Data and AI where who can see what matters as much as the answer
We have not published a healthcare case study yet, and we will not borrow one from another industry. What we can show is how we work where access, audit and accuracy are not optional: access rules on the data rather than the dashboard, a graded evaluation before an AI feature ships, and a record of every model call.
- Published case study in this industry
- None yet
- Your data and model training
- Never used to train a model
- Median to first production artefact
- 6 weeks
- Team on a build
- Pod of three, no bench
On this pageWhat we hear in healthcare and life sciences
What we hear in healthcare and life sciences
Each of these comes from an article we wrote for teams in healthcare and life sciences.
- 01Two people open the patient dashboard and see two different numbers
A clinician sees her own unit and a regional analyst sees her sites. That is a real control, until a notebook or an export reaches the same data without the dashboard's filter.
- 02Where do the backups go?
The database is in the right region. The backups, the logs and the vendor's support tooling may not be.
- 03An assistant answered confidently and wrongly
Look at retrieval first. Prompt revisions and a model upgrade will not fix a system that never put the right passage in front of the model.
- 04Two reviewers grade the same answer differently
That is not noise to average away. It is the clearest evidence that nobody wrote down what a good answer looks like.
- 05Nobody wrote down what the AI must refuse
Acceptance reviews argue about how good the answers are. The shorter, harder list is what the feature must refuse, and that is the one a reviewer asks to see.
- 06A batch has to be traced within the hour
For life sciences manufacturing, whether a recall takes an hour or a week was settled by how consumption was recorded.
What we can show you
What we have delivered
We have not published a case study in this industry, so these are firm-wide delivery figures. We will say on the call which of them came from work like yours.
Across India, APAC, the UK, Europe and the US, from Pune.
Fixed scope and fixed price, measured across all of our engagements.
With your own data in it, not a slide about it.
The people on the call are the people doing the work.
Read the full accounts
- Our stanceYour data is not used to train models
Not ours, not anyone else's. A person is accountable for every deliverable, and AI is disclosed wherever it is used.
How we use AI ↗ - Life sciences manufacturingUloborus suits pharma ancillaries
Our manufacturing system is built for batch plants, including pharma ancillaries and their contract manufacturers.
See Uloborus ↗
Where to start
Four places a first engagement can begin. Week 1 tells you which one comes first.
- AIProve the AI works before anyone relies on it
Graded eval sets, not a demo and an opinion.
AI evaluation ↗ - AIControls a reviewer can inspect
Defensible in a board meeting, auditable on demand.
AI governance ↗ - ProductAccess rules on the data, not the dashboard
Antvia enforces column-level access control and PII masking on every query, in a lakehouse deployed in your own cloud.
See Antvia ↗ - ApplicationsApprovals with a trail
The process people actually follow, moved off paper and shared spreadsheets, with an audit trail on every approval.
Process digitisation ↗
How the first weeks run
The same three phases in every industry. What changes is what we build in them.
- Week 1, fixed feeAudit
Two calls and a written one-pager. Yours to keep whether or not you continue.
- Weeks 2 to 6Build
Something real in front of real users by week three. Median six weeks to the first production-grade artefact.
- Week 7 onwardOperate
Quarterly reviews and on-call governance. The people who built it pick up the phone.
Questions we get asked
Further reading
- Access rules that live in the BI tool are not access rulesA row filter in a dashboard protects the dashboard, and nothing else.
- When a RAG system answers confidently and wrongly, look at retrieval firstA model upgrade will not fix a system that never found the right passage.
- Write down what the AI feature is not allowed to do before it shipsThe shorter, harder list is the one a reviewer asks to see.
Start with the Week 1 audit
Two calls and a written one-pager naming the first thing worth building, and who owns it. You keep it either way. NDA-friendly, fixed scope. Write to hello@woodfrog.tech.
