From Consulting Solution to
Operable Product
We are experts in not just developing AI project solutions, but productively evolving, operating and maintaining them – with complete governance for security-critical on-prem environments.
The Core Problem
Many AI solutions today are built as consulting solutions:
- ⚡ Quickly built (PoC/MVP)
- 🎯 Strongly use-case oriented
- 🔧 Often on open source or cloud stacks
But typically not sufficiently:
Reliability, Observability, Patchability, Runbooks
SLA-capable, L1-L3 processes, reproducible builds
Evidence trails, audit artifacts, risk management
Offline updates, artifact delivery, hardening, IAM
The transition from "consultant code" to "product in customer operations" is not a refactoring task – it's a production and governance transformation.
Our Vision: “From Consulting Solutions to Operable AI Products”
The Two Instances
Second Line of Defense + Policy-as-Code
The instance that:
- Defines governance & compliance
- Operationalizes controls (Policy-as-Code, Pipeline Gates)
- Automatically collects audit evidence
- Grants or denies approvals
Artifact Canon:
- Use-Case Cards
- Data Cards & Model Cards
- Risk Register
- Control Library
- SBOM & Audit Evidence Pack
- Red-Team/Abuse-Case Reports
Platform Engineering + SRE/ITSM
The instance that transforms a stack into a standardized operations platform:
- Kubernetes/OpenShift Baseline
- CI/CD + GitOps
- Artifact Management & Secrets/KMS
- Observability (Logs/Metrics/Traces)
- Policy Enforcement (OPA/Gatekeeper)
Support Model:
- L1: Service Desk/Operations
- L2: Productions/SRE
- L3: Engineering + Model Team
Guiding Principles
Not as an afterthought, but built-in from the start.
Air-gapped ready. Cloud is the special case, not vice versa.
Every release automatically generates audit evidence.
SLOs, Error Budgets, Observability, Incident Learning.
Platform engineering instead of project handwork.
Versioning, roadmap, backward compatibility.
Deterministic builds, signed artifacts, offline bundles.
Productization Lifecycle with Stage Gates
Stage Gates (Definition of Done)
Gate 0 – Product Candidacy
- Business owner, value, risk level defined
- Architecture sketch + dependency list
Gate 1 – Engineering Baseline
- Containerization, IaC/Helm, config separation
- Initial SBOM + license scan
Gate 2 – Security & Compliance Baseline
- Threat model, abuse cases, OWASP-LLM risks
- Data Card/Model Card, privacy review
Gate 3 – Operational Readiness Review (ORR)
- SLOs/SLIs, runbooks, alerts, backup/restore test
- Support model (L1-L3) + KB articles
Gate 4 – Production Release
- Signed artifacts, offline bundle
- Rollback strategy, change procedures
Gate 5 – Continuous Compliance
- Regular re-evaluation
- CVE management, model re-evaluation
Reference Architecture
┌─────────────────────────────────────────────────────────────┐
│ Customer Processes/Users │
└─────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────┐
│ AI Application Layer │
│ APIs/UI · Workflows · Domain Logic · Guardrails · RAG │
└─────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────────┐
│ AI Runtime Layer │
│ Model Serving (LLM/Embeddings) · Vector DB · Retrieval │
└─────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────┐
│ Platform Layer ("Productions") │
│ K8s/OpenShift · CI/CD & GitOps · Artifact Registry │
│ Secrets/KMS/HSM · Observability · IAM · Policy Engine │
└─────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────┐
│ Infrastructure Layer │
│ Compute (CPU/GPU) · Storage · Network · OS Hardening │
└─────────────────────────────────────────────────────────────┘
═══════════════════════════════════════════════════════════════
Cross-cutting:
┌─────────────────────────────────────────────────────────────┐
│ Cognitive Hive │
│ Governance · Risk · Compliance · Assurance │
│ Policy-as-Code Gates · Audit Evidence · Model/Data Cards │
└─────────────────────────────────────────────────────────────┘
Compliance Domains
Access, logging, hardening, vulnerability management
Data minimization, purpose limitation, deletion, DPIA
Transparency, traceability, bias/quality, human oversight
OSS licenses, SBOM, dependency risks
Reference Frameworks:
- ISO/IEC 42001 for AI Management Systems
- NIST AI RMF as risk-based approach model
- OWASP LLM Top 10 as GenAI security risk list
- EU AI Act (phased effectiveness 2025-2027)
Success Criteria
PoC → Prod decreases without increasing risk
Complete audit evidence per release
Mean Time to Recovery decreases
Reduce release over release
Ready for Production Governance?
Let's work together to make your AI solutions production-ready – with complete governance, support capability and compliance.