We design, evaluate, and deploy reliable AI systems for drug discovery, clinical research, and regulatory intelligence. From evidence-grounded AI agents to molecular models, our solutions prioritize measurable performance, scientific rigor, and human oversight.
Four kinds of engagement, each grounded in systems we have built.
Search and question answering over journal articles, patents, lab reports and ELNs that reads chemical structures, reaction schemes, tables and figures, not just the text.
Typical work: Literature copilots, prior-art search, internal R&D knowledge bases
ADMET and physicochemical property models built on SMILES and molecular graphs, using fine-tuned chemical language models and graph neural networks.
Typical work: Virtual screening, lead-optimisation triage, model reviews
Agent systems that search ClinicalTrials.gov, FDA labels and guidance documents, compare protocols, and draft summaries where every claim links to its source.
Typical work: Trial landscape analysis, eligibility comparison, regulatory intelligence
From prototype to production on AWS or GCP, with evaluation pipelines, guardrails, caching, monitoring and documentation your quality team can review.
Typical work: Architecture reviews, RAG hardening, cloud deployment
Systems we have built and are building. Each one is a reusable component for client work.
Multi-agent clinical trial intelligence engine
Six specialist agents search and reason over ClinicalTrials.gov, with three-tier memory and a human-in-the-loop learning loop that improves answers from reviewer feedback. Runs on Google Cloud.
Hybrid dense and sparse retrieval with reranking, corrective RAG with critic and verifier steps, a nine-layer guardrail pipeline and human approval for generated SQL. Deployed on AWS.
RAGAS evaluation scores
Chemistry-aware PDF ingestion. Nougat parses scientific papers, DECIMER converts drawn structures to SMILES, and RxnScribe turns reaction schemes into structured data.
Multi-task ADMET prediction from SMILES using a ChemBERTa-2 chemical language model fine-tuned with LoRA, served through FastAPI on AWS ECS Fargate.
Property prediction directly on molecular graphs with AttentiveFP and Graph Transformer models in PyTorch Geometric.
From biological target to synthesizable candidate molecules in three phases, built on the components above.
Regulated science needs systems you can audit. The process is built around that.
We start from your documents, assays or trial questions, and agree what a correct answer looks like before choosing any model.
Each release is measured on a fixed test set (faithfulness, retrieval precision, prediction error) and blocked if it regresses.
High-stakes steps, such as generated database queries or regulatory summaries, go to a reviewer before they take effect.
Containerised deployment in your own cloud, with monitoring, runbooks and documentation your team owns.
Notes on building AI systems that hold up in production.
Enterprise RAG series · Part 1 of 5
What it took beyond the demo: structured and unstructured data, safety controls, evidence, cost and repeatable quality measurement.
Enterprise RAG series · Part 2 of 5
How one question-answering system routes between document retrieval, SQL and a hybrid of both.
Enterprise RAG series · Part 3 of 5
Sentence-aware chunking, hybrid search and reranking, and why wrong evidence often starts well before the reranker.
Enterprise RAG series · Part 4 of 5
Source precision, source recall and sealed holdouts, for answers that look right but rest on the wrong evidence.
Enterprise RAG series · Part 5 of 5
Why retrieving relevant passages is not the same as having the evidence to answer the whole question.
Venu Thottempudi is an AI solutions architect with more than twelve years in enterprise software engineering. He builds production retrieval, agent and molecular AI systems and deploys them on AWS and Google Cloud.
AIScientificLabs brings that engineering discipline to life-science teams that need AI they can measure, review and trust.
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