Power of Pre-Trained, Fine-Tuned by Machine Learning Development Service.
Our machine learning development services include customizing and deploying open-source and proprietary foundation models to your industry needs, from processing insurance claims to analyzing biotech research, to create production-ready AI that works where it matters.
Foundation Model Engineering, End-to-End Machine Learning Development Services
Our machine learning development services cover all aspects of the foundation model integration lifecycle, from base model selection, domain adaptation, quantization, and production deployment.
Our machine learning development services include adapting Llama, Mistral, Falcon, and other open-source models to your domain over proprietary datasets using supervised fine-tuning and RLHF techniques.
We provide machine learning development services to evaluate and benchmark candidate foundation models to your task requirements before a single dollar is spent on fine-tuning or infrastructure.
We utilize our machine learning development services to implement GPTQ, AWQ, and GGUF quantization for reducing inference costs and latency while keeping meaningful task accuracy in production environments.
We provide pre-trained models for scientific and research purposes through our machine learning development services to summarize literature, generate hypotheses, and gain document intelligence in technical fields.
Using our machine learning development capabilities, we design the APIs, connectors, and workflow integrations needed to integrate customized foundation models within your enterprise systems and workflows.
Our machine learning development services offer deployment of models with full audit logging, PII handling, and data residency controls for regulated industries such as insurance, healthcare, and financial services.
Start With the Right Base Through Machine Learning Development Services. Build the Right System
Not all applications require a frontier model. In fact, choosing the right base model and tweaking it to fit your application better could prove much more valuable than model scale. As part of our machine learning solutions service offering, we assess different base models against your tasks prior to any fine-tuning effort.
Domain adaptation involves the curating of datasets using your own private documents, instruction-tuning the model’s behavior, testing against real-world tasks, and safety alignment relevant to your context.
A US insurance carrier was seeking to minimize the manual effort involved in reviewing and classifying inbound claims documentation. As part of our machine learning development services, we fine-tuned a Llama-based foundation model on five years of their claims data, policy documents, and adjusters’ notes. The resulting system automated first-pass classification and extracted structured fields from unstructured claims text, significantly reducing processing time 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.