Data Preparation for AI
Data preparation services for AI: profile, clean, standardize, and structure business data for a defined AI, analytics, or retrieval use case.
Typical timeline: 2 to 4 weeks for a focused source pilot and 3 to 6 months for a multi-source production preparation pipeline, depending on scope. No fixed public price. We agree sources, quality targets, deliverables, access, schedule, and fee before work begins.
Overview
AI systems depend on data that is accurate, complete, consistent, current, relevant, and understandable in context. Our data preparation service identifies what a specific AI use case needs, profiles the selected sources, and fixes or documents the gaps that could make the results unreliable. Work may include deduplication, normalization, validation, document structuring, metadata, and repeatable preparation pipelines. We test the prepared data against representative questions or tasks and hand over the changes, quality checks, assumptions, and source references. Preparation is scoped to the sources required for the agreed use case, not an automatic migration of your entire data estate.
What We Offer
We begin with the intended AI use: what the system must find, extract, compare, summarize, or analyze, and how the team will judge whether its information is fit for that task. We then inventory the approved data sources, formats, owners, update frequency, access rules, and known constraints. Profiling checks dimensions such as accuracy, validity, completeness, consistency, uniqueness, timeliness, relevance, and missing context. The work may clean duplicates, normalize labels and units, standardize fields, resolve format differences, identify obsolete or conflicting records, structure documents, add source and date metadata, and create a repeatable transformation or retrieval-preparation pipeline. We preserve source references and document transformations so the team can understand where prepared information came from. Quality gates and representative cases help verify that the prepared data meets the agreed acceptance criteria. Data preparation does not guarantee that a model will be accurate or that an AI product will be production-ready. Broad system connections belong to AI Systems Integration; AI application and retrieval-system development belong to Custom AI & Agent Development; enterprise-wide data governance and ongoing AI controls belong to AI Governance & Operations.
Key Capabilities
Data profiling and readiness assessment for a defined AI use case
Cleaning, deduplication, normalization, and validation
Document structure, metadata, context, and source references
Repeatable data preparation and quality-check pipelines
Business Value
Tangible outcomes that matter to your business.
Find data quality gaps before they undermine AI results
Make approved information easier to retrieve, compare, and validate
Give delivery teams traceable data changes and clear quality criteria
Ideal Use Cases
Prepare an approved knowledge library for retrieval or question answering by organizing authoritative documents, versions, access metadata, and source references. Standardize CRM, ERP, finance, support, or operations records used by an AI assistant or workflow. Normalize inconsistent supplier, product, customer, or transaction fields before analysis. Structure forms, PDFs, policies, manuals, and other unstructured sources so an AI system can identify relevant sections and retain their context. The work is most useful when the intended AI task and source boundaries can be agreed; it is not a substitute for deciding which business problem to solve.
Outcomes we drive
An inventory of the approved data sources in scope
A data profile with prioritized quality and context gaps
Cleaned, normalized, deduplicated, or structured data for the agreed task
Document and record metadata, provenance, and transformation rules
Repeatable preparation steps and data quality checks where required
Validation evidence and a handover plan for data ownership and upkeep
Our Methodology
Understand the AI task, prepare the right sources, and verify what changed.
Define the AI task and acceptance criteria, inventory approved sources, profile quality and context, prioritize preparation, apply documented transformations, validate against representative cases, then hand over source lineage, checks, and upkeep responsibilities.
Impact & economics
What you can expect before you commit.
Quality baseline
Profiled before preparation
Record the selected measures, such as completeness, validity, consistency, duplicates, freshness, and context coverage.
Preparation checks
Agreed quality gates
Document expected formats, source references, transformation rules, and checks for the chosen AI task.
Use-case validation
Tested on representative tasks
Review whether the prepared data supports the agreed questions or tasks and report remaining gaps.
Engagement options
Time-boxed, owner-assigned, cost-aware.
Data Readiness Pilot
2 to 4 weeks, scope dependentProfile and prepare one priority data source for a defined AI use case, then validate it against representative tasks before deciding what to do next.
Deliverables
Source inventory, data profile, prioritized quality fixes, prepared sample, agreed quality checks, and validation findings.
Production Data Preparation
3 to 6 months, scope dependentPrepare the agreed sources and repeatable data pipelines needed for production AI use across one or more defined use cases.
Deliverables
Prepared and structured data, documented transformations, repeatable preparation pipeline where required, quality gates, validation evidence, and handover.
Run & Scale
Ongoing, scoped monthlyMaintain agreed data preparation pipelines, review quality and freshness issues, and make separately scoped updates as sources change.
Deliverables
Agreed quality monitoring, pipeline maintenance, issue handling, source refreshes, and documented improvements.
Illustrative data preparation example
Example onlyExample: preparing an internal policy library for AI search
- Inventory: identify approved policy sources, owners, effective dates, revisions, duplicate copies, and access restrictions.
- Preparation: remove superseded copies only with owner approval, structure documents by meaningful sections, add source and version metadata, and retain links to the original material.
- Validation: test representative employee questions against the approved source set, review whether the right current passages can be found, and record unresolved gaps. This example is illustrative, not a client project or measured result.
Risk & compliance
Prepare only approved sources and use access permissions agreed with the data owner.
Keep source references and document transformations so changes can be traced and reviewed.
Test for stale, incomplete, conflicting, duplicated, sensitive, and out-of-scope information before use.
Do not silently invent missing values or make business decisions about conflicting records; surface assumptions for owner review.
Is this a fit?
Clarity before you commitGood fit
- You have a specific AI, analytics, or retrieval task and can identify the information it needs.
- You can provide approved access to source data and a subject matter owner for questions about quality and meaning.
- You want measurable quality criteria, traceable transformations, and a useful handover.
Not a fit
- You want an AI-ready certification or guaranteed model accuracy without defined tests.
- You need a company-wide migration, replacement data platform, or data estate modernization in a narrow preparation engagement.
- No one can confirm which sources are authoritative or approve how conflicting records should be resolved.
After the engagement
We don’t leave you hangingThe handover records sources, quality checks, transformations, limitations, and data owners.
Run & Scale can maintain agreed preparation pipelines and review source freshness or quality failures.
Re-profile sources when their structure, ownership, update frequency, or AI use changes materially.
Key Deliverables
Use-case and source register showing the data required, owners, formats, access, and update needs
Data profile covering agreed quality dimensions, examples, risks, and gaps
Prioritized preparation plan with acceptance criteria and out-of-scope sources
Cleaned, deduplicated, normalized, or structured data within the agreed scope
Document structure and metadata rules for context, version, date, and source identification
Repeatable transformation or retrieval-preparation pipeline when required
How We Work
Seven steps from use-case definition to traceable handover.
What to expect
- Agree the AI task, approved data sources, owner decisions, and quality acceptance criteria.
- Keep source lineage, transformations, assumptions, checks, and limitations documented.
- Receive prepared data, validation evidence, and clear ownership for future updates.
Define the AI use case
Agree what the AI system needs to know or do, the required output, the representative cases, and the quality criteria for accepting prepared data.
Inventory approved sources
Record the systems, files, formats, owners, permissions, authoritative versions, update frequency, and known source constraints in scope.
Profile data quality and context
Inspect accuracy, validity, completeness, consistency, duplicates, timeliness, relevance, and missing context. Mark evidence, assumptions, and gaps separately.
Prioritize preparation work
Rank the gaps by their effect on the agreed AI task. Confirm transformation rules, data boundaries, quality gates, and what will remain out of scope.
Clean and structure the sources
Apply agreed cleaning, normalization, deduplication, document structuring, metadata, source references, and repeatable preparation steps.
Validate against real examples
Check quality gates and test representative questions or tasks, including stale, incomplete, conflicting, duplicated, or hard-to-find information.
Document and hand over
Provide prepared outputs, source and transformation records, known limitations, quality checks, named data owners, and a plan for agreed upkeep.
Frequently Asked Questions
Click to expand answers01What does data preparation for AI include?+
02Which data quality problems can you address?+
03Can you prepare documents and other unstructured data?+
04Do you connect directly to our CRM, ERP, or data platforms?+
05Do you migrate or clean the entire company data estate?+
06Will clean data guarantee accurate AI answers?+
07How long does data preparation take, and what does it cost?+
08Who maintains data quality after handover?+
Is your data ready for the AI use case you have in mind?
Tell us what the AI system needs to know or do, which data sources it should use, and what makes an answer or result trustworthy. We will help scope the data profile, preparation work, and validation criteria before building.