We build the data, governance, and platform foundation that gets you into the 15%. The AI Data Factory — a battle-tested blueprint, proven at Fortune 10 scale — turns AI ambition into production reality.
The reason isn't model accuracy. It's everything underneath: messy data, unclear ownership, missing governance, fragmented tooling, and platforms that can't carry experiments into production. Four failure modes account for the overwhelming majority of stalled AI programs.
Teams spend 80% of their effort on data wrangling, lineage hunts, and reconciling sources nobody owns.
PHI, PII, consent, and compliance gaps surface only after the model is built — killing the project at intake.
Every team picks its own stack. The result: no reuse, no observability, and no path to production at scale.
Generative AI flows through ungoverned APIs. Security has no visibility. Legal has no answer.
A blueprint — not a product — for delivering AI solutions quickly, safely, and repeatedly across business and clinical domains. Four pillars, one industrial delivery model, governance baked in as code.
Medallion architecture (Bronze / Silver / Gold), data quality scorecards, FHIR-aware privacy standards, and an intake process that makes "Gold readiness" measurable instead of aspirational.
One industrial delivery model for both classical ML and generative / agentic AI. Feature stores, model registries, RAG infrastructure, and prompt orchestration — all governed at the platform boundary.
GovOps as code — controls enforced by the platform, not manual review. Aligned with ISO 42001, NIST AI RMF, HIPAA, and OWASP Top 10 for LLMs. Auditable from intake through retirement.
Curated, opinionated, production-grade. Azure + Databricks + Delta Lake as the backbone. Reference patterns for chat, RAG, document understanding, and agentic workflows — ready to instantiate.
Concrete artifacts, not slideware. Each deliverable is documented, reviewable, and built to be operationalized.
Full architecture definition document covering business, data, application, and technology layers — aligned to TOGAF and the cloud Well-Architected Framework.
Vetted reference patterns: Foundry chat, multi-agent workflows, document intelligence, conversation knowledge mining, MLOps orchestration on Databricks.
Seven control domains spanning pre-intake through model completeness — mapped to ISO 42001, NIST AI RMF, HIPAA 2026, and OWASP Top 10 for LLMs.
Recommendations for operationalizing governance, augmenting your existing AI intake, and standing up an AI Review Board process that doesn't slow teams down.
Repeatable rubric for scoring datasets against "Gold readiness." Intake questionnaire that captures DQ evidence, privacy constraints, and ownership before work starts.
A medallion data lake for AI observability. Every prompt, response, and decision logged through governed gateways — ready for audit, ready for compliance.
Our blueprint was developed in production with one of the largest healthcare companies in the world — navigating PHI, FHIR, HIPAA, multi-business-unit complexity, and the realities of regulated data. The patterns are battle-tested. Your team gets the benefit of that work, compressed into weeks.
The AI Data Factory is our flagship engagement. It's also a window into how we work — pragmatic, opinionated, and focused on outcomes a CTO can defend in the boardroom.
Application portfolio rationalization, cloud migration roadmaps, and the hard architectural calls that determine whether modernization compounds or stalls.
Internal developer platforms, golden paths, and the operating model that turns infrastructure investment into measurable engineering velocity.
Data mesh vs. centralized lake, lakehouse adoption, lineage and stewardship — designed so your data org accelerates AI rather than blocking it.
Where AI actually moves the P&L — and where it's a distraction. Use-case prioritization, build-vs-buy, and capability roadmaps grounded in your business model.
Trusted-advisor relationships for executives navigating board-level technology decisions. Architecture reviews, due diligence, vendor selection, and strategic counsel.
Org design, delivery practices, SRE / observability maturity, and the engineering culture work that compounds over years rather than quarters.
Consilent Solutions is led by senior practitioners with deep experience in AI Governance, AI Architecture, platform engineering, and enterprise data — and backed by a network of specialist experts we bring in by domain.
Enterprise architecture, AI platform strategy, and the operating models that make modernization stick.
LinkedInAI governance, data engineering at regulated scale, and the delivery discipline behind production-grade AI platforms.
LinkedInAI governance specialists, MLOps engineers, healthcare data architects, and regulatory experts — assembled to fit the engagement, not billed by the seat.
Whether you're scoping an AI Data Factory engagement, looking for a second opinion on architecture, or trying to unlock an AI program that's stalled — a 30-minute conversation is the fastest way to find out if we can help.