On-Demand AI Labs & R&D Services for Enterprise
Validate AI with confidence before you invest. We design and run enterprise AI experiments, model evaluations, architecture benchmarks, and feasibility assessments that replace assumptions with evidence, so you make faster, lower-risk AI decisions.
On-Demand AI Labs
A dedicated experimentation capability you can call on when you need it, without hiring an internal research team.
AI Experimentation
Structured, controlled experiments that test a specific AI approach against your real data and constraints.
AI Research & Development
Evidence-backed recommendations on architecture, models, and feasibility before a single line of production code is written.
Why Enterprise AI Initiatives Fail Before They Start
The problem is not the technology. It is the decision to build before anyone validated whether the technology would actually perform.
Unclear AI Roadmap
Difficulty turning AI ideas into a clear plan.
Limited Internal R&D Capacity
No dedicated team for AI research or experimentation.
Model Selection Uncertainty
Unclear choice between LLMs, SLMs, open, or proprietary models.
Architecture Confusion
Uncertainty around on-prem, private, or cloud setups.
Privacy & Regulatory Constraints
Compliance requirements limit safe experiments.
Early Technology Lock-In Risk
Wrong early choices lead to costly rework.
Unproven Feasibility & ROI
Hard to prove accuracy, performance, and value upfront.
What On-Demand AI Labs & R&D Services Are
GenAI Protos designs and runs structured AI experiments covering model evaluation, architecture benchmarking, use case feasibility testing, LLM evaluation, and proof-of-value validation, across private, edge, and cloud environments, depending on your requirements.
The output of every engagement is not a strategy document. It is tested, benchmarked evidence that tells you whether a specific AI capability works in your environment, on your data, within your operational constraints, before a single line of production code is written.
What We Deliver
Structured experimentation and validation across every major AI decision point, from initial feasibility through to production readiness.
AI Roadmap & Prioritization
Define a practical, experiment-driven AI roadmap aligned to business goals and technical constraints.
Architecture Experimentation & Validation
Evaluate and compare on-prem, private, hybrid, and cloud AI architectures through real tests.
Model Benchmarking & Selection
Benchmark and validate models (LLMs, SLMs, fine-tuned variants) to choose the best fit.
Feasibility Testing & Technical Proofs
Run experiments to prove technical feasibility, performance, and integration viability.
Data Strategy & Synthetic Data Experiments
Test data strategies including real, synthetic, and privacy-preserving approaches for training and testing.
Technical Workshops & Handover Sessions
Hands-on sessions that transfer findings and technical know-how directly to your team.
AI Experiments and Validations We Have Delivered
Clinical Trial Assistant
A fine-tuned Small Language Model that converts unstructured clinical visit notes into protocol-compliant summaries in under one minute, deployed securely on NVIDIA DGX Spark for complete data privacy.
Spark Vault
A secure, on-premises enterprise search solution for medical documents, combining containerized AI models and vector databases for rapid, private searches without cloud dependency.
Synthetic Data Generation Strategy
AI-powered platform generating realistic patient records with medical reports, demographics, images, and professional PDFs at scale for ML training and testing.
AI-Powered Contract Intelligence Platform
See how structured AI experimentation and rapid prototyping accelerated legal document review, cutting turnaround from days to minutes with verified deterministic citations.
Industries We Serve
Healthcare
Healthcare organisations validating AI for clinical documentation, medical record intelligence, diagnostic assistance, and operational workflow automation require experimentation in private environments where patient data never leaves controlled infrastructure.
Financial Services
Financial institutions evaluating AI for document processing, regulatory compliance, fraud detection, and customer service automation require AI experimentation environments where transaction and customer data remains governed.
Legal
Law firms and legal departments evaluating AI for contract review, legal research, e-discovery, and document intelligence require air-gapped experimentation environments where client-privileged data cannot leave controlled infrastructure.
Retail
Retail organisations evaluating AI for demand forecasting, personalisation, and fulfilment automation require experimentation against real transaction and customer data before committing to a full rollout.
Software Engineering
Software engineering organisations evaluating AI for code generation, automated testing, documentation, and developer tooling require AI experimentation environments where proprietary codebase data cannot leave internal infrastructure.
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Ready to validate your AI initiative?
Get evidence-backed answers on feasibility, architecture, and model choice before you commit budget or engineering time.
Our AI Experimentation Methodology
Experiment Design and Success Criteria Definition
We translate your AI initiative question into a precise experimental specification, covering the hypothesis, evaluation metrics, and data environment.
Environment Configuration and Data Preparation
We configure the lab environment appropriate for your experiment, private on-premise, controlled cloud, or edge hardware.
Controlled Experimentation and Benchmarking
Experiments run under controlled conditions with systematic variation of the parameters under evaluation.
Analysis, Interpretation, and Failure Documentation
Raw results are analysed against your defined success criteria, and failure modes are documented, not hidden.
Findings Report and Recommendation
You receive a clear findings report and a recommendation on whether and how to proceed to full build.
Ready for production deployment?
Once your AI initiative is validated, our Full-Stack AI Engineering team takes it into hardened, full-scale production.
Frequently Asked Questions
Everything you need to know about our On-Demand AI Labs & R&D services, timelines, pricing, and experimentation deliverables.
How is an AI lab service different from AI consulting?
What is the difference between AI experimentation and a proof of concept?
Can you run AI experiments in a private or air-gapped environment?
What does a typical AI lab engagement produce?
How long does an on-demand AI lab engagement take?
How is an AI lab engagement priced?
Ready to validate your AI initiative before you build it?
Reduce uncertainty before investing in AI development. Once your AI initiative is validated, our Full-Stack AI Engineering team takes it into production.
