Put frontier models to work on your data, in your environment, with Aurelis adaptation and deployment services.
Use your proprietary corpus to turn a general-purpose model into a specialised system for your domain, trained and evaluated with the Aurelis applied team.
Reach measurably better accuracy with continued pre-training on the language, formats and edge cases of your sector.
Hold performance while cutting model size by two to three times using our distillation and quantisation pipeline.
Serve billions of requests a week more efficiently, or tune for edge inference and real-time interfaces.
Run models on the infrastructure you already trust, keep full control of weights, and hold continuous operations for business-critical work.
Aurelis Compact story
We adapted Aurelis Compact (19B parameters) to code tasks on a customer's private repository, with a marked lift on review workloads:
Measured outcomes
Organisations that have co-trained models with the Aurelis applied team report:
From finding the next use case through to model creation and deployment support, our applied team stays with you the whole way.
We help you set success criteria for adoption and build the first use cases around your organisation, business goals and data platforms.
We help you build adapted models fitted to specific business goals, using the data only you have.
We help you run Aurelis models anywhere: managed deployment on major clouds, private VPC, self-hosted clusters and edge devices.
We are opening up the practice of building world-class models and running hyper-scale AI infrastructure. Our science, tooling and applied methods travel with you, from GPU allocation to interfaces, so you can hold your AI systems in house.
Deployable on public cloud, private cloud or your own premises, with support from our field engineers.
from aurelis.client import Aurelis from aurelis.schema import Turn key = os.environ["AURELIS_KEY"] model = "aurelis-compact" client = Aurelis(api_key=key) turns = [ Turn(role="user", text="Draft the incident timeline")]
For large-scale internal use cases such as company assistants and coding copilots, shipped ready to configure.
Anything on my plate today?
Three meetings and five reminders across your calendar and the team channel — the vendor review moved to 15:30.
Professional services from adaptation through to co-training, for the use cases nobody else can solve off the shelf.
Working with the Aurelis applied team, Rivenbank moved a decade of advisory research into an adapted model, cutting the time to a client-ready brief from two days to under an hour.
Portline trained a logistics-specific model on twelve years of routing and customs records, now running inside their own data centres across three regions.