Industries · Science & R&D
The lab produces data. Syrtis turns it into knowledge that compounds.
A good R&D assistant isn't a smarter model — it's a model that finally reaches the link between what you set out to test (hypotheses, Design of Experiments) and what actually happened (results). That link is the substrate. Without it, lab AI is a literature chatbot. With it, every experiment makes the next one faster.
The problem
In most labs, the experiment and its result never speak to each other.
Experimental designs live in spreadsheets. Results live in instrument exports, electronic lab notebooks, scattered files. And the reasoning — why this condition, what the result meant — lives in people's heads, and leaves with them. Negative results vanish. "Didn't we already try that?" has no answer.
Put an AI assistant on top of this disconnection and it inherits it: it can summarize a paper, but it cannot reason about your program, because your program was never captured as connected knowledge.
The scientific substrate
Link every hypothesis to its result. That's where lab AI turns from gimmick to leverage.
Syrtis captures the experimental loop as connected, sovereign knowledge: the hypothesis, the design (factors, levels, conditions — the Design of Experiments), the execution, the measured result, and the scientist's interpretation. Each design point is bound to its outcome and its rationale. This connected corpus is three things at once:
- Institutional memory — every tried condition stays queryable, failures included. Negative results stop being lost.
- Grounding signal — the R&D assistant reasons over what you actually did, cited and traceable, not generic web knowledge.
- Predictive substrate — enough (design → result) pairs let you model the response surface, propose the next experiment, and close the loop (active learning, Bayesian optimization).
The R&D assistant
A lab copilot that actually knows your program.
Sitting on the substrate, the assistant reads the exact context it needs and writes fresh knowledge back — the same pattern as Syrtis agents, applied to the bench.
Proposes the next experiment
Suggests the next conditions from the response surface — not guesswork.
Never re-runs a dead end
Flags conditions already explored, failures included, with the run that proves it.
Drafts and traces
Drafts the report or protocol, every claim cited to its run.
Surfaces contradictions
Surfaces inconsistent results across batches, operators, instruments.
Transfers knowledge
Onboards a new scientist into years of program memory in days.
Where it applies
Anywhere progress runs through experiment → measurement cycles.
The pattern is domain-agnostic: wherever a team designs experiments and reads measured outcomes, the substrate applies. For example —
Micro-electronics & semiconductors
process and recipe optimization (etch, deposition, lithography), yield ramp, DoE across tool parameters.
Peptide & protein formulation / drug delivery
excipient screening, co-formulation, stability.
Materials science
alloys, composites, coatings: composition × process × property.
Fine chemistry & catalysis
reaction optimization, ligand and condition screening.
Batteries & energy storage
electrolyte/electrode formulation, cycling outcomes.
Process & bioprocess
fermentation, scale-up, media optimization.
Different molecules, the same loop — hypotheses designed, results measured, knowledge that should compound but usually evaporates.
Sovereignty & compliance
Your R&D data never leaves your perimeter.
R&D data is the crown jewel: intellectual property, trade secrets, partner confidentiality. Syrtis runs on-premises or in sovereign EU cloud. Model-by-node selection keeps sensitive steps on local models. Full provenance, versioning, audit trail. GDPR and the EU AI Act respected by design. This is also what makes deployment possible behind confidentiality walls and inside consortium R&D.
Turn years of experiments into an advantage that compounds.
FAQ
How does Syrtis connect to our ELN / LIMS / instruments?
Through ingestion nodes: documents, SQL databases, APIs/webhooks, instrument exports. Everything enters via the indexer, with no upfront rebuild.
Do we have to structure our data first?
No. Capture comes first; indexing routes each item into the memory that fits it (vector, graph, document, SQL).
Does this replace our DoE tools or statisticians?
No. Syrtis links and remembers the design → result pair. It complements your DoE and optimization, it doesn't replace them.
Where does our data live?
In a sovereign substrate: on-premises or EU cloud. Data does not leave your perimeter.
How long until a useful first assistant?
A first assistant is built in hours, not months. Compounding value grows as the experimental loop fills in.