47 assessments completed · 38+ enterprises served · average 2-week delivery
Get complete AI data readiness services before you build
See exactly where your data is ready and where it isn't. We deliver a structured, engineering-ready action plan in 2 weeks, before you commit budget to a build.
Assessment sprint
2
week sprint-ready action plan
- Data source registry
- Quality scorecard per dataset
- Pipeline architecture diagram
- Governance readiness matrix
- Prioritized action list for your use case
The cost of skipping a data readiness check
Teams that start building AI without assessing their data spend 30 to 50 percent of project budget on rework. Here is what that pattern looks like compared to starting with a clear data assessment.
- AI project starts with unmapped data sources and unknown quality
- Critical data gaps surface 3 sprints in, forcing scope changes
- Data engineers rebuild pipelines mid-build, burning 6 to 10 weeks
- AI models trained on inconsistent, incomplete data underperform expectations
- Project overruns budget by $40,000 or more in avoidable rework costs
- Full inventory of data sources, schemas, and access paths before Sprint 1
- Every data gap documented and prioritized before any development starts
- Pipelines validated and documented so engineers build on a solid foundation
- Clean, correctly labeled data available on day one of your AI build
- Assessment costs a fraction of one wasted sprint, saving weeks of delivery time
Score your AI data readiness across 5 dimensions
Work through the items below. Your readiness score updates in real time. This is the same framework our consultants use on day one of every engagement.
Your readiness snapshot
0 of 20 checkpoints complete
Five dimensions in this checklist
- Data availability
- Data quality
- Infrastructure
- Governance
- AI enablement
Select any item in the cards to update your score and unlock a tailored recommendation with a direct next step.
This checklist is a directional self-assessment. A full engagement goes deeper into each dimension with hands-on data profiling, pipeline testing, and stakeholder interviews.
Four structured work streams. One sprint-ready report.
Data source inventory and access mapping
We enumerate every data source relevant to your AI use case. Every table, application programming interface, file store, and event stream gets catalogued with schema, freshness, access path, and volume.
You receive a structured asset register your engineering team can use from day one of development, not a slide with bullet points.
Data quality profiling and scoring
We profile every priority dataset against four dimensions: completeness, consistency, uniqueness, and timeliness. Every issue is given a severity rating and a remediation recommendation.
You know which datasets are safe to use today and which need targeted cleaning before your AI build can rely on them.
Pipeline architecture review and gap analysis
We map how data moves from source to storage to consumption. We identify missing pipeline stages, transformation bottlenecks, and latency issues that would block your AI use case.
You get a visual pipeline map and a prioritized list of infrastructure changes, sorted by impact on your target AI deliverable.
| Control area | Status | Action |
|---|---|---|
| Data ownership registry | Active | None |
| Access control policy | Partial | Expand to AI layers |
| GDPR / CCPA compliance | Active | None |
| Data lineage tracking | Missing | Implement dbt docs |
| AI bias / fairness audit | Not started | Add to Sprint 2 |
Governance and compliance readiness mapping
We audit your data governance posture against AI deployment requirements: ownership, access controls, lineage, GDPR or CCPA compliance, and bias risk surface.
You get a governance matrix showing what is in place, what blocks AI deployment, and what can be resolved before or alongside the build.
Assessment fee: $4,500 flat · 2-week sprint · Full written report delivered
One undiscovered pipeline gap can cost 4 to 6 weeks of rework. At typical development rates that is $40,000 or more. This assessment costs a fraction of one wasted sprint.
Your data pipeline, visualized in the report
Every assessment delivers a visual pipeline diagram showing where your data flows, where it stalls, and where it connects to your AI use case. This is the diagram your engineers build from.
What ai data readiness consulting unlocks in practice
Enterprise apparel and retail organization
Apparel & RetailA global apparel and retail enterprise operating across multiple enterprise resource planning systems with fragmented customer analytics and revenue operations.
Customer payment behavior data was fragmented across three enterprise resource planning systems with no unified view. Revenue recovery efforts were reactive and poorly targeted because no one knew which accounts were high risk until it was too late.
Reporting took 40 hours per cycle because data was manually reconciled across sources. Teams lacked confidence in the numbers they were using to make decisions.
After a structured data readiness assessment and centralized analytics build, the team reduced reporting time by 40% and achieved a 35% improvement in recovery targeting accuracy.
Why teams choose our enterprise AI data readiness assessment over generalist consultancies
Hands-on data profiling
We run SQL, profile schemas, and pull samples. You get numbers, not opinions.
Use-case specific scope
We assess data against your specific AI use case, not a generic maturity framework.
Sprint-ready deliverable
The report includes an action-sorted issue list your team can execute from immediately.
2-week delivery, flat fee
$4,500 flat, always completed in 2 weeks. No billable-hour surprises.
Assessment leads directly to build
Teams that complete the assessment can roll straight into an AI data engineering sprint without re-scoping.
Maturity model presentations
Abstract frameworks and heat maps. Rarely any hands-on data work in the assessment itself.
Generic data strategy scope
Assessments evaluate the whole data estate. The output isn't tied to your specific AI goal.
Recommendations decks only
Slide-based output. Difficult to translate into a development backlog without further work.
6 to 12 week timelines
Enterprise delivery pace. Often still billable-hour pricing with scope creep risk.
Assessment is a separate engagement
The assessment is sold separately from the build. You re-scope and re-staff before anything gets built.
Questions about the assessment
Answered directly. No pitch language.
An AI data readiness assessment is a structured 2-week sprint that evaluates whether your current data infrastructure can support a specific AI use case. We examine your data sources, quality, pipelines, governance posture, and AI enablement signals. You receive a written report that tells you exactly what is ready, what needs fixing, and in what order to act.
A general AI readiness assessment covers your organization's overall preparedness for AI: technology, people, processes, and strategy. This assessment focuses specifically on your data: sources, quality, pipelines, and governance. The output is an engineering-ready action list rather than a strategic roadmap.
We cover any data source relevant to your target AI use case: relational databases, NoSQL stores, data warehouses (Snowflake, BigQuery, Databricks), streaming platforms (Kafka, Pub/Sub), customer relationship management and enterprise resource planning exports, file stores, and external application programming interface feeds. If you aren't sure which sources are relevant, we help you identify them in the kickoff call. See our AI data engineering services for full infrastructure support.
You receive a written assessment report including: a full data source registry, a quality scorecard per dataset, a visual pipeline architecture diagram, a governance readiness matrix, and a prioritized action list sorted by impact on your AI use case. Every finding includes a specific, actionable recommendation your engineering team can begin executing immediately.
Yes. Teams that complete the assessment can roll directly into an AI data engineering sprint to resolve the gaps identified. Because the assessment produces a structured action list, there is no re-scoping phase. You continue immediately with the same team on a clear build plan. This is how we collapse the time between assessment and production AI delivery.
Not at all. Many teams run this assessment after Sprint 1 or 2 when they suspect data issues are limiting model performance. A mid-build data assessment identifies exactly which data problems are degrading your outputs and gives you a clear remediation path.
Is our ai readiness consulting right for you?
- You have a specific AI use case in mind and need to know if your data can support it
- Your organization has data in multiple systems that has never been fully mapped or profiled
- Leadership is asking for proof that data is ready before approving an AI build budget
- A previous AI project stalled because data wasn't ready and you want to avoid repeating that
- You want to connect this assessment directly to an AI data engineering or AI consulting sprint
- You have no specific AI use case yet and need strategy and discovery work first. Start with an AI discovery workshop.
- You need governance, risk, and compliance evaluation across your entire AI program. See our AI governance and risk assessment.
- You have no data at all and need greenfield data collection and infrastructure built first
- You need a broad enterprise data strategy engagement covering all business units and systems
Not sure? Tell us your situation and we'll be straight with you.
Know what your data needs before you build
Submit your brief and get a scoped plan for our ai data readiness services within 3 business days. No commitment. No pitch. Just a clear picture of your data and a sprint-ready action list.
Assessment fee: $4,500 flat · 2-week sprint · Full report delivered
Submit your brief
Brief received
We'll review your data situation and send a scoped assessment plan within 3 business days. Expect a call from our team within 48 hours.