Early access: join our pilot program

The operating system for your laboratory.

Describe your experiment in natural language. Infera turns it into validated, executable workflows across your instruments. One system from intent to execution.

See how it works ↓

Catches protocol errors in under 90 seconds. Before you waste reagents, instrument time, or a week of troubleshooting.

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PCR Amplification Protocol

Objective: Amplify DNA fragments using polymerase chain reaction

Materials:

- DNA template (50 ng/uL)

- Forward primer (10 uM)

- PCR Master Mix (2X concentration)

Procedure:

Step 1: Transfer 12.5 uL of PCR Master Mix to each well...

Step 2: Add 1 uL of forward primer to each well

Step 3: Add 1 uL of reverse primer to each well

Protocol steps

7 steps in your protocol

⠇Step 1TransferTransfer PCR Master Mix

⠇Step 2Tip EjectEject used tips

⠇Step 3Tip PickupPick up fresh tips

⠇Step 4TransferAdd forward primer

⠇Step 5Tip EjectEject used tips

⠇Step 6Tip PickupPick up fresh tips

⠇Step 7TransferAdd reverse primer

Describe the experiment. Simulate the workflow. Run the samples. No vendor scripts. No plate definitions. No guesswork. It just works.

The problem

Why experiments fail before they start

Protocols are written for humans. Full of implied knowledge, vague thresholds, and steps that only make sense if you were standing next to the person who wrote them. Every time a new student joins a lab, every time someone tries to reproduce a result, every time a protocol moves from bench to robot. That's where things break.

“Stir until dissolved”

Your protocol says it. Your robot can’t do it. There’s no quantifiable endpoint for a machine to hit.

“Immediately after”

Sounds clear to a human. Creates a race condition when two instruments are running concurrently.

“Room temperature”

Is that 20°C or 25°C? It depends on your building, your season, and your instrument’s calibration. Protocols assume. Machines can’t.

“Transfer to the next plate”

Which plate? Where on the deck? What was in it before? Protocols lose provenance across every handoff.

Know it works before you run it.

From natural language to trusted workflow

How scientists write protocols

Before

“Prepare the cell lysate by resuspending the pellet in 500µL lysis buffer. Incubate on ice for 30 minutes, vortexing occasionally. Centrifuge and transfer the supernatant to a fresh tube. Proceed to column purification.”

closeNon-executable by automation

What Infera produces

After

ACTION: resuspend(target: "cell_pellet", buffer: "lysis_buffer")
VOLUME: 500 µL
INCUBATION: 30 min @ 4°C
MIXING: vortex @ 5 min intervals
CENTRIFUGE: 12,000g × 10 min @ 4°C
TRANSFER: supernatant → container("tube_clean_001")
VALIDATION: volume_check(expected: ~450 µL)
NEXT: column_purification_protocol_v3

⚠ FLAGGED: “vortexing occasionally” / interval undefined. Set to 5 min. Confirm?

checkVerified machine-readable object

Human-in-the-loop: Infera doesn't guess. When something is ambiguous, it asks you. Instead of silently making assumptions that surface as a failed run three hours later.

Four steps from protocol to execution

Ingest

Drop in your PDFs, SOPs, lab notebooks, or just describe what you want to do in plain English. Infera parses for procedural intent, reagents, equipment, and constraints.

Disambiguate

Missing parameters like time, temperature, volumes, and sequence dependencies get surfaced for your review. You fill in the gaps. Infera doesn’t guess.

Validate

The execution graph gets checked against safety rules, equipment limits, and internal consistency. Dead ends, timing conflicts, and spatial violations get flagged before anything touches your instruments.

Execute

Validated workflows export to your ELN, LIMS, or directly to your robotic execution platform. Every decision logged. Full audit trail.

Catch the dead ends before they cost you a run

Infera doesn't just parse words. It simulates the execution graph to find logical dead ends, unsupported branches, and constraint violations before anything runs.

account_tree
Branching logic
Finds the hidden if/then decisions buried in your protocol text. The ones that only surface when a run fails at 2am.
timer
Temporal dependencies
Maps every time-sensitive sequence and flags when two steps can’t actually happen at the same time on your hardware.
view_in_ar
Spatial constraints
Checks whether your deck layout and material movement actually support what the protocol assumes.

Experiments2 active

pcr4/5/2026

Draft2 protocols

cell-culture-passage4/7/2026

Running1 protocol

Built for everyone in the lab

science

For researchers

You shouldn’t need to learn Python to automate your experiment. Describe what you want to do. Infera handles the translation, the validation, and the handoff to your instruments.

precision_manufacturing

For automation engineers

Stop manually translating SOPs into vendor-specific scripts. Infera takes the protocol as written and produces validated, executable workflows across any supported platform.

supervisor_account

For lab directors

Your $200k instruments sit idle because the pipeline between scientist intent and machine execution is broken. Infera fixes the pipeline so your equipment gets used.

The gap

This isn't a new problem. It's an unsolved one.

Labs spend $200k on instruments that sit idle because no one can program them fast enough.

Protocols get written once, understood by one person, and break the moment that person leaves.

Scientists shouldn't have to learn Python to run an experiment. Engineers shouldn't have to hand-translate SOPs into vendor scripts.

The gap between what a researcher means and what a machine needs is where time, money, and reproducibility go to die.

We're closing that gap.

Harvard + Caltech team · Y Combinator P26 · Early access: join our pilot program

Works with your stack

Infera outputs to open formats compatible with ELNs, LIMS, and robotic execution platforms. We're building direct integrations with our design partners. Tell us what you use.

notifications

Slack Alerts

Get notified in Slack when a protocol fails

Connectedexpand_more

cloud_upload

Infera Gateway

Auto-upload from instrument PC. No manual export needed.

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Automation Devices

Send protocols directly to Hamilton, Opentrons, and more

Lab Modeexpand_more

hub

Data Connectors

Auto-detect instrument format, map columns, set QC thresholds

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Let's build this together.

We're a Harvard + Caltech team, backed by Y Combinator (P26), looking for research labs who want to shape how experiments get designed and run. If your team spends too much time translating protocols into machine instructions, we should talk.