Pre-Trained Power.
Fine-Tuned for Your Domain.
We adapt and deploy open-source and proprietary foundation models to your specific industry needs, from insurance claims processing to biotech research analysis, delivering production-ready AI that performs where it matters.
Foundation Model Engineering, End to End
From selecting the right base model to domain adaptation, quantization, and production deployment: we manage the full lifecycle of foundation model integration.
We adapt Llama, Mistral, Falcon, and other open-source models to your domain using supervised fine-tuning and RLHF techniques on proprietary datasets.
We evaluate and benchmark candidate foundation models against your task requirements before a single dollar is spent on fine-tuning or infrastructure.
We apply GPTQ, AWQ, and GGUF quantization to reduce inference costs and latency without sacrificing meaningful task accuracy in production environments.
We deploy pre-trained models for scientific and research tasks: literature summarization, hypothesis generation, and document intelligence across technical domains.
We build the APIs, connectors, and workflow integrations that embed fine-tuned foundation models into your existing enterprise systems and business processes.
For regulated industries including insurance, healthcare, and finance, we deploy models with full audit logging, PII handling, and data residency controls.
Start With the Right Base. Build the Right System.
Not every use case needs a frontier model. Selecting the right foundation model for a task, and adapting it precisely to your domain, is often more impactful than raw model scale. We evaluate base models against your specific tasks before any fine-tuning begins.
Our domain adaptation process includes dataset curation from your proprietary documents, instruction-tuning to shape model behavior, evaluation on real task samples, and safety alignment appropriate to your deployment context.
A US-based insurance carrier needed to reduce the manual effort involved in reviewing and classifying inbound claims documentation. We fine-tuned a Llama-based foundation model on five years of their claims data, policy documents, and adjuster notes. The resulting system automated first-pass classification and extracted structured fields from unstructured claims text, reducing processing time significantly and improving consistency across the claims team.
Foundation Models Delivering
Measurable Impact.
Real deployments across technology startups, small businesses, e-commerce, and local services where foundation model fine-tuning solved specific, practical problems without enterprise-scale budgets.
The Onboarding Bottleneck: How We Cut Engineer Onboarding Time in Half for a US Tech Startup
A fast-growing US tech startup was losing senior engineer hours to repetitive onboarding questions. We fine-tuned a foundation model on their internal documentation so new hires could get answers directly, without pulling anyone away from their work.
The Legal Review Bottleneck: Reducing Attorney Review Time for a US Small Business Owner
A US small business owner was sending every vendor contract to an external attorney for full review. We fine-tuned a foundation model to flag common risk clauses automatically, cutting the volume of billable review time required.
The Language Barrier: Matching English and Spanish Support Response Times for a US E-Commerce Brand
A US e-commerce brand with a growing Spanish-speaking customer base was seeing response times three times longer for Spanish queries. We fine-tuned a foundation model on their full support history to close the gap without adding headcount.
Local Services · United States The Review Response Grind: Taking a US Local Services Business from 30% to Near 100% Response Rate
A US local services business with multiple locations was responding to fewer than 30 percent of its weekly reviews. We fine-tuned a lightweight model on their review history and brand voice to draft personalized responses at scale.