What a private AI deployment
looks like in practice.
These are representative deployment architectures for each sector AKYB serves. They show the problem pattern, system design, and typical scope — not inflated client testimonials.
Private retrieval for confidential work product
A regional firm with 5-15 lawyers needs faster access to internal memos, precedent notes, and matter research without exposing privileged material to shared AI.
Why standard AI fails here
Solicitor-client privilege requires that confidential material stays under the firm's control. Shared AI platforms process data on infrastructure the firm does not own, under terms that do not guarantee exclusivity. Most firms' risk committees reject this.
What gets deployed
- ✓ Document ingestion pipeline (memos, briefs, internal guidance)
- ✓ Private retrieval-augmented generation (RAG) system
- ✓ Chat interface for plain-English queries against firm knowledge
- ✓ Query audit log (who asked what, when, what was returned)
- ✓ Staff training and runbook
Internal knowledge assistant under HIA constraints
An Alberta clinic needs staff-facing retrieval for procedures, forms, and internal guidance. The deployment must be defensible under provincial health information privacy expectations.
Why standard AI fails here
Alberta's Health Information Act restricts cross-border transfer and requires a Privacy Impact Assessment before AI tools go live. Cloud-based AI infrastructure under US jurisdiction cannot satisfy this without significant legal risk.
What gets deployed
- ✓ On-premise hardware with private LLM
- ✓ Internal procedure and policy document ingestion
- ✓ Staff-facing knowledge assistant (not patient-facing)
- ✓ PIA support documentation for internal review
- ✓ Access controls and audit trail
Engineering knowledge retrieval for proprietary data
A technical team needs retrieval across reports, procedures, and engineering documentation without moving trade-secret-level material onto third-party platforms.
Why standard AI fails here
Reservoir models, exploration data, and engineering reports are trade-secret-level assets. Processing them through a shared platform under a foreign provider's terms is a risk most operators and their legal teams will not accept.
What gets deployed
- ✓ Technical document ingestion (reports, manuals, procedures)
- ✓ Private retrieval system for engineering knowledge
- ✓ Workflow automation for repetitive reporting (if scoped)
- ✓ Integration with existing document management where applicable
- ✓ Full audit trail and access documentation
Every deployment follows the same structure.
1. Data stays under your control
No data leaves your infrastructure. The model runs where your security policy says it should.
2. Architecture matches risk posture
On-premise, your infrastructure, or isolated cloud — chosen during Discovery based on your constraints.
3. You own the result
Open-weight models, full documentation, runbook handoff. No vendor lock-in, no ongoing licence dependency.
The same model applies to other
data-sensitive environments.
Accounting & finance
Private retrieval and classification for client records, review documentation, and internal procedures.
Access technology / field service
Technician knowledge retrieval and structured access to service records and site-specific documentation.
Aviation & aerospace
Controlled retrieval for technical manuals, maintenance records, and export-sensitive engineering documentation.
See what this looks like for your organization.
Start with a scoping call to determine whether a private AI deployment fits your workflows, data, and approval requirements.
Book a Private AI Assessment