AI reference architecture
Layered view of data, models, orchestration, integration, and observability with non-functional requirements explicit.
AI Consulting
Architecture, governance, and roadmap design for AI at scale.
Engage senior architects for board-ready blueprints, data readiness, model strategy, LLMOps posture, security controls, and sequenced investment before major build spend.
Fit
Consulting entry points
Engagements are scoped to decisions you need to make now, not open-ended advisory retainers.
Board asks for an AI strategy with defensible architecture
Executive briefing plus target-state blueprint and investment sequence
Security review blocking generative AI rollout
Threat model, control matrix, and approved deployment patterns
Vendor proposals with conflicting technical claims
Independent architecture assessment and build-vs-buy recommendation
M&A or platform consolidation affecting AI assets
Due diligence on models, data rights, and integration risk
Perspective
Decisions worth getting right before build
Enterprise AI failures are often architectural: retrieval without lineage, agents without permission models, or model choices that ignore data residency and cost at scale.
InheritX consulting engagements produce artifacts your CTO, CISO, and enterprise architecture forum can act on, reference diagrams, control mappings, and phased roadmaps tied to measurable outcomes.
We stay technology-agnostic where appropriate, recommending patterns that fit your estate rather than defaulting to a single vendor stack or open-source religion.
Capabilities
Typical engagement outputs
Data & readiness assessment
Corpus inventory, quality gaps, and ingestion priorities ranked by impact on planned use cases.
Model & LLMOps strategy
Guidance on hosted vs. self-managed models, fine-tuning triggers, evaluation standards, and promotion gates.
Governance & operating model
RACI for platform vs. product teams, policy templates, and vendor evaluation criteria.
Capabilities
Risk programs we design before scale
Named for buyers who search these as workstreams, not a parallel consulting brand.
AI eval, safety, and red team
Golden sets, offline/online eval, and adversarial testing so quality and abuse cases are evidence, not a slide on guardrails.
AI security (model threat)
Prompt injection, data exfiltration, tool abuse, and model-supply risk mapped to identity, DLP, and logging, distinct from classic AppSec checklists.
Responsible AI operating model
Policy, human gates, and audit evidence. Implementation detail sits in the AI Governance resource; consulting produces the RACI and control map.
Build-vs-buy for eval and safety tooling
When to use Langfuse / LangSmith-class observability versus in-estate harnesses, chosen against your data-residency and SRE constraints.
Dual view
Consulting vs. delivery
Consulting scope
Architecture and roadmap artifacts
Security and compliance alignment
Vendor and build-vs-buy analysis
Executive and board-ready narratives
When to add delivery
- Reference implementations to validate architecture choices
- Pilot capabilities that de-risk the roadmap
- Embedded squads during platform stand-up
- Knowledge transfer through paired engineering
How we engage
Engagement flow
01
Stakeholder alignment
Interviews with technology, security, legal, and domain leaders to surface constraints and success criteria.
02
Current-state assessment
Review existing pilots, data assets, integration landscape, and policy gaps.
03
Target architecture & roadmap
Draft and iterate in working sessions with your architecture board.
04
Executive readout
Decision-ready package with sequenced investments, risks, and recommended next engagement.
FAQ
AI Consulting FAQ
Most architecture and roadmap engagements run two to six weeks depending on stakeholder breadth and existing documentation.
No. We map options, build, buy, hybrid, and articulate trade-offs on control, speed, and total cost of ownership for your context.
Yes. Many clients proceed to platform build, agent delivery, or squad embeds using the same team for continuity.
No. Governance is the control design for production AI. We publish the pattern as a resource and produce the operating model in consulting, then enforce it in delivery.
Next step
Map this capability to your mandate.
A focused strategy conversation, constraints, systems, and what production readiness looks like for your organization.