Trusted by clients:
The roadblocks to Cloud and AI deployment
Building custom GenAI, shifting legacy apps to modern cloud architectures, or migrating infrastructure forces you to confront three hard hurdles.
New technology requires heavy upfront investment. You need proof it works before risking your operational budget.
Cloud is billed on consumption. You need a strict mathematical forecast to ensure auto-scaling doesn’t destroy your profit margins.
Validating an idea on your own systems takes weeks of bureaucratic approvals and diverts your engineers from revenue-generating work.
Validate technical feasibility and business ROI
You get a total reality check for your project through a two-phase approach. First we test basic functionality. Then, prove tangible business outcomes in a simulated real-world environment.
Can we build it? (Proof of Concept)
Building a prototype to prove that features, security, and scalability of the solution meet your requirements and technical success criteria (that we define with you.)- Should we build it? (Proof of Value)
Testing the technology using your real data in a simulated production environment to assess its impact on the KPIs and goals that prove its business value.
Can we build it? (Proof of Concept)A validation timeline with zero scope creep
To guarantee answers, we run time-boxed, fixed-scope tests that protect your project against budget creep and expanding requirements.
Define Success
Fix the Scope
Rapid Implementation
Data Validation
The Decision
The Turnkey Cloud Sandbox
Testing new software usually forces your engineers to waste weeks deploying infrastructure and securing approvals.
We bypass this bottleneck by adapting to your capabilities.
How we test critical architecture
This sandbox environment handles the most severe engineering challenges. We rely on active load testing, not theoretical projections. Here is what that looks like in practice:
A regional distributor requires an AI bot to automate inventory queries, but the owner worries the AI will hallucinate incorrect stock levels or expose supplier pricing.
- The Sandbox Test: we feed anonymized inventory data into a secure Gemini Enterprise Agent Platform. We measure the agent’s accuracy and reasoning under heavy query loads.
- The ROI Proof: the test proves the agent handles 40% of routine inquiries safely and clearly flags rest for human input. The CFO calculates the exact salary savings of deferring new support hires, justifying the full build.
A production facility needs to identify failing equipment using existing sensor data. To justify building a machine learning model, they must first prove the algorithm can process the data stream fast enough to be useful on the factory floor.
- The Sandbox Test: we ingest a sample of their historical sensor data into BigQuery ML. Then we build an initial model to test two technical requirements: processing speed and detection accuracy.
- The Technical Proof: the prototype successfully processes the data stream, identifying equipment anomalies in under 30 seconds with 95% accuracy. The engineering team gains the empirical data to validate the feasibility before budgeting in a full-scale integration.
A mid-sized healthcare provider relies on a monolithic billing app on physical servers. The CTO knows they must move to the cloud, but migration risks crashing the system and delaying client invoicing.
- The Sandbox Test: instead of a full migration, we extract the heaviest billing module, containerize it on Google Kubernetes Engine (GKE), and run simulated traffic.
- The ROI Proof: the module processes data 35% faster without failing. The CTO proves feasibility, and the business owner verifies a projected 22% drop in monthly maintenance costs before authorizing the full migration.
A retail brand’s website crashed during a holiday sale. They need auto-scaling cloud servers, but the owner fears a spike of consumption-based pricing will result in a massive bill.
- The Sandbox Test: we deploy a replica of your storefront in our isolated environment and simulate a traffic spike 300% larger than your previous crash.
- The ROI Proof: the architecture handles the load without downtime. Based on monitoring metrics and actual spends, we prove that Google Cloud scales down immediately when traffic drops, guaranteeing cost control.
What you receive: the Decision Assets
When the test concludes, you receive the functional prototype, the usage metrics,
the measures for success criteria, and the documentation required to confidently plan your next step.
Working code built in a secure environment. You interact with the minimal implementation of your challenge to verify its performance against your specific data.
A strict mathematical model calculating the Total Cost of Ownership. Based on consumption metrics from the test, you get projections for your future monthly Google Cloud bills.
An objective assessment detailing the actual measures for your identified success criteria and a confirmation whether the concept achieved the desired business KPIs.
A diagnostic of the prototype’s performance. A rundown of the specific Google Cloud tools utilized and a map of any data constraints or integration bottlenecks discovered during the test.
FAQ: clients ask these questions about the sandbox test
Variable cloud pricing and AI token consumption make generic estimates dangerous. By monitoring real consumption during the test, we extrapolate the data to deliver a precise Total Cost of Ownership (TCO) model. You know exactly what your monthly Google Cloud bill will be before you authorize full-scale development.
You walk away. If the PoC proves that the technology cannot handle your data, or if the unit economics do not yield a profit, we halt the project. You save the massive budget you would have wasted on an unverified build.
No. We may build and host the prototype inside our own secure, temporary Google Cloud sandbox. You do not provision servers, manage security access, or pay infrastructure setup fees during the testing phase.
Yes. Whether we build the prototype in your Google Cloud organization or host it in ours, your data remains isolated. We use a minimal, closed subset of your data, and your proprietary information is never used to train Google or third-party LLM models.
We operate on a fixed-scope, fixed-budget model to prevent scope creep. Depending on complexity, PoC engagements start from €2,000 and are typically completed within 4 to 8 weeks.
