• 01

  AI strategy for core R&D technology

  Custom proprietary integration into wet-lab R&D of biological modality AI, such as molecular design and omics foundation models, can expand the discovery and design space and enhance future partnership potential. With a large and growing landscape of open-source and proprietary models as well as model infrastructure ecosystems, it can be challenging to identify which, if any, solutions are best suited for or can be adapted to a company's specific platform needs and resource constraints. Engagements focus on identifying where and how biological modality AI models could be transformative to R&D efforts, where agentic AI can facilitate their integration and deployment, and establishing a preferred build vs. buy vs. partner strategy for AI transformation and AI-native positioning of core platform technology.

• 02

  Automation and efficiency

  Agentic AI adoption in the life sciences has significant potential for driving R&D productivity and automation, but given both usage costs and the need for quality guarantees companies can struggle with envisioning and prioritizing where agentic workflows could have real and high-quality impact. Agentic workflows can also enhance lab-in-the-loop setups that are increasingly driving faster iterations on discovery and development. Engagements are designed to identify gaps and opportunities in the company's processes that could benefit from agentic workflows and tools and to develop internal product vision and strategy for software-based efficiency and automation layers.

• 03

  Digital asset repurposing for derivative value

  Companies often possess large amounts of underutilized data without realizing that this data could be repurposed toward new model development or fine-tuning of open-source models. Digital asset repurposing can create new internal platform capabilities or open opportunities for partnerships, but the large optionality space makes it challenging to determine data-model-market fits. Identifying which assets and how they could be repurposed toward optimal internal or external-facing value involves a combined technical-commercial approach; engagements apply this lens to uncover opportunities and plan their development.

• 04

  Biopharma market strategy for AI-first companies

  With a rapidly evolving competitive landscape and a crowded capability space, AI-first companies must ensure that their development program and product vision are keenly attuned to biopharma needs and provide clear differentiating value. At Coescent, engineering, product, and partnerships are treated as an intertwined whole. Engagements are designed to pinpoint problems with market-product fit and identify how technical differentiation can be converted into product differentiation to open new market and partnership opportunities. Engagement focus areas are R&D-targeting products and capabilities, including molecular design capabilities, single-cell foundation models, and agentic AI directed at research, discovery, and wet-lab integration.

• 05

  Discovery and exploration

  When companies find themselves at a pivot point, either due to negative R&D signals or a change in the competitive landscape, they may need to rethink their core value proposition and look for pivot options that ensure their continued existence as an attractive investment target. Coescent partners with companies navigating strategic inflection points to identify AI-driven pivots that can reposition the company for continued innovation and differentiation. Engagements combine strategic planning and scientific exploration to surface and pressure test new directions and translate them into strategic priorities.

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