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A generic LLM can talk CMC. InThoth helps you build the process.
ChatGPT, Claude, and general assistants can all answer biopharma questions. But R&D doesn’t need a paragraph that sounds right — it needs computation that actually runs, evidence you can trace, a process that reaches the bench, and a clear answer to where your data went. Here’s the difference.
Generic AI (ChatGPT, etc.)
InThoth
Domain knowledge
Trained on public text, with no real CMC process precedent — prone to answers that are textbook-correct but not engineering-usable.
Inscinstech CMC v2.2 knowledge base (82+ curated entries) + FDA review distillations + process precedents — a corpus generic LLMs can’t reach.
Scientific data sources
Answers from what it memorized at training time — data may be stale, and it can’t query authoritative databases to check.
Connected to 35 life-science data sources (UniProt, ChEMBL, Open Targets, PubMed, ClinicalTrials.gov, openFDA, plus domestic sources such as iProX and CNCB) — queried live, with links you can follow back.
Structure & molecular design
Can describe the methods but can’t actually run folding, docking, inverse folding, or binding free energy.
Actually runs folding and co-folding, de novo binder design, inverse-folding sequence design, docking, and binding free energy — every run captures provenance, structures render as interactive 3D.
Prediction & DoE
Can’t run mechanistic + ML process prediction or active-learning experiments — only restates generic practice.
Hybrid mechanistic + ML prediction and active-learning DoE — reaches optimal starting conditions in the fewest wet-lab rounds.
Device–software loop
Text-only output that never reaches the bench; design and execution stay disconnected.
Connected to our own instruments — bioreactors / chromatography / TFF / analytics. A designed process can flow straight to the bench.
Your process records
Can’t see your historical batches, so it can’t analyze yield variability or trace impurities from your own data.
Reads the process records in your tenant — analyzes CQA trends across batches and locates root causes, auditable throughout.
Compute sovereignty & routing
Requests go to the vendor’s own cloud; you can’t pin where inference runs, and there is no “this class of data must not leave the region” switch.
Compute is routed by session sensitivity: sensitive and confidential work runs only on compliant in-region providers and our own GPUs. With no compliant option it refuses outright rather than falling back overseas — and any provider without a declared residency is treated as overseas.
Data residency & compliance
Inputs often enter third-party models and may be used for training; no residency guarantees, hard to pass GxP / 21 CFR Part 11.
Data stays in-region, residency per contract, customer data never trains the model, audit trails ready.
Traceable conclusions
No citations, can hallucinate, and can’t trace a claim back to its basis.
Every key conclusion carries a knowledge-base citation you can open, trace, and hand to your CMC team.
Vague ask → complete task
Answers exactly what you asked — an incomplete brief yields an incomplete answer.
Smart Brief turns a vague sentence into a structured brief card; 68 expert skills ensure the task is delivered in full.
Same question. Two outcomes.
A generic AI hands you a paragraph — then you still have to turn it into a process, find the basis yourself, and own the compliance. InThoth hands you a hand-off-ready, auditable, cited process — because it was built for biopharma CMC, not merely able to chat about it.