- Workflow
- Internal meeting transcription and searchable summaries
- Input
- Meeting recordings
- Constraint
- Raw audio should remain inside controlled infrastructure
- Options evaluated
- Public speech API
- Local speech-to-text
- Private cloud inference
- Hybrid processing
- Output
- Recommended deployment model
- Infrastructure requirements
- Retention/data-flow recommendation
- Pilot scope
- Evaluation criteria
Private AI Readiness Audit
Find out whether private AI is worth operating for your workflow
A written recommendation on whether the workflow belongs on a public API, a private deployment, or a hybrid — before hardware or a pilot is purchased.
From €1,500
One workflow. One architecture decision.
Evaluate one concrete AI workflow and decide whether it belongs on a public API, private infrastructure or a hybrid architecture before committing to implementation or hardware.
Good fit when
- You already have a concrete AI workflow in mind
- Company documents, recordings or internal data are involved
- Data cannot simply be sent to arbitrary external services
- You are considering local models or private infrastructure
- Someone has suggested buying GPU hardware before the architecture is clear
- You need to compare public API, private and hybrid approaches
Inputs
- Use case
- Users
- Data sensitivity
- Representative documents or audio
- Approximate volume
- Required output
- Latency expectations
- Existing infrastructure
- Integrations
Analysis
- Public API versus private versus hybrid
- Speech-to-text requirements
- RAG suitability
- Model requirements
- Infrastructure requirements
- Data flow
- Access control
- Logs and retention
- Evaluation criteria
- Operational complexity
Deliverables
- Architecture recommendation
- Public / private / hybrid decision
- Pilot scope
- Required components
- Risks
- Indicative infrastructure needs
- Evaluation plan
- Recommended next step
The audit may conclude that a public API is simpler and more economical for the workflow.
What a recommendation can look like
Illustrative example — not a client project.
What this audit does not include
- Production implementation
- Purchasing GPU hardware
- A generic company-wide “AI strategy”
- Regulatory certification
- A promise that private infrastructure is better than a public API
Backend engineering, not an AI demo
Private AI systems still need APIs, storage, access control, queues, deployment, observability and failure handling. The audit is approached as a production backend architecture problem rather than a chatbot demonstration.
- Senior Backend Engineer
- Production backend systems
- Go / PHP / Python
- PostgreSQL / Redis
- Docker / Kubernetes
- Local, private-cloud and hybrid architecture
Next step
Name the workflow before buying infrastructure
If a public API is the better fit, that is a valid result of the audit.