Fine-Tuning Domains
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.
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.
5x
Faster Processing
50%
Onboarding Time Reduced
30+
Foundation Model Deployments
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.
Model Selection & Benchmark
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.
Optimization and Quantization
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.
Research Acceleration
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.
Enterprise Integration
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.
Compliance-Ready Deployment
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.
Our Engineering Approach
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.
Featured Engagement
Infotech
5X
Faster Deployment
40%
Efficiency Gain
It was a hard slog for Infotech engineers to work thru years of internal documentation and they would send repetitive questions to senior staff. In our enterprise AI model deployment process, we did foundation model fine-tuning over their entire knowledge base and integrated with their ticketing system. New engineers were up to speed in days. Senior staff weren’t pulled into unnecessarily escalated issues.
R
Rebecca L.
VP of Engineering, Infotech
Featured Engagement
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.
Technology · United States
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.
3.5→2wk
Onboarding Time Cut
4
Internal Tools Indexed
Small Business · United States
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.
Reduced
Legal Review Cost
Reduced
Vendor Turnaround
E-Commerce · United States
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.
30 days
To Response Parity
Equalized
Satisfaction Scores
Local Services · United States
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.
30%→~100%
Response Rate
<24hrs
Avg. Response Time