Pre-release · testing only. Free throughout 2026. No support, server capacity is what it is — this is new technology and a new service.
GrowtOperator ProgramSign in
Exclusive territories — earned, not claimed

Keep 75–85%
of every deal.

Operate Growt's structural audit engine under your own brand, in your own region. We provide the platform, MCP, and infrastructure. You own the customer.

14-day trial
No setup fee
Monthly payouts

Estimate your earnings

You keep over 12 months
0 kr
Healthy · 54% margin
5
20 000 kr
12 months
Gross 1 200 000 kr · Droidtech 252 000 krFull calculator →

Every industry

Where operators deploy Growt

The core question is always the same: are these two windows onto the same thing actually seeing the same thing?

Apply for your domain →
Model layers showing structural degradation after quantization

Quantization auditing

Int4 overall accuracy drops 1.6%. But automobile loses 7.3% while truck stays at 98.2%. Growt finds the blind spots accuracy benchmarks miss.

FPGA monitoring chip mounted on PCB alongside edge AI processor

Edge FPGA black box

Hardware-enforced AI monitoring. FPGA co-processor audits every inference in under 10μs — tamper-proof, hash-chained, insurable.

Quantum circuit validation dashboard

Quantum output validation

Your quantum computer gave you an answer. Is it structurally sound? Detect noise types, calibration drift, and cross-backend divergence — without classical simulation.

NIST AI Risk Management Framework compliance mapping

NIST AI RMF compliance

Map structural auditing directly to the seven characteristics of trustworthy AI defined in NIST AI RMF 1.0. Per-category disaggregated metrics and audit certificates.

Neural network data transfer visualisation

AI model transfer

Does your trained model transfer to production data? Validate before you ship — not after it fails.

Autonomous vehicles with LiDAR sensor overlays

Sensor pair comparison

Two radars, two lidars, two cameras observing the same scene — do they structurally agree, or has one drifted?

Medical research lab with data monitors

Multi-site clinical studies

Patient data from Site A vs Site B — certify structural equivalence before pooling cohorts or training shared models.

Satellite view of Earth with sensor grid overlay

Cross-modality alignment

Radar vs optical, Sentinel-1 vs Sentinel-2 — does the structural content of two modalities agree over the same geography?

Precision industrial measurement instruments

Equipment calibration

Two test benches, two lab instruments — are they producing structurally equivalent results or has calibration drifted?

Time-series monitoring screens with drift curves

Temporal drift detection

Has your data distribution shifted between last quarter and today? Catch regime change before your model notices.

Robot arm with simulation ghost overlay

Sim-to-real transfer

Does your simulated training data structurally match real-world sensor readings? Validate the reality gap before deploying a robot trained in simulation.

Split-screen simulation environments comparison

Sim-to-sim comparison

Two simulators, same environment — do they produce structurally equivalent training distributions? Validate before migrating from one physics engine to another.

Autonomous vehicle in varied environments

Domain randomisation

Does your randomised sim actually span the real-world variation your robot will encounter? Structural coverage analysis — not guesswork.

Neural network adapter module visualisation

LoRA adapter & task testing

Does a fine-tuned adapter trained for one task or domain structurally transfer to new data? Validate adapter compatibility before deploying to a new base model or dataset.

Pharmaceutical research lab with molecular data screens

Pharma & drug discovery

Cross-lab assay reproducibility, compound library transfer, multi-instrument calibration. Certify that two labs' molecular data are structurally equivalent before pooling or modelling.

Radar sensor data alongside a predicted camera view

Predict from any single sensor

Run all your instruments together once during calibration. Then drop any one in the field — Growt infers what the missing instrument would have recorded. Radar only? Predict what the camera would have seen.

The difference

Every other tool asks
“are they different?”

Growt asks how, and where.

MMD gives you a distance. KS gives you a p-value. Drift detectors give you alerts. Every one of them is a binary signal with magnitude. Operators sell structural insight.

vs MMD · KS · Wasserstein

Global statistics miss the structure.

Same mean, same covariance, completely different internal structure — they won’t catch it. Growt will, and tells you which dimensions are driving the divergence.

vs Classifier two-sample tests

A yes/no answer isn’t actionable.

Can the classifier tell A from B? Yes or no, with an accuracy score. It won’t tell you where or why. Growt identifies which part of the structure is mismatched.

vs Evidently · Arize · Fiddler

Post-deployment monitors are too late.

Built for time-series monitoring after launch. Not designed for pre-deployment validation, cross-instrument comparison, or multi-site equivalence certification.

By the numbers

Built for operators, not platforms

You retain the majority of revenue while licensing Droidtech's platform, brand, playbooks, API access, and infrastructure.

75–0%
Operator share of revenue
40–0%
Target net margin
0 days
Free trial franchise

Operator tiers

Start small. Grow as your franchise grows.

API

Usage-based

Stateless endpoints only.

  • Pay per call
  • $0 idle
  • Branded MCP
  • Basic support

Team

Monthly

Branded portal for your customers.

  • Everything in API
  • Session pipelines
  • Branded portal
  • Usage dashboard
Most popular

Compliance

Monthly

Audit trail, GDPR, EU AI Act registry.

  • Everything in Team
  • Audit log
  • GDPR export
  • AI Act registry
  • PDF reports

Enterprise

Custom

Dedicated infra and SLA.

  • Everything in Compliance
  • Silo DB
  • Custom MCP
  • VPN
  • SLA

Operator network

You're not alone — you're part of a network

Growt operators are domain experts. We bring you together — in dedicated 1:1 sessions and at our annual operator summit.

Operator 1:1 strategy meeting with Growt team

Dedicated 1:1 operator meetings

Regular strategy calls with the Growt team — your roadmap priorities, customer challenges, new vertical opportunities. Direct line to the people who build the platform.

Annual Growt operator summit — operators meeting in person

Annual operator summit

Once a year, operators from every domain gather in person — share what's working, shape the product roadmap, and meet the people solving the same problem in different industries.

Application process

Five steps. No surprises.

01

Apply

Tell us your domain, region, and the customer problem you want to solve.

02

Talk to the agent

The Growt operator AI agent checks availability and records your intent.

03

Territory check

We confirm your region and use-case domain are open. If not, adjacent options.

04

Activate or call

Skip straight to activation, or book an optional intro call.

05

Sign the agreement

Your region and domain are protected — no other operator can compete in it.

Template contracts

Draft templates — review with your legal counsel before use. Print or save as PDF from the page.

Try it first

Talk to the Growt operator agent

Describe your use case, region, or the problem you want to solve for your customers. The agent will run a demo audit and check territory availability.

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Ready when you are.

14-day trial franchise — full Operator MCP access, no billing until you convert. Applications reviewed personally.