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From inventionto production

Infrastructure for research translation

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Breakthrough inventions move fast.

The systems around them don't.

Variance

Same inventiondifferent outcomes

Country

Outcomes depend on where it lands

The same invention can lead to radically different outcomes depending on the institution, team, and process around it.

Waiting for signature from Department head of X

What is the status of [x]? Can you update me?

Non-confidential summary - 1 page static pdf

Time sinks causes unpredictability

If we fix recurring time sinks, we can make tech transfer more predictable.

Case001of 847
Disclosure form
Prior art search
Due diligence
Market research
1 Page Non-confidential summary
Same tasks. Every case. Every time.

Reduce manual work

Automate the manual parts of tech transfer and make it faster and predictable.

Who we are building this for

The startup ecosystem has scaled beautifully. Thousands of VCs, incubators, and accelerators, all running the same proven playbook — plug-and-play infrastructure that turns ideas into companies.

But that playbook starts mid-stream. The real pipeline for deep tech is upstream, inside universities — and that infrastructure was never built. Inventions move slowly, unevenly, and founders are left waiting on a system that wasn't designed for them.

Fix the upstream and the downstream compounds. The ecosystem below is already fast.
ttOS makes the upstream match the speed - and the whole pipeline moves 10X.

Partner with us

Three groups we are building for

Academic / Inventor

Where you work, who handles it: neither should limit your impact

DIY. Self Service.
No waiting.
No variance.

Technology Transfer Office

Hiring more people to run the same system produces the same results.

Fix the time sinks.
Transparent.
Do what matters.

Startup ecosystem

Universities are the biggest source of deep tech inventions and founders.

Venture ready pipeline.
Derisk manufacturing.
Compounding effect.

Features

Tech transfer made
efficient, transparent, and scalable.

Automation Agents

Fix manual work across disclosure, evaluation, marketing, and licensing with guided automated workflows.

p

PDFDraft publication nature.pdf

Disclosure agent

extracting information and filling the invention disclsoure form

Signature routing

Fixes waiting on someone else by routing each signature step to the next owner, with auto-delegation when someone is delayed.

  1. Legal Review

    General Counsel Office

    Signed 2 days ago

  2. Department Head

    Prof. J. Lindström

    Signed yesterday

  3. Faculty Dean

    Prof. A. Okonkwo

    Awaiting – Day 2

    Head of TTO

    Final signatory

    Pending

    Auto-delegate

    Dr. R. Mehta (Acting)

    Routes in 3 days

    Rule

    IF
    No reply
    AFTER
    3 days
    THEN
    Delegate

Dashboard

Fixes manual updates with one live source of truth for CRM details and project status.

Self service
Collaborative
Auto CRM

Contract Logic Engine

Contracts as code.

Contract Logic Engine (CLE) extracts the logic of every clause. Contracts follow logic like computer code - WHOTHENWITHIF, and so on.

Once extracted, it is easy to compare diffs, set rules, and let business development teams work within the institution's risk profile.

CLE unlocks engagement with universities by making the process more transparent and predictable.

License Agreement · CLE parse · 4 clauses shown

What CLE unlocks

Diff & compare

See what changed, clause by clause

Governance

Version control, policy, and audit trails

Equity15%
0%10% max policy25%
Approval required for equity above 10%.

Clause 8.2 · Variance request

Reasons for the variance request

Deal context

Legal and Business development align

atom factory

Materials science is foundational to humanity's future. Every civilisational era has been defined by the materials it mastered.

AI is accelerating discovery — the bottleneck is no longer finding new materials.
It's manufacturing them.

2,000 samples.
Zero capex.

Atom Factory uses CLE to unlock university infrastructure globally — equipment, cleanrooms, synthesis facilities — by making contractual access fast, standardised, and programmable. Commission production runs across multiple labs simultaneously, with milestone-linked payments.

20 labs × 100 samples = 2,000 samples.

Running across 20 labs also surfaces reproducibility data — where errors cluster and what fails to scale — before you commit to a pilot plant.

2,000
samples, first run
20+
labs in parallel
$0
capex required

Novel Composite Material · Sourcing results — 6 universities found

University A
£2,000
100 samples · 6 weeks
AgreementDeliverablesOrder
University B
$2,200
100 samples · 6 weeks
AgreementDeliverablesOrder
University C
$2,100
100 samples · 7 weeks
AgreementDeliverablesOrder
University D
¥280,000
100 samples · 5 weeks
AgreementDeliverablesOrder
University E
CHF 2,400
100 samples · 6 weeks
AgreementDeliverablesOrder
University F
SGD 3,100
100 samples · 7 weeks
AgreementDeliverablesOrder

Contact us

The missing layer in research translation.

Great teams and real breakthroughs stall every day — not because the science fails, but because the system around it does.
ttOS is the infrastructure that gets out of their way.

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