LAgentBot · infrastructure for science
We turn deep scientific and regulatory expertise into callable AI agents and MCP tools — grounded in verifiable, network-owned data.
The thesis
The hardest knowledge — safety, regulatory, compliance, procedural — lives in fragmented PDFs, vendor portals, instrument logs, and human heads. General models hallucinate on exactly the questions where being wrong is expensive. LAgentBot makes that knowledge callable, verifiable, and current.
Regulatory sheets, vendor portals, and tacit expertise never make it into a form software can query with confidence.
LLMs sound authoritative on hazards and regulations — plausible, confident, and wrong precisely where it matters most.
We package expertise as agents and MCP tools grounded in a verifiable, current, network-owned data network.
The platform
LAgentBot is a platform, not a single app. Deep domain knowledge is packaged as agents, exposed as MCP tools that plug into any client, and grounded in a data network the community keeps honest. It's a pattern — one that generalizes across scientific domains.
We encode domain and regulatory reasoning into agents that answer in plain language — not a chatbot bolted onto a search box.
Every capability is a callable MCP tool — reachable from Claude, ChatGPT, Teams, Slack, or any MCP client, with per-user metering.
Answers trace to sources in a network-owned data layer that stays fresh through use — the ground truth agents are held to.
MSDS Chain
A conversational agent that answers in plain language — every verdict traced to its source.
msds-chain · verified agent
$ compatibility.check
"Can I store bleach and ammonia together?"
Prova · research preview
Scientific instruments already write down what they did — and scientists still write methods by hand, with no way to check them until the run fails. Prova closes that loop from both ends: it turns plain language into a method a machine can check, and reads an instrument's own provenance log back into the procedure that actually ran. The same pattern as MSDS Chain — expertise made callable, and answers you can trace.
prova · method check
$ method.check
"Collect the peak, then wash with 2 column volumes of buffer B."
01WATCHuv_1 rising 50 mAU → start collection
02HOLDuntil peak_end
03WASH2 CV · buffer_B
04MISSINGblock is never exited
Plain language becomes a structured method, then passes a deterministic linter and a simulator before anyone touches the instrument. The value isn't that a model can write a method — it's that the method can be proven wrong on a laptop.
Instruments emit audit trails, processing histories, and acquisition records. Prova mines them into the procedure a lab actually follows — and flags the runs that departed from it.
One engine, three unrelated instrument ecosystems: 132 NMR experiments, 197 crystallography structures, and 66 mass-spec runs — all public datasets, each read through its own native provenance format.
Prova is an active research programme, not a generally available product. The coverage figures are from public reference datasets; the method check above is an illustrative example run through our own linter and simulator. Talk to us if you run instruments and want to be early.
Principles
An answer you can't check isn't infrastructure — it's a guess with better production values. Three commitments hold everything we build to a higher bar.
Every answer traces back to a source. If we can't cite it, we don't assert it — no confident guesses dressed as fact.
Regulations and hazards change. The data network stays fresh through use, so answers reflect the world as it is now.
The data layer belongs to the network that builds it. Value compounds for the people whose use makes it trustworthy.