Cut AI costs now. Build the path to private LLM infrastructure when it pays off.
Sobrn audits one or two real workflows and gives your business a written decision: keep using closed APIs, move to open-weight or private cloud, prepare to run your own AI/LLM infrastructure, simplify the workflow, or stop using AI where it is not earning its place.
2–3 weeks · 1–2 workflows · Cost model · Infrastructure path · EU-only
Best fit for teams dealing with
- Customer support workflows touching user data.
- Internal knowledge assistants using private documents.
- Legal, finance, or compliance document review.
- AI features blocked by security or customer requirements.
- Rising OpenAI / Anthropic API costs.
- Pressure to reduce vendor dependency without losing model quality.
- Uncertainty about whether open-weight models are good enough.
- Leadership asking when it makes sense to run AI infrastructure in-house.
The architecture decision behind the savings
The audit compares each option on your workflow, your data, and your volume — so infrastructure ownership becomes a financial decision, not a slogan.
| Option | When it fits | Risk | Cost | Complexity |
|---|---|---|---|---|
| Closed API | Best model quality, low setup | Data & vendor exposure | Variable | Low |
| Open-weight | More control, cheaper at scale | Quality varies | Medium | Medium |
| Private cloud | Sensitive data, ownership path | Ops burden | Medium / high | High |
| Self-run | High volume, in-house capability | Reliability & staffing | Lower at scale | Highest |
| No AI | Workflow does not justify AI | Lowest risk | Lowest | Low |
The audit gives you
One written report. Recommendation named on page one. The rest is the cost, risk, and infrastructure argument behind it.
Workflow map
Every manual, software, and AI step in the workflow you scope — end-to-end.
AI usefulness review
Where AI actually moves the needle, and where a rule, template, or removed step would do the job.
Data sensitivity assessment
What data flows through each step, what can leave your perimeter, and what cannot.
Closed API vs open-weight vs private deployment comparison
Side-by-side on your workflow, with assumptions written down for API, hybrid, private cloud, and self-run paths.
Cost, risk, and speed model
Fully loaded cost for each option at your stated volume, including the break-even point where private infrastructure starts to make sense. We show the math.
Go / no-go recommendation
One sentence on page one: keep on API, move private, simplify, or stop using AI for it.
Practical infrastructure path if it is worth doing
If the recommendation is to change architecture, the report names the first three steps toward a private or self-run setup — not a 90-slide roadmap.
Three workflows where the audit pays off most
If you recognise your workflow below, the first-pass report is the fastest way to see whether the audit is worth deeper analysis.
Customer support touching user data
The workflow. Inbound tickets summarised, classified, and drafted-against by an AI assistant before an agent replies.
Why it matters. Customer PII flows through a closed API on every ticket. Cost scales linearly. Some teams cannot expose ticket content to a US provider under their own customer contracts; others simply need a credible route away from per-token rent.
The decision. Keep on a closed API, move summarisation private (hybrid), pre-classify deterministically, or stop — if AI is not earning its place.
Product AI and internal assistants on private data
The workflow. In-product copilots or internal knowledge assistants that mix tenant, employee, or customer-private documents into prompts.
Why it matters. Sensitive material has to be embedded and queried somewhere. Closed APIs raise residency and trust questions; legal, HR, or works councils may already be pushing back. Private infrastructure can be the right answer, but only when usage and operations justify it.
The decision. Move private (open-weight or private cloud), restrict the doc set on closed API, simplify the scope, or no-go — if adoption blockers are not about AI at all.
Legal, finance, compliance, or ops document review
The workflow. Long documents (contracts, supplier paper, filings, incident logs) read, extracted, classified, or summarised by AI alongside humans.
Why it matters. Documents are confidential, often privileged. Per-document API cost rises fast with long context. Misses are expensive; vendors and customers ask where the data went.
The decision. Migrate bulk to private and keep long-context edge cases on closed API (hybrid), simplify with structured extraction, or stay on closed API if volume does not justify the ops load.
Founder-led, vendor-neutral analysis
Sobrn is led by Rui Abreu, based in Portugal. We help European teams make practical AI workflow and architecture decisions.
Sobrn is vendor-neutral: no GPU, hosting, model, cloud credit, or subscription incentive shapes the recommendation. The recommendation is based on workflow fit, data sensitivity, cost, model quality, and operational reality — including whether your business is ready to run more of the AI stack itself.
Rui Abreu — Founder
Lead Data Scientist with 10+ years across software engineering, machine learning, and data systems — full-stack web, data pipelines, and production NLP on cloud infrastructure. MSc in Informatics from ISEC (Coimbra); Microsoft Certified: Azure Data Scientist Associate.
- Founder-led review. You speak with the practitioner who writes the report.
- Qualifying requests answered personally.
- EU-based; no subprocessors outside the EEA for site and intake.
- Mutual NDA before detail on request.
If you have a specific workflow in mind, generate a first-pass report.
Two minutes of context. Immediate fit, risk, and architecture direction. Calls come later only when the use case has enough signal.