Reproducibility for Life-Science R&D

Make biological research reproducible.

Paste a paper, protocol, or DOI. Telomis returns a vendor-resolved cart with real catalog numbers, every line traced to where the protocol calls for it, then a complete procedure you can run at the bench.

No third-party API. Use the hosted version or self-host.

01The demo

Watch one sentence become a resolved cart.

Telomis reads the experiment, pulls every reagent, and resolves each to a real catalog number across vendors, with a cheaper validated swap flagged and a rationale per line. The example below is fixed and runs in your browser.

Telomis/ resolve
Ready
Experiment
Describe an experiment, or paste a publication, protocol, or DOI.
Controls: NAC rescue armCitation audit
Subtotal$1,491
Worked example

A published protocol, resolved into a cart.

The cart above is illustrative. This one is real: the optical pooled screening protocol from Feldman, Funk et al. (Nature Protocols, 2022), resolved into an 11-stage procedure of more than 140 reagents and materials. Every line is traced back to the source and resolved to a real catalog number across vendors.

Telomis/ protocol · nihms1846522
Resolved
  • KAPA HiFi HotStart ReadyMixRoche · KK2602
  • FastDigest Esp3IThermo Fisher · FD0454
  • Ampure XP SPRI beadsBeckman-Coulter · A63881
  • Phi29 DNA polymeraseThermo Fisher · EP0091
11 stages140+ itemsevery line traced to the source
02The problem

Over half of preclinical experiments are irreproducible.

That costs about $28B a year in the US. Reagents and reference materials are the single largest cause, 36% of it, roughly $10B. That slice is what Telomis resolves today.

Source: Freedman et al., PLOS Biology, 2015.

$28B
Lost each year to irreproducible preclinical research in the US.
36%
other causes
≈$10B
Reagents and reference materials. The slice Telomis resolves today.
Over 50%

of US preclinical results fail to reproduce.Freedman et al., PLOS Biology, 2015.

Up to 25% / ~10 hrs a week

of a scientist's time goes to routine inventory and reagent upkeep.Merck KGaA / MilliporeSigma survey, 2022.

One reagent, hundreds of papers

More than 300 cancer-biology papers used the wrong antibody, one that tags an unrelated protein sharing the name p16, and nearly the molecular weight, of the tumor suppressor they meant to detect. The error was invisible on a standard assay because both proteins run to the same place.

Reported by Science, 2026

Measured, not anecdotal
  • A survey of about 1,500 scientists found reproducibility failures across fields.Baker, Nature, 2016.
  • A multi-year replication of landmark cancer-biology results.Errington et al., eLife, 2021. Reproducibility Project: Cancer Biology.
03How it works

From a sentence to a resolved cart, then a procedure you can run.

01

Describe or paste

Describe your experiment, or paste a publication, protocol, or DOI.

02

Get a resolved cart

Telomis pulls the reagents and resolves each to real catalog numbers across vendors, with cheaper validated swaps flagged and a rationale per line.

03

Run the procedure

The cart becomes a complete procedure, step by step, with concentrations and dilutions worked out and a checkpoint at each step.

04Why a lab keeps using it

The reason a lab opens Telomis every week.

The cart resolves once per experiment. The procedure you run from it, and the shared inventory underneath, are what a lab returns to every week.

After the cart

A procedure you can run

The resolved cart becomes a complete procedure, step by step, with concentrations, dilutions, and volumes worked out and a checkpoint at each step. When the protocol does not specify a value, Telomis marks it proposed and says why, rather than filling it silently.

Procedure preview
  • 1Seed HeLa cells
  • 2Dose doxorubicin, ROS arm1:1000
    Checkpoint, ROS rises with dose and falls in the NAC arm.
  • 3NAC rescue armproposed · unspecifiedC₁V₁ = C₂V₂
Every week

Reagent management across labs

A shared inventory that matches what a protocol needs against what the lab already has. It flags gaps, expiries, and duplicate orders, and remembers across runs, so repeat work gets faster every time.

Inventory match
  • DMEM, high glucoseIn stock
  • Doxorubicin hydrochlorideOrder needed
  • Fetal bovine serumExpiring soon
  • HeLa cell lineDuplicate avoided
Remembered across runs, so the next one is faster.
05Where it goes
On the roadmap, not yet shipped

The papers that fail even with a correct procedure.

Every cart and procedure will generate a worked or not-worked signal, tied to a specific catalog number and a specific paper. At the scale of a network, that record will include the failures no database has, because they were never published.

Captured at the bench

Each run will log a worked or not-worked outcome against the exact reagent and the exact paper it came from. The record will hold the failures that never reach a journal, the ones no existing database can see.

Detection, not repair

When enough labs run a correct procedure and a result does not reproduce, Telomis will flag the paper. The signal stays aggregate and anonymized, never a person, and never an accusation of fraud.

“N labs could not reproduce this with a validated procedure.”
The moat

The moat is outcome data no one else has, because the failures it learns from were never published.

Ships today, the cart and the procedureOn the roadmap, outcome data that flags papers
06Who it's for

Built for the bench, on both sides of the dataset.

The data engine

Academic labs

Low friction, because the money is public, the norms are open, and whether an experiment worked is not their IP. They feed the outcome dataset.

  • Resolved carts with real catalog numbers
  • Vendor comparison and validated swaps
  • No third-party API. Hosted or self-host.
Private by default

Biotech and pharma

The shared, academic-fed outcome data, plus their own private layer on top. Lab-wide consistency, a shared library, and their data kept to them.

  • Lab-wide consistency and a shared library
  • Outcome capture across teams
  • Private data kept to them, no IP ever leaves the institution
Why getting reagents right matters

Re-validating a single failed study before clinical work is slow and expensive.

$500k to $2M
per study replication
3 to 24 mo
to re-validate
Freedman et al., PLOS Biology, 2015.
07Why it's different

Not a search box. Not a chatbot. A resolved cart.

Not a search box

Takes a protocol or research goal as input

Describe an experiment or paste a DOI. Telomis works from the science, not a keyword query.

Not text

Outputs a real cart with resolved catalog numbers across vendors

Specific SKUs, prices, and validated swaps you can act on, not a paragraph to chase down.

Not procurement or the notebook

Lives at the bench

It sits where the experiment is designed and run, between the idea and the order.

The part no one can copy is the outcome data the cart generates, including the failures that were never published.

08Features

Everything Telomis does, from the bench to the order.

One place to design an experiment, resolve every reagent, and keep the record.

Unlimited protocols

Turn any experiment, publication, or DOI into a resolved cart, with no cap on how many.

Resolved reagent cart

Real catalog numbers across vendors, with vendor comparison and cheaper validated swaps flagged per line.

Controls and citation audit

The right controls flagged for each protocol, and every line traced back to the source that calls for it.

PDF and Markdown export

Take any protocol or resolved cart out as a clean PDF or Markdown file.

Shared protocol library

One library your whole lab works from, for consistency across the bench.

Outcome capture and audit logs

Record what worked and what didn't, including failures that never get published, with a full change log for review.

09Team

Three founders, building from the bench up.

Andrzej Bachleda-Curuś, CEO of Telomis

Andrzej Bachleda-Curuś

CEO
  • Stanford CS/AI + Math
  • 2x National Math Olympiad finalist
  • Shipped multiple AI projects
  • VP of Startup Development at Stanford BASES
Stanford UniversityStanford BASES
M

Mateusz

CTO
  • Biophysicist
  • Bioinformatics and LLM research
  • 3+ years of computational experience
  • Co-author on AI in drug discovery papers
Jagiellonian University
Maciej Łach, CSO of Telomis

Maciej Łach

CSO
  • Biophysicist and PhD student in materials engineering
  • 4+ years of hands-on wet-lab experience
  • Co-founder of a medtech startup, tech licensed to a public company
Jagiellonian UniversityCracow University of TechnologyAptamedicaAptamedica
Private beta, 2026

Bring a real protocol. We'll resolve it.

Send a protocol you actually run. We'll turn it into a resolved cart and show you, before anything else.

Prefer a PDF? Email it to contact@telomis.com.

By sending, you agree we use your details to reply about the beta. Privacy policy

No third-party API. Use the hosted version or self-host.