R&D teams spend enormous effort on the work around discovery — reviewing literature, searching prior art, synthesizing data, documenting findings. AI can compress that, freeing researchers for the actual science, if it’s used with scientific rigor about verification. Here’s how, and how dgm implements it. (dgm implements osFoundry, a separate company’s platform — we are not osFoundry.)

What AI actually does for R&D teams

The honest framing: AI accelerates the search and synthesis that surrounds research — reading and distilling large bodies of literature and data — so researchers spend more time experimenting, reasoning, and deciding. It speeds the cycle; researchers own the conclusions.

High-value use cases

  • Literature and prior-art synthesis — distilling large bodies of research into what’s relevant.
  • Experiment data analysis — helping analyze and surface patterns in experimental data.
  • Knowledge capture — preserving institutional knowledge so it isn’t lost when people move on.
  • Documentation — drafting reports and write-ups from research notes and data.

The pattern: high-volume reading, synthesis, and documentation that surrounds the actual research.

The non-negotiable: scientific verification

Research carries a hard requirement: verification. AI can hallucinate — including fabricating citations and overstating findings — so researchers must verify every claim and source, and AI must never be the final authority on a scientific conclusion. Used as an accelerant for search and synthesis with rigorous human verification, AI is genuinely valuable in R&D; used unchecked, it’s dangerous in a context where accuracy is everything.

A funding note

If your R&D itself involves building or customizing novel AI or technology, that work may generate qualifying R&D for the federal R&D tax credit and could fit grants like SBIR/STTR. That’s distinct from using AI to assist R&D, but worth knowing if you’re an R&D-driven company.

How to start

Pick one high-volume task — literature synthesis is a common first win — and implement it well, with verification built into the workflow. Prove the time saved, then expand. dgm’s assessment finds the right starting point.

How dgm helps

dgm implements osFoundry and other AI for US R&D teams — connecting it to your research sources and data, building synthesis and documentation workflows with verification, and training your team. Pricing is fixed and public: a $399 assessment and $3,999/month implementation, with no per-seat fees. If you’d rather explore the platform first, go straight to osFoundry; if you want R&D AI done right, that’s where dgm comes in.