Solution
Data & AI Readiness.
Get your data, infrastructure, governance, and team production-ready for AI, and ship the first real use case alongside the foundation. Three practices, one outcome: AI that actually reaches production.
- Time to first prod use case
- ≤ 6 mo
- Data quality lift
- +62%
- Governance posture
- Audit-ready
- AI cost per use case
- −68%
The condition
Most enterprises have data, infrastructure, and ambition — and a stalled AI program.
The familiar pattern: a board-level AI mandate, a data lake that nobody trusts, a security team that hasn't approved a model endpoint, a procurement process that hasn't seen a model card, and a backlog of pilots that all evaluate as 'promising' but never reach production. The gap isn't talent or technology: it's the operating substrate. Without a data foundation, a governance frame, and a deployable infrastructure pattern, every AI use case has to relitigate the same plumbing arguments. Most don't survive the relitigation.
Data & AI Readiness exists to close that gap in a single, orchestrated program. We assess where you are, prioritize the foundations that unblock the most use cases, build them alongside the first real production deployment, and hand you the operating cadence that sustains AI delivery quarter over quarter. CORTEX (AI/ML), FOUNDATION (data engineering), and CITADEL (security/compliance) move together. So the first use case ships on a substrate that the next ten can reuse.
What success looks like
Outcomes you can track monthly.
Every data & ai readiness engagement publishes a metrics dashboard at kickoff and updates it monthly. No vanity metrics, no mystery ROI.
- Time to first prod use case
- ≤ 6 mo
- Data quality lift
- +62%
- Governance posture
- Audit-ready
- AI cost per use case
- −68%
Practice mix
3 practices, orchestrated against one outcome.
Solutions are not single-practice engagements. The roles below show how each practice contributes, the same way a delivery plan names owners and acceptance criteria.
- 01
CORTEX
AI & Machine Learning
Generative AI, agents, computer vision, predictive analytics, and MLOps, engineered for production.
Role here
Owns the use-case selection, evaluation harness, and the production AI deployment that ships alongside the readiness program.
Open the practice - 02
FOUNDATION + SKYWAY
Platform & Cloud
Cloud architecture, DevOps, SRE, migrations, data engineering.
Role here
Stands up the data platform, vector stores, model serving infrastructure, and IaC-backed environments AI workloads will reuse.
Open the practice - 03
GUARDIAN + CITADEL
Quality & Security
Test automation, performance, accessibility, application security, secure SDLC.
Role here
Authors the AI governance frame: model cards, data lineage, evaluation policy, audit log requirements, and procurement-grade vendor risk reviews.
Open the practice
How we engage
4 phases, orchestrated across practices.
Each phase has named owners across the practices listed above, a shared deliverable, and an acceptance criterion at the program (not the squad) level.
- 01
Readiness assessment
15-question diagnostic across data maturity, infrastructure, talent, governance, and use-case backlog. Output is a scored report, a prioritized roadmap, and a defensible budget. Two-week engagement, fixed price.
- 02
Foundation build
Data platform, vector stores, model-serving infrastructure, and the governance scaffolding (model cards, eval harness, audit logs, vendor review process). Built once, reused across every AI use case the company will ship in the next two years.
- 03
First production use case
Selected jointly during the assessment. Built on the foundation as it stands up, not after. The use case proves the substrate works and produces an operating cadence the team can repeat without us.
- 04
Operating handoff
Quarterly model upgrade cadence. Continuous evaluation. Cost-optimization sprints. Hand off to your team with runbooks and a 90-day shadow period. Or stay on as Managed Services for production operations.
Capabilities
What’s in scope.
Capabilities span all the practices contributing to this solution. Out-of-scope items are named in the SOW too.
- AI Readiness Assessment with scored report + prioritized roadmap
- Data platform: lakehouse, warehouse, ELT pipelines, data quality monitoring
- Vector store deployment: Pinecone, Weaviate, Qdrant, pgvector
- Model serving: AWS Bedrock, Azure OpenAI, Vertex AI, self-hosted vLLM/TGI
- Governance frame: model cards, data lineage, eval harness, audit logging
- Vendor & subprocessor review process aligned to procurement requirements
- First production AI use case shipped in parallel with the foundation
- Operating cadence: quarterly upgrades, drift monitoring, cost optimization
Industries
Where data & ai readiness lands.
Most-frequent buyer industries. Each card opens the industry-scoped playbook with sector-specific compliance and operating constraints.
- 01
Financial Services
PCI-DSS, SOX, regional banking compliance built in.
- 02
Healthcare & Life Sciences
HIPAA, HITECH, FHIR-aligned engineering.
- 03
Retail & E-Commerce
PCI-DSS, consumer privacy, scale-tested architectures.
- 04
Manufacturing
OT/IT convergence, predictive maintenance, vision systems.
- 05
Logistics & Supply Chain
Routing, ETA prediction, exception management.
- 06
Energy & Utilities
NERC CIP-aware, grid analytics, demand forecasting.
Selected work
Quantified outcomes, not adjectives.
- 01Financial Services
+37%
fraud catch rateAI readiness + production fraud-detection deployment.
Replaced a rules-based engine with a streaming ML pipeline on AWS while standing up the bank's first AI governance frame. Reduced false positives 42% while raising true catches. Substrate now serves three additional use cases.
9 months
- 02Healthcare
$4.2M
annual labor savingsFHIR-aligned data platform + clinical decision support RAG.
Stood up a HIPAA-aligned data platform and the governance frame, then deployed clinical RAG over 12M documents on top of it. Two additional use cases (prior-auth automation, ambient documentation) now in delivery on the same substrate.
11 months
Common questions
Asked before the first call.
- 01
How is Data & AI Readiness different from buying a vendor platform?
Vendor AI platforms (Databricks, Snowflake Cortex, Microsoft Fabric, etc.) solve infrastructure but don't solve governance, use-case selection, or organizational readiness. They also don't ship a production use case for you. Readiness is the engineering and operating program that makes those platforms deliver value. Many of our engagements use one of these platforms as part of the substrate. We just don't stop at provisioning it.
- 02
What's a 'production' use case vs a pilot?
A production use case has SLAs, on-call coverage, monitoring, an evaluation harness, governance evidence, a documented rollback path, and a stakeholder who is accountable for the metrics. A pilot has none of those things. Most enterprise AI projects are pilots that nobody ever upgraded to production: Readiness is engineered to ship a real production system from day one, not a demo.
- 03
Do you require us to migrate to a specific data platform?
No. We're platform-agnostic and have shipped Readiness programs on Databricks, Snowflake, BigQuery, Synapse, and on-prem stacks. We assess the existing substrate during the readiness phase and recommend the smallest amount of platform change that unblocks the most use cases. Often the answer is 'fix the data quality on what you already have', not 'migrate everything'.
- 04
Who owns the AI governance frame at the end of the engagement?
You do. The frame includes documented policies, templates, and the operating cadence, not a black-box tool. We co-author it with your security, compliance, and legal teams during the engagement so it reflects your real procurement requirements, regulatory posture, and risk appetite. CITADEL (our compliance department) provides ongoing advisory under Managed Services if you want to keep us as a co-pilot, but ownership is yours from day one.
- 05
How do you select the first production use case?
Joint selection during the assessment. We score candidate use cases on data availability, business impact, technical risk, governance complexity, and time-to-production. We deliberately avoid the 'most ambitious' use case for first deployment. We pick the one with the highest probability of shipping cleanly so the team builds operating muscle. The next use cases get harder; the first one builds confidence.
- 06
What's a typical Readiness program budget and timeline?
Assessment: 4–6 weeks, $50K–$150K. Foundation build + first production use case: 4–9 months, $400K–$1.5M, depending on data complexity, regulatory frame, and the chosen use case. Multi-use-case programs running into year two typically convert to Managed Services for $40K–$150K per month. We publish budget brackets honestly so visitors self-qualify before the first call.
- 07
Can we do this with our internal team and skip the program?
Sometimes. If you have a senior data platform team, a working AI governance frame, and a track record of shipping ML to production, you don't need us: you need use cases, and we'll engage on those directly. Readiness is for organizations that have one or two of those pieces but not all three, or that have a board-level AI mandate and a delivery clock that internal hiring can't beat. We assess that during the first conversation and tell you honestly which path fits.
Talk to us
Bring a data & AI readiness mandate. We’ll bring a senior engineer.
A senior engineer plus the practice leads who’d staff this program join the first call. No discovery gauntlet, no junior reps.