Therefore, we also deploy embedded solutions (usually via RocksDB and/or LevelDB), with critical-path backend logic via C++ and pybind.

As needed, we will design and implement both internal and public-facing APIs, with choice of REST over HTTP, gRPC, Thrift, or other protocol based on your use case's precise requirements.

Although an effective O&M plan is crucial to any technical product, O&M considerations are frequently overlooked in AI/ML systems. Entropic's frontend development practice delivers comprehensive and accessible  browser-based model instrumentation—a major determinant of an AI/ML system's longevity.

Entropic develops monitoring tools for engineers and non-engineering operations personnel, supporting AI/ML systems at million-user scale. We bring years of experience working with common React tech stacks such as Next.js. As a result, the dashboards, experiment monitors, and model inspectors we build are as modern as the AI/ML systems to be instrumented. A focus on modularity ensures that our instrumentation tools can be flexibly extended within your stack.

Although we typically build frontend deliverables in the context of larger AI/ML development efforts, we also take on discrete frontend projects in appropriate circumstances.