What challenges did Tangram Therapeutics face before AI implementation?

Long timelines and inefficiencies in traditional drug discovery. Need to accelerate and scale drug target discovery and evaluation for RNAi medicine development.

What was the impact of the AI solution?

Document processing that previously took weeks now takes hours. Tangram can assess four to five target-indication propositions in a few hours instead of one quarter, improving speed by up to 50x. The platform processes about 300x more data than before, with roughly 288 million input tokens and 16 million output tokens per day. LLibra OS enabled researchers to explore questions and hidden targets that were previously out of reach.

Source

Tangram transforms drug discovery with agentic multi-LLM AI on AWS

Use case typeDrug discoveryUpdated Jun 13, 2026

Tangram Therapeutics is a UK biotech company focused on solving human disease through computational RNA interference (RNAi) medicines. To accelerate drug target discovery and evaluation, Tangram built LLibra OS, an agentic AI platform that unifies proprietary, licensed, and curated public datasets for research and target-indication assessment. The platform supports retrieval augmented generation, web search, and text-to-SQL to help researchers identify novel targets, evaluate therapeutic potential, and design medicines.

Organization: Tangram Therapeutics
Industry: Pharma
Location: United Kingdom
Published: June 2026

Reported outcomes

50x

target-indication evaluation speedupTime & speed

300xdata processed daily increase288 million tokensdaily input tokens16 million tokensdaily output tokens

Strategic outcomes

Speed & agilityAccelerated target discovery and evaluationScale & capacityUnified large biological datasetsNew product / capabilityEnabled novel target explorationNew product / capabilitySupported target and medicine design

Catalog median for time & speed deployments: +75.8% across 200 reported metrics. Compare benchmarks →

Primary read

Use case focus

Showing 3 of 3

1Drug discovery
2Research and development
3Knowledge discovery

Long timelines and inefficiencies in traditional drug discovery.
Need to accelerate and scale drug target discovery and evaluation for RNAi medicine development.

Built LLibra OS as an agentic AI platform on AWS.
Used Amazon Bedrock for generative AI and agentic orchestration.
Used AWS Glue for data ingestion, preparation, and integration across more than 1,000 biological datasets.
Used Amazon Athena for interactive query and analytics.
Added retrieval augmented generation, web search, text-to-SQL, and an in-house evaluation harness to keep models and components current.

Technologies

Amazon Bedrock AWS Glue Amazon Athena RAG text-to-SQL

Document processing that previously took weeks now takes hours.
Tangram can assess four to five target-indication propositions in a few hours instead of one quarter, improving speed by up to 50x.
The platform processes about 300x more data than before, with roughly 288 million input tokens and 16 million output tokens per day.
LLibra OS enabled researchers to explore questions and hidden targets that were previously out of reach.

Architecture

LLibra OS runs on a serverless AWS architecture and uses Amazon Bedrock for access to and evaluation of LLMs, AWS Glue for discovering, preparing, and integrating data, and Amazon Athena for interactive analytics. The system has an agentic orchestration layer, retrieval augmented generation, web search, text-to-SQL, modular LLM swapping, and an in-house evaluation engine.

Implementation partners1

AWS Professional Services

Sources & evidence1

Groundedness: 4/5Type: Customer StoryPublished: Jun 8, 2026Publisher: AWSEvidence: PrimaryConfidence: High

Primary source

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