Improving CMC productivity with automation and AI.
Most of what a biologics program loses is not lost to science. It is lost to assembling, transcribing and re-proving things the organization already knows. This is what the technology can genuinely do about that, why so many attempts stall before they reach production, and what to require of anyone who tells you otherwise.
“AI” is four different purchases
The word covers at least four families of technology with different failure modes, different data requirements and different validation stories. Treating them as one product is the first mistake, because the diligence question is not the same for any two of them.
Language models
Read and write. They are good at turning a corpus of protocols, reports and SOPs into an answer with a citation, and at drafting a document from structured data. They are not calculators, and anything numerical they assert has to come from a tool that computed it.
Machine learning
Learns a relationship from data without being told the form of it. Useful where you have runs and want to know what actually drives a response. It needs enough runs to be worth trusting, and it will not tell you why.
Mechanistic and hybrid models
Physics and kinetics: mass transfer, oxygen supply, growth and consumption. They extrapolate where a purely statistical model cannot, which is exactly what a change of scale demands. Hybrid models fit the residual with data.
Vision
Turns what an instrument or a camera produces into structured data: cell morphology and confluence, a plate image, a reading off a display, a signed paper record. Narrow, reliable, and straightforward to qualify against a known answer.
Generative models sit across the top of these rather than beside them. In a CMC setting the useful generative work is not open-ended creation: it is producing a protocol in the shape your organization approves, a report from data that already exists, or a knowledge graph over a molecule's history. The generation is bounded by a record, and that boundary is what makes the output reviewable.
What it means for you
When a vendor says AI, ask which of the four they mean, then ask what happens to the answer when the underlying data is thin. The families fail differently, and you are buying the failure mode as much as the capability.
Where it actually lands in the lifecycle
The productivity is not evenly distributed. It concentrates wherever a person is currently acting as the integration layer between two systems, which is to say wherever somebody reads one screen and types into another.
In development, the win is design and analysis in the same place as the data: a study designed against the question being asked, analyzed against its own runs, and written up from the run record rather than from a spreadsheet somebody maintained by hand.
In technology transfer, the difficulty is rarely the science and almost always the memory. What the sending site did is documented; why they did it usually is not. Models of the unit operations, calibrated on real runs, make a change of scale predictable, but they are only half of it. The other half is carrying the reasoning behind a set point along with the set point.
In manufacturing, real-time monitoring turns an excursion from something discovered at review into something visible while the batch is running, and review by exception turns a page-turning exercise into a judgment about the handful of steps that actually deviated.
What it means for you
Look for the places where a person is currently the integration layer. That is where automation returns time immediately, and where the return is measurable against work your team already did last quarter.
Why most of these programs stall
The failure is rarely the model. It is that the model was bought as a point solution, and a point solution in a regulated environment carries costs that do not appear on the quote.
It solves one piece
Most tools address an isolated part of the workflow. The value is in what happens between them, and that is where nobody has built.
Every pilot is another integration
The license is rarely the expense. The expense is a fresh connection to the ELN, the LIMS and the historian, and a fresh validation package, per tool.
The data is not where the model needs it
Runs in one system, methods in another, the reasoning in an inbox. Model quality is bounded by this long before it is bounded by architecture.
The organization has been here before
Teams that watched a machine learning program fail to survive contact with a validated process are right to be skeptical. Skepticism is a design requirement, not an obstacle.
There is a cultural barrier underneath the technical ones, and it deserves to be taken seriously rather than managed around. A validated process is an asset. The reluctance to put anything non-deterministic near it is not resistance to change; it is the correct instinct of people who are accountable for product quality. The way through is not persuasion. It is architecture: keep the record deterministic, keep the AI on top of it, and make every AI output reviewable against the evidence it used.
What to require before you buy any of it
The following is the diligence list we would want a customer to hold us to. It is deliberately about properties of the system rather than features of the product, because features are easy to demonstrate and properties are what you live with.
A system of record, before a model
Ask what the system knows about a batch with the model switched off. If the answer is a vector index over PDFs, every answer it gives you is an approximation of your own data rather than your data.
Every claim carries its evidence
A number in an output should be traceable to the run, well or signed step it came from, in one click. Anything that cannot show its working cannot be used in a submission or an investigation.
It fails loudly
Ask what happens when a value is missing. A system that quietly substitutes a near-enough match will pass its demo and cost you a deviation later. The correct behavior is to stop and say what is missing.
A named human signs
The regulatory position across FDA and EMA is consistent on this: the accountable decision stays with a person. Design for the AI to assemble, compute and propose, and for a qualified reviewer to confirm.
Your data trains nothing but your own instance
Get it in the contract, not the brochure. Ask specifically whether prompts, documents and outputs are used to train shared models, and whether one company's data is separated from another's by the storage itself or only by application code.
Validation is a first-class feature
Part 11 and Annex 11 apply whatever is inside the box. Ask for the audit trail specification, the change-control story for model updates, and a qualification package you can execute rather than one you have to write.
The regulatory ground here has firmed up considerably. ICH Q2(R2) and Q14 changed what an analytical procedure is expected to carry with it. FDA and EMA have both set out how they intend to look at AI used in the product lifecycle, and the shape of both is the same: define the context of use, then establish credibility proportionate to the risk that context carries. That is a workable standard, and it maps cleanly onto the properties above. A system that can show what a model was used for, on what data, and what a human did with the output, can be assessed. One that cannot, cannot.
What it means for you
Ask for a closed investigation or a released batch to be run on the system, and judge the output against the work your own team produced. It is the only diligence that cannot be rehearsed.
Where this leaves you
The technology is ready for a narrower claim than the one usually made for it. It will not design your process. It will remove the assembly work between a scientist and a decision: finding the runs, aligning the data, checking a result against its own history, drafting the document, and proving afterwards that all of it happened in the right order.
That is what BioprocessAI is: one governed system of record for molecules, runs, methods, batches and documents, with AI that investigates, analyzes and drafts on top of it, and a named human on every decision that matters. Nothing it asserts is unsourced, and nothing it cannot evidence is presented as an answer.
Bring us the work you
already know the answer to.
A closed investigation. A released batch. A report your team wrote last quarter. We will run it on the platform, and you judge the output against work you can already check.
or email sales@bioprocess.ai