Service
Architecture review before implementation
Implementation scope and rollout planning
Clear next-step recommendation
Transform your proprietary biological data into a proprietary AI advantage with custom foundation model development.
Generic models like ESM and AlphaFold are trained on public data, offering limited competitive edge. We architect and implement custom foundation models fine-tuned on your exclusive datasets—proteomics, genomics, assay results—to deliver domain-specific accuracy and novel insights your competitors cannot replicate.
Move from using AI to building defensible, proprietary AI assets that accelerate your R&D timeline and create new IP moats.
Our approach bridges the gap between computational promise and wet-lab validation. We ensure your model integrates seamlessly with downstream AI-Driven Drug Discovery platforms and AI-Powered Lab Automation Systems, creating a closed-loop R&D engine. Explore our related service on Computational Biology Model Fine-tuning for specialized adaptation to your organism or disease context.
We translate strategic vision into production-ready, proprietary AI assets. Our consulting delivers concrete, high-impact outcomes that accelerate your R&D timeline and secure a competitive moat.
A production-ready model (e.g., ESM, AlphaFold) fine-tuned on your exclusive biological datasets. This asset delivers higher accuracy on your specific targets than any public model, directly accelerating internal discovery pipelines.
Lab-validated model outputs for your priority use case, such as protein-ligand binding affinity or novel enzyme function. We deliver benchmark reports against public baselines, providing the evidence needed for internal investment and regulatory submissions.
A fully deployed model instance within your controlled, compliant infrastructure—whether a private cloud, on-premises cluster, or air-gapped environment. This ensures IP protection and meets data residency requirements under frameworks like the EU AI Act.
A phased approach to developing a competitive, proprietary biological foundation model, from initial assessment to production deployment and ongoing support.
| Phase & Key Deliverables | Discovery & Strategy (Weeks 1-4) | Architecture & Data (Weeks 5-12) | Model Development (Weeks 13-24) | Deployment & MLOps (Weeks 25-30) | Ongoing Support & Evolution |
|---|---|---|---|---|---|
Strategic Roadmap & Model Selection | Quarterly Reviews | ||||
Proprietary Data Audit & Pipeline Design | Pipeline Optimization | ||||
Custom Architecture (e.g., GNN, Multimodal) | Architecture Updates | ||||
Model Training/Fine-tuning (e.g., ESM, AlphaFold) | Incremental Training | ||||
Validation & Benchmarking Against Baselines | Continuous Monitoring | ||||
Production MLOps & Inference API Deployment | Managed 99.9% Uptime SLA | ||||
Team Knowledge Transfer & Documentation | Priority Support | ||||
Estimated Timeline | 4 weeks | 8 weeks | 12 weeks | 6 weeks | Ongoing |
Primary Inference Systems Expertise | Strategy & Compliance | Data & Architecture | Training & Validation | Deployment & Security | Evolution & Support |
Our Bio-AI foundation model consulting delivers lab-validated outcomes for R&D teams. We translate proprietary biological data into competitive advantage with models fine-tuned for your specific domain.
Accelerate small molecule and biologic lead identification by fine-tuning foundation models like ESM and AlphaFold on proprietary compound libraries and assay data. Achieve higher hit rates and reduce preclinical timelines.
Learn more about our end-to-end approach in AI-Driven Drug Discovery Platform Development.
Engineer novel enzymes and optimize metabolic pathways. We implement generative AI workflows to design variants with improved catalytic activity, stability, and substrate specificity for sustainable manufacturing.
Explore our specialized service for Generative AI for Enzyme Engineering.
Develop predictive models for patient stratification and treatment response by integrating multi-omic data with clinical records. Build AI systems that identify novel biomarkers and enable personalized therapeutic strategies.
Apply multimodal AI to genomic, phenotypic, and environmental data for trait prediction, stress resistance, and yield optimization. Fine-tune models for specific crops to accelerate precision breeding programs.
Optimize guide RNA design and predict off-target effects with AI. Integrate high-content screening data to build predictive models that massively accelerate functional genomics and therapeutic development workflows.
Ensure your AI/ML models meet stringent FDA, EMA, and ISO 13485 standards for diagnostics and clinical decision support. We implement robust validation, documentation, and MLOps pipelines for market approval.
Our dedicated service for Bio-AI Regulatory Compliance and Validation provides full lifecycle support.
Enabling Efficiency, Speed & Accuracy
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Answers to common questions about our strategic consulting and technical implementation services for proprietary biological foundation models.
Our engagements follow a structured 4-phase methodology: 1. Discovery & Data Audit (1-2 weeks) to assess your proprietary datasets and define success metrics. 2. Architecture & Model Selection (1 week) where we design the training pipeline and select the optimal base model (e.g., ESM, AlphaFold). 3. Implementation & Fine-tuning (2-4 weeks) involving secure data processing, distributed training, and rigorous validation. 4. Deployment & MLOps (1-2 weeks) to integrate the model into your research pipeline with monitoring. We deliver a detailed project plan and weekly technical syncs.
About the author
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
The first call is a practical review of your use case and the right next step.
