Every other industry can ship an automation and watch what happens. You can't. The systems are old, the data volumes are enormous, and anything automated has to be explainable to someone who reviews it eighteen months later without the context you had at the time. That constraint shapes everything we build for life sciences clients.
Decades of commercial, medical, and field data spread across systems that were never meant to talk. The technical move is the easy half; producing the evidence that the move was complete and faithful is what actually holds the project up. We design the reconciliation and the evidence pack as part of the migration, not as a clean-up phase afterwards.
Protocols, submissions, SOPs, correspondence, scanned legacy records. The information exists but nobody can find it fast enough to act on. We build retrieval that returns a cited answer with its source page attached, so a reviewer verifies rather than trusts.
Field teams, medical affairs, and commercial each carry their own version of the account, the HCP, and the interaction history. Data Cloud and a properly governed object model turn that into one addressable profile — which is also the precondition for any AI worth trusting.
Intake triage, case handling, data entry between systems, routine correspondence. Agentic automation handles the volume; human checkpoints stay exactly where the cost of being wrong is real. Everything the agent does is logged, replayable, and attributable.
Industry reporting through 2026 puts production agent accuracy in the low-to-mid nineties. That is genuinely useful for drafting, triage and summarisation. It is nowhere near the threshold for an unsupervised decision that ends up in a submission or an audit file — and the gap between those two facts is where most enterprise AI programmes quietly fail.
Test sets built from your real cases, scored against a defined standard, with the number written down before anything reaches a user. If we can't measure it, we don't ship it.
Hybrid reasoning keeps business rules as business rules. The model handles genuine ambiguity; calculations, eligibility and routing stay in code that behaves identically every time and can be read by an auditor.
Human review is placed where the cost of being wrong is real, not sprinkled everywhere. Every automated action is logged, replayable and attributable to a version of the system.
We don't hand your quality organisation a finished system and ask them to make it compliant. Traceability, documentation, and test evidence are produced as the work happens, in the format your QA team already uses — because retrofitting them is where these projects die.
Joan Software is a young studio built by engineers with long enterprise careers behind them. We are not a Salesforce partner and we don't resell licences — we sell architecture and code. Our pharma products are in active development and we'll tell you plainly which parts are production-ready and which are not. If that candour is useful to you, we're probably a good fit.