AI Experimentation & R&D
Choose your next AI approach with evidence from hands-on experiments. GenAI Protos provides generative AI consulting services focused on evaluating models, exploring tools, and comparing technical options against your tasks and data. Get practical findings to guide AI model selection and your next development decision.
How We Help
Our applied AI R&D services help you evaluate technologies, compare approaches, and build practical know-how through focused engineering experiments.
Evaluate AI Tools and Platforms
Test selected platforms and frameworks against your workflows, integration needs, and technical constraints.
Compare AI Models
Our AI model evaluation services compare quality, response time, and cost using your tasks and data, including LLM evaluation for language-based workflows.
Experiment with RAG and AI Agents
Use RAG evaluation and AI agent evaluation to assess retrieval quality, outputs, tool use, and task completion against defined business workflows.
Test Technical Feasibility
Use an AI feasibility assessment and small proofs of concept to answer specific questions about capabilities, integrations, or deployment approaches.
Build Your Team’s AI Know-How
Learn through shared experiments, technical walkthroughs, and reusable code your engineers can build on.
Access Ongoing AI R&D Support
Move research priorities forward with agreed engineering capacity, regular reviews, and documented findings.
Benefits of Working With Us
Reach Technology Decisions Faster
Use focused experiments to compare options and resolve technical questions that slow your AI plans.
Access Specialist Expertise
Work with engineers who help your team explore unfamiliar models, tools, and approaches through practical testing.
Control Your R&D Spend
Keep research focused on agreed questions, deliverables, and budgets before committing to platforms or larger builds.
Get Evidence Your Team Can Use
Receive documented comparisons, limitations, and reusable experiments to support informed decisions and continued learning.
What You Get
Technology Exploration
Explore selected platforms, tools, or frameworks through small working examples. Test the capabilities that matter for your use case and document integration requirements, dependencies, and limitations.
Deliverables:
- Technical questions and an experiment plan.
- Working proofs of concept or experiment assets.
- Setup notes and integration findings.
- Findings report and recommended next steps.
Model and Approach Comparisons
Compare selected models or solution approaches using consistent tasks and representative data. For language-model workflows, LLM benchmarking can assess answer quality, task accuracy, response time, cost, and deployment requirements.
Deliverables:
- Comparison criteria, baseline, and test cases.
- Evaluation results under documented conditions.
- Working examples and observed failure patterns.
- Tradeoffs and recommendations for further testing.
Practical Knowledge Transfer
Review the experiments with the engineers who performed them. Receive agreed assets so your team can understand the setup, interpret the results, and repeat or extend the work.
Deliverables:
- Technical walkthroughs and review sessions.
- Code and configurations specified in the agreement.
- Test cases and setup documentation.
- A prioritized list of recommended next experiments.
Scope and costs
We agree on the research questions, engineering capacity, deliverables, milestones, and budget before starting. Model usage, platform subscriptions, cloud resources, and specialist infrastructure are identified separately.
Changes to active work, specialist requirements, or delivery dates are discussed before they affect the engagement.
Common Questions About AI Experimentation and R&D
We compare selected AI models, platforms, and approaches against your workflows and technical constraints. You receive practical findings on output quality, response time, cost, and integration needs to support your decision.
Yes, we use representative examples from your business and consistent evaluation criteria to compare the selected options. We document results, limitations, and failure cases so your team can assess performance on the tasks that matter.
Yes, our focused engineering team can investigate agreed technical questions while your engineers continue their delivery work. Regular reviews, documented findings, and reusable experiments help your team benefit from the research without managing every experiment.
Yes, we test retrieval-augmented generation (RAG) applications and AI agent workflows around defined business tasks. We evaluate retrieval quality, outputs, integrations, and task completion to identify what works and where improvements are needed.
Yes, we can begin with a focused AI R&D sprint covering a small set of questions and deliverables. At review points, we discuss the findings and agree on changes to priorities, scope, or subsequent experiments.
Related services
Explore adjacent AI capabilities tailored to solve your technical and operational challenges.
What decision do you need to make?
Bring the model, platform, or technical question you want to explore. We will help define an experiment that produces useful evidence.
Let's Explore Your Idea
Tell us what you want to build, test, or improve. You don't need a detailed brief to get started.