Designing Enterprise AI Systems Beyond the Prototype
A practical architecture lens for moving enterprise AI from a convincing demonstration to a governed, observable, and operable system.
Insights
Thinking that helps
the work move forward.
Practical perspectives on enterprise AI, architecture, platforms, governance, and modernization.
A practical architecture lens for moving enterprise AI from a convincing demonstration to a governed, observable, and operable system.
Responsible AI becomes practical when principles are translated into system boundaries, controls, evidence, and accountable operating roles.
The difficult parts of enterprise retrieval concern authority, access, lifecycle, evaluation, and user trust—not only similarity search.
A platform creates value when it reduces repeated decisions while preserving the autonomy teams need to deliver useful services.
From the principal’s library
Practical guides hosted here on Arihant: governance, procurement, evaluation, and the engineering needed to put decisions into practice.
How to build a defensible AI business case: total cost of ownership, value levers, measurement plans, and the assumptions to stress-test before you commit.
Read the building the business case for enterprise ai: costs, value, and honest math guideThe human side of AI adoption: workflow redesign, training, trust, resistance, and the operating changes that turn pilots into habits.
Read the change management for ai adoption: getting people to actually use it guideA practical guide to AI governance: risk-tiered controls, model inventories, audit trails, and the operating cadence that keeps AI systems accountable.
Read the ai governance in practice: risk tiers, controls, and audit trails guideHow enterprises buy AI well: structuring RFPs, scoring technical and commercial proposals, running demos and pilots, and negotiating terms that protect you.
Read the evaluating ai vendor proposals: rfp design and scoring for enterprise buyers guideArchitecture decision records capture the why behind technical choices. Format, workflow, and how ADRs strengthen architecture governance.
Read the architecture decision records (adrs) explained guideHow Canadian public-sector technology procurement works: standing offers, competitive processes, and what vendors need to know before bidding.
Read the ai procurement in canada's public sector: how it works guideReduce cloud spend without slowing delivery: FinOps fundamentals, the architecture levers that matter, and how to sustain savings over time.
Read the cloud cost optimization: a finops and architecture guide guideA practical playbook for enterprise cloud migration: portfolio assessment, the 6 Rs, landing zones, migration waves, cutover strategies, and operating after the move.
Read the cloud migration playbook: assess, plan, move, operate guideDesigning an enterprise data platform: warehouse vs. lakehouse vs. mesh, data products, governance, and the sequencing that makes AI possible.
Read the data platform strategy: the foundation enterprise ai stands on guideA phased playbook for enterprise AI adoption: assess readiness, run disciplined pilots, scale what works, and govern the full lifecycle.
Read the enterprise ai adoption playbook: assess, pilot, scale, govern guideA plain-language glossary of 40+ enterprise AI terms — RAG, agents, evals, guardrails, fine-tuning, and more — for technical and business readers.
Read the enterprise ai glossary: 40+ terms, plainly explained guideDesigning enterprise integration: API strategy, event-driven architecture, iPaaS build-vs-buy, and the governance that keeps integrations from becoming spaghetti.
Read the enterprise integration strategy: apis, events, and platforms guideHow to evaluate LLM applications systematically: eval harnesses, quality metrics, human review, and red-teaming basics for production readiness.
Read the evaluating llm applications: evals, metrics, and red-teaming guideModernizing legacy estates without big-bang rewrites: strangler fig patterns, replatform vs. refactor vs. replace, risk management, and realistic sequencing.
Read the legacy modernization playbook: strangler patterns and honest roadmaps guideWhat observability means for AI systems: LLM tracing, evaluation in production, quality signals, and cost telemetry for every request.
Read the observability for ai systems: tracing, evals, and cost telemetry guidePlatform engineering done right: internal developer platforms, golden paths, and self-service that speed teams up without removing their autonomy.
Read the platform engineering: building paved roads for delivery teams guideHow Canadian public-sector organizations modernize legacy IT: digital service standards, delivery models, funding realities, and sequencing that survives elections.
Read the modernizing public-sector it in canada: a strategy guide guideTake retrieval-augmented generation from demo to production: chunking strategy, retrieval evaluation, grounding, access control, and ongoing operations.
Read the rag from pilot to production: retrieval, evaluation, and operations guideHow enterprise buyers assess vendor security: questionnaires, SOC 2 and certifications, penetration tests, data handling, and right-sizing reviews by risk.
Read the vendor security reviews: a buyer's guide to assessing technology suppliers guideWeighted scoring frameworks for technology vendor selection — criteria design, scoring discipline, demos, reference checks, and decisions that survive audit.
Read the vendor selection scorecards: making technology decisions defensible guideA free 30-minute introduction to discuss your AI governance challenge, constraints, and whether we can help. No project brief required.
Contact us for current availability. Scope, timing, and fees are agreed before paid work begins.