Abkarino

Abkarino is a domain-specialised, text-only AI foundation model for computational chemistry. It supports day-to-day work in organic, inorganic/non-organic, and physical chemistry while natively integrating regulatory and compliance reasoning. Target use cases include reaction troubleshooting, solvent and catalyst optimisation, greener process design, and early detection of regulatory risk.

Innovation is built into the design. Abkarino performs literature-grounded reasoning that links mechanisms, kinetics, thermodynamics, and safety guidance without relying on multimodal inputs. It is jointly tuned for scientific validity and compliance, so outputs explain both why a step is plausible and whether it risks non-compliance. Generation is guided by green-chemistry heuristics (“greener-by-design” decoding). Recommendations come with transparent, auditable rationales backed by built-in validators such as atom/charge balance checks and incompatible-reagent flags. The training strategy is sample-efficient, drawing strength from a focused, high-quality corpus.

Abkarino enables capabilities suited to a highly regulated sector: faster resolution of reaction failures, improved catalyst/solvent selection, greener process pathways, and early compliance flagging. Together, these outcomes accelerate R&D while reducing operational and regulatory risk.