AI Data Readiness Assessment Services
Data readiness assessment for generative AI and RAG. Examine quality, access, provenance and coverage before choosing ingestion and retrieval approaches.
The fastest way to fail at AI is to build the model before you understand the data.
Unstructured data assumed to be ready because it exists
Terabytes of documents, logs, and records sitting in storage. Nobody has cleaned, organized, or validated any of it. The assumption is that having data means being ready. It doesn't.
Inconsistent formats that break retrieval
Documents with different naming conventions, conflicting schemas, and metadata that was applied by different people at different times. RAG systems built on this retrieve the wrong things confidently.
Labels applied inconsistently across time
Classification models trained on data where the labeling criteria changed midstream, or where different teams applied different standards. The model learns the inconsistency, not the pattern.
No ground truth for evaluating model outputs
Teams building AI systems with no way to measure whether the output is correct. No reference dataset. No evaluation criteria. No way to know if the system is working or just generating plausible-looking answers.
Data siloed across systems
Critical information spread across databases, SaaS tools, file shares, and email threads in formats that are expensive to unify. The AI system needs a coherent view. The data doesn't provide one.
Works in the demo, fails in production
The demo used clean, curated data. Production has messy, incomplete, real-world data. This is a data readiness problem disguised as a model problem. The model didn't change. The data did.
We assess your data against the specific requirements of the AI system you're building.
Different use cases have different data requirements. A classification model needs different things from a RAG system. We don't apply a generic checklist. We evaluate what your data needs to do for this particular system.
Evaluate five dimensions
Completeness, quality, recency, consistency, and representativeness. Each assessed against the specific use case, not against abstract standards. The question is whether your data is good enough for this system.
Identify gaps and their actual cost
We tell you what's missing and what it takes to close each gap. More importantly, we tell you whether the gap is worth closing or whether you should change the approach entirely.
Build pipelines that keep data clean
Where cleanup and preparation is needed, we do it. We also build the pipelines so your data stays clean as it evolves. Data readiness is not a one-time fix. It's an ongoing requirement.
Fix the data or change the approach. That distinction is what separates this from a data audit.
An honest picture of your data reality.
A clear understanding of what your AI system can and can't do given your actual data. Not what's theoretically possible with perfect data. What's achievable with what you have now.
No failed projects because the foundation was rotten.
Data quality assessment
Five dimensions evaluated against your specific AI use case. Completeness, quality, recency, consistency, representativeness. Each scored with clear criteria, not subjective opinion.
Gap analysis with remediation paths
Every gap identified. For each: what it takes to close, how long, what it costs, and whether closing it is the right move or whether the approach should change.
Data pipeline recommendations
How to keep data clean as it evolves. Ingestion, validation, and monitoring patterns designed for your specific systems and data sources.
Results
Use-case specific
Completeness, quality, recency, consistency, and representation are tested against the intended decision.
Traceable
Sources, ownership, permitted use, transformations, labels, and evaluation sets have documented provenance.
Actionable
Each gap ends with a remediation option, owner, validation method, and effect on the use-case decision.
Decision evidence
The written assessment distinguishes observed data conditions from assumptions and names the evidence, remediation, owner, and retest needed for each gap.
Related services
AI Readiness & Feasibility
Before data readiness comes the bigger question: is AI the right approach for this problem? A frank assessment of what's achievable.
Use Case Validation
You've identified the use case. Test whether it works with real data, real failure modes, and real consequences before committing the budget.
LLM & RAG Systems
When the data is ready and the use case is validated. Production LLM and retrieval systems built on solid data foundations.
Frequently asked questions
What is a data readiness assessment for AI?
A review of whether your data is available, accurate, accessible and governed well enough to support the AI use cases you want.
What data issues commonly block AI projects?
Missing or inconsistent data, siloed systems, unclear ownership, poor documentation, access restrictions and privacy concerns.
Is our unstructured data ready for generative AI and RAG?
We check document quality, duplication, permissions, formats and freshness, since these strongly affect answer accuracy.
What do we receive from a data readiness assessment?
A data quality summary, gaps against target use cases and a prioritised remediation plan.
Do we need a data warehouse before starting AI?
Not always. Some use cases can start with targeted data pipelines, while broader programmes benefit from a stronger data foundation.