Use case scoping
We tighten the scope, define success criteria, and pin down data access, interfaces, and security requirements. The contract is the contract.
AI Implementation
Production AI for your environment. Not a demo, not a sandbox — a system your operations team can run.
What this is
From validated idea to production system.
The jump from prototype to production is where most AI projects stall. The model works in a notebook, then needs to talk to your CRM, respect your access controls, log to your observability stack, and survive the next dependency upgrade. We build that whole stack — data integration with your sources, API design to industry standards, security-compliant architecture, monitoring, alerting, and runbooks. Stack-neutral: OpenAI, Anthropic, open-source, on-premise — chosen by requirement, not by sales pitch. What you get: a working production system, API documentation, monitoring dashboards, drift detection, and runbooks your operations team can act on. No demo video left in your inbox.
How we build.
Six steps from defined use case to a system in production.
Data readiness
Quality check, pipeline setup, access rights. No reliable model without clean data — and no clean data without engineering.
Architecture design
System architecture for integration, scale, security, and operations. Data flow, API design, and rollback path are decided up front.
Build & integration
Model development, API implementation, integration with your systems. Iterative, with weekly reviews and visible progress.
Test & validation
Functional tests, load tests, security review. We validate the system against real conditions before it goes live.
Deploy & monitor
Production deployment with rollback. Monitoring, alerting, drift detection, and runbooks for operations.
What production AI delivers.
A few concrete markers across delivery, reach, and continuity.
12–16 wk
To production
Typical timeline for a defined use case
99.5%
API availability
On a sound architecture with monitoring in place
3
Operations artefacts
Runbook, dashboard, API documentation — handed over
100%
Security reviewed
Every system audited before it goes live
Common questions.
We don't ship isolated features. Every system is integrated into your stack with API, security, and operations designed in. We hand over something your team can actually run.
Data stays in Switzerland or the EU as required. On-premise deployment is on the table. Architectures meet FADP/nDSG. For sensitive data we skip cloud AI APIs and use local models.
We install monitoring for model performance and data distribution. You get alerts. The runbook describes re-evaluation and retraining. Optional maintenance agreement available.
Stack-neutral. OpenAI/Anthropic for general LLM work, Hugging Face or self-hosted models for specific tasks, infrastructure on AWS/Azure/GCP or on-premise. Chosen by requirement.
Ready for AI that holds up?
Thirty-minute intro call directly with a senior AI engineer.