Custom-designed sensor kits deployed at your supplier's production site, paired with software that turns raw process signal into reports your CMC team can actually use. Independent. Physically grounded. Verifiable.
Today, what you know about your supplier's process comes through a paper trail — batch records, certificates of analysis, quarterly reports. By the time a deviation surfaces in your CMC package, the reaction is already six weeks in the past.
Every CMC team has read this line in a deviation report. Without process-level data captured at the time of the event, root cause analysis becomes archaeology.
The COA tells you the batch met spec on the sampled aliquot. It doesn't tell you what happened during hours 3 through 7 of the reaction — or whether the next batch will behave the same way.
"The CMO said so" is the highest level of confidence you can currently purchase. For clinical programs where a single failed batch delays an IND filing, that ceiling is too low.
By the time a batch-to-batch variability issue reaches your clinical sample, the affected material has been consumed, the process drift has already completed, and you're investigating a ghost.
Every engagement is custom-designed around the specific chemistry, reactor configuration, and quality-relevant parameters of your product. The deployment typically spans four steps.
I work with your CMC team to build a quality-by-design parameter map specific to your chemistry. For an exothermic reaction with moisture-sensitive intermediates, this might be exotherm profile and moisture; for a high-pressure catalytic step, pressure-drop rate and catalyst load. The outcome is a short list of physical signals that — if measured continuously — would have caught every deviation in your deviation history.
I ship a sensor kit engineered against your parameter map — temperature, pH, mechanical load, pressure, and whatever else your chemistry demands. Each kit uses industrial-grade, non-invasive sensing matched to the specific reactor and process. Physical install is coordinated with your supplier; electrical and data isolation ensure I never touch their control system.
Sensor data is sampled at process-relevant rates and written to a hardware-signed log at the point of measurement. Every record is timestamped and cryptographically chained — so that the evidence is provably the same evidence that was produced at time of reaction, not a reconstruction assembled after the fact.
After every batch, my software translates raw sensor streams into structured reports: batch-to-batch comparison, deviation flagging against your spec, integrity attestation, and narrative summaries written in the language of CMC documentation. If something drifted, you know before the COA arrives — not six weeks after your clinical sample fails spec.
AtomTruth exists because the gap between what pharma manufacturing can measure and what consumer hardware already measures is now almost a decade wide.
At Apple, a 0.1% defect rate meant millions of failed devices. The only way to survive that math was to instrument every supplier process down to the shift and the machine — to treat quality as a measurable, traceable, enforceable signal rather than a trust-based claim. That discipline is what I'm bringing to the APIs and intermediates going into clinical trials.
I'm building this for North American biotechs. If supply-side variability has ever cost your program time, data, or a clinical sample — that's the problem I'd like to hear about.
20-minute conversations. No pitch deck. I come with three specific questions about your current process-evidence workflow, and share what I'm learning across other conversations.