Architecture
Reviewable
Trust boundaries, data flows, model gateway, and tool policy drawn before sprint zero.
For CTOs, VP Engineering & platform leaders
Build AI systems your enterprise can actually scale.
Architecture, security, and handover, so production AI survives design review and still runs after we leave. Your estate. Your IP.
VPC
Private / hybrid / sovereign deployment
LLMOps
Eval → release → observe → improve
Secure
Policy, audit, secrets, tool sandboxing
Owned
Code, weights, runbooks, your estate
Perspective
What CTOs evaluate before they trust a partner
The failure mode is familiar: a flashy prototype that cannot pass security review, has no evaluation regression suite, and cannot be operated by your platform team. Engineering leadership does not need more copilots. It needs systems that behave like infrastructure.
InheritX designs and delivers enterprise AI platforms and agent systems in private and hybrid estates, with GitOps-friendly delivery, observability, cost controls, and runbooks your platform team can operate after handover.
Executive snapshot
Technical credibility, scannable
What we expect to defend in your design review, without a fake scorecard.
Release quality
Eval-gated
Golden sets and regression suites as release blockers, not a one-off accuracy slide.
Operations
Handover
Your platform team can deploy, roll back, and extend without vendor presence.
Control plane
Your estate
IAM, secrets, VPC, cost caps, and audit trails in the environment you already run.
Production proof
Production systems that survived engineering review
Published architecture and reliability outcomes, not a scored audit of your estate.
AI PlatformsKavia AI
Governed AI software engineering for complex codebases
- Enterprise knowledge graph for dependency and system intent modeling
- VS Code extension, CLI, and Git-aligned branch management
10×
Faster spec-to-code iteration
100%
Codebase & dependency visibility
Computer VisionT2D2
AI-powered building and asset inspection at scale
- AWS S3 and cloud pipelines for high-volume image upload
- PyTorch models trained on forensic imagery for 80+ defect classes
70%
Faster exception & audit cycles
80+
Damage types classified
Conceptual framework
Enterprise AI architecture we design against
Intelligence sits on a real data and control plane. Agents are an application layer, not a substitute for architecture.
A pressure-testable topology. We map your estate onto this before we propose build.
01
Data sources
Systems of record, documents, events, and approved APIs, with residency and classification explicit.
02
Data & knowledge
Pipelines, corpora, vector/search fabric, and access control the rest of the stack can trust.
03
AI / ML services
RAG, routing, fine-tunes when earned, document intelligence, and model gateway policy.
04
Models
Your approved providers and serving path, Bedrock, Azure OpenAI, Vertex, or private vLLM.
05
Agents & applications
Orchestration, MCP/tool calling, HITL queues, and channels your users already work in.
06
Enterprise systems
ERP, CRM, core banking, clinical, and plant systems, integrations with scoped credentials.
07
Observe & govern
Tracing, eval, cost, audit, incident runbooks, and policy in runtime, not a PDF after the fact.
Conceptual reference architecture, not a latency SLO, capacity plan, or measured benchmark.
Architecture
Enterprise AI reference stack
A pressure-testable layer model, not a product catalog. We map your estate onto this before we propose build.
Experience & channels
AppsVoice / real-timeAPIsHuman approval queues
Agent & orchestration
Multi-Agent SystemsMCP / tool callingLangGraph / LlamaIndexHITL gates
Intelligence services
Enterprise RAG / AI SearchFine-tunesDocument AIGuardrails
Data & knowledge
Qdrant / pgvector / OpenSearchGraphRAGPipelinesFeature / corpus governance
Platform & ops
vLLM / FastAPI servingMLflow + LLMOpsOpenTelemetryGitOps / Argo CD
Security & control plane
IAM / secretsNetwork & VPCEval / red teamCost & rate controls
Architecture review
Review deployment strategy with an architect
Private cloud, VPC, hybrid, or sovereign, map trust boundaries, data flows, and serving topology before sprint zero.
Readiness
Production Readiness checklist
Use this in design review. If answers are vague, the system is not ready for enterprise traffic.
Deployment topology
VPC / private / hybrid paths drawn; egress and data residency explicit
Identity & secrets
Workload identity, vault patterns, no long-lived shared API keys in apps
Evaluation gates
Golden sets, regression suites, and release blockers for quality/safety
Observability
Tracing, latency budgets, token/cost metrics, incident runbooks
Tool & MCP governance
Scoped tools, audit trails, sandboxing, approval for high-risk actions
RAG quality controls
Chunking, hybrid retrieval, citations, corpus freshness, poison resistance
Operability
Platform team can deploy, roll back, and extend without vendor presence
Cost controls
Per-use-case caps, fallback routing, and budget alerts in the control plane
Dual view
Design review vs. demo theater
What we will not defend
- API-key sprawl and shadow model usage
- Agents with unrestricted tools and no audit trail
- Pilots with no path to shared platform services
- “Trust the model” without evaluation or policy
- Black-box stacks your SRE team cannot operate
What we bring to your review
- Target architecture with trust boundaries drawn
- RAG, routing, MCP, and guardrail patterns you can fork
- LLMOps operating model: eval → release → observe → retrain
- Security & diligence materials for CISO alignment
- Reference systems like Agent Bank for multi-agent workflows
Roadmap
AI lifecycle we operate against
From mandate to industrialized platform services.
Discover
Constraints & risk class
Estate, data, compliance, and whether RAG, agents, classical ML, or none applies.
Architect
Topology & controls
Trust boundaries, model gateway, retrieval fabric, MCP/tool policy, evaluation plan.
Prove
Governed release
Production-constrained pilot with integrations, observability, and human gates.
Industrialize
Platformize & hand over
Shared services, hardened LLMOps, IP transfer, enablement for your teams.
Decision matrix
Where CTOs typically go deeper
Jump to the lane that matches your current mandate.
| Need | Need | Motion | Start |
|---|---|---|---|
| Enterprise AI platform / RAG | Governed retrieval, routing, policy | Enterprise AI solution | Open |
| Multi-agent production systems | Agents + MCP + HITL | AI Agents + Agent Bank | Open |
| Security & diligence first | CISO-ready materials | Security FAQ + Diligence Pack | Open |
| Need production delivery without hiring lag | Architect-led squads in your rituals | Dedicated AI squads | Open |
FAQ
CTO diligence FAQs
Do you fine-tune, or only prompt?
Both when justified. Simplest approach that meets accuracy and latency bars first; fine-tune when evaluation proves the gap, and document the tradeoffs.
How do you handle secrets, PII, and tool access for agents?
Secrets in your vault patterns; tools scoped and logged; high-risk actions require human approval. Policy before autonomy.
Can our platform team operate this after you leave?
Yes, that is a design requirement. Handover includes architecture, evaluation suites, runbooks, and enablement.
What clouds and model providers do you support?
Your approved estate, AWS, Azure, GCP, and your model gateway choices. No proprietary runtime you cannot own.
How does MCP fit your agent architecture?
As a governed tool interface with scoped servers, auditability, and clear trust boundaries, not unrestricted tool sprawl.
How do GitOps, CloudOps, and LLMOps show up in delivery?
GitOps for environment promotion (typically Argo CD on your cluster), CloudOps for VPC/IAM/cost controls in the estate you already run, and LLMOps for eval → release → observe → improve. They are operating disciplines, not a product SKU.
Can you deploy private, hybrid, or sovereign?
Yes. Topology is a design input: private cloud, VPC, hybrid, or sovereign paths with egress and residency explicit before sprint zero.
Technical next step
Discuss your AI architecture.
Bring constraints, estate, and risk class. Leave with a clearer topology, evaluation plan, and go / no-go on the proposed shape.