Platform
2.1
Concept Generation
Start from a technical problem and generate candidate solutions tested against both the published record and your own internal knowledge bases. Understand which approaches are genuinely new, which your teams have already tried, and why the ones that failed didn’t work. Use the surviving shortlist to develop a few concepts that stand up to the evidence, rather than repeating an experiment a colleague ran three years ago.
2.2
Reformulation
Take a product that already works and adapt it to a constraint it was never designed for, whether a restricted ingredient, a new market’s regulations, a supply shortage or a lower price point. Understand which substitutions are viable, how each is likely to affect performance, and which have already been tried across your own formulation history and the published literature. Use that to reach a small set of candidate reformulations worth taking to the bench, with the trade-offs understood before the first batch is produced.
2.3
Experiment Design
Bring together results held across ELNs, instrument outputs and historical project reports, and turn them into a single comparable picture of what your teams have already tested. Understand which parameters drive performance, where the data is thin, and which conditions remain untested. Use that to plan the experiments that resolve the most uncertainty per run, and to feed each result back so the next round is more precisely targeted than the last.
2.4
Knowledge Capture
Turn your organisation’s technical knowledge, standards, and ways of working into a shared intelligence layer. Ground every workflow in your internal context: from engineering documents and prior work to preferred methods, terminology, and quality expectations. As teams use Ankar, that knowledge compounds, making outputs more relevant, consistent, and specific to how your organisation innovates.
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