AI Data Migration Services
Turn messy PDFs, spreadsheets, CRMs, ERPs and documents into clean, validated, vectorized knowledge your AI can actually use.
Last updated: 2026-07-16. Provider: SnapSiteBuild AI Centre of Excellence — founder Krish Chimakurthy.
AI data migration is the work of turning your existing business data — documents, spreadsheets, databases, and CRM and ERP records — into clean, structured, vectorized knowledge that an AI system can retrieve and reason over reliably. It is not a straight database lift-and-shift: the target is not another table but an embedding index plus governed metadata that grounds a model in your facts. SnapSiteBuild runs it as a measured pipeline — discovery, cleaning, field mapping, parsing, vectorization, reconciliation and governance — so what reaches your AI is complete, accurate and safe to answer from.
TL;DR. We move legacy and live data into AI-ready form through a structured pipeline, load it into a vector database (Qdrant, pgvector or Weaviate), and prove completeness with reconciliation testing before anything goes live.
- What: AI data migration and RAG data preparation — legacy data to AI, data cleaning for AI, embeddings migration and vector database migration.
- How: discovery → extraction & document parsing → cleaning → field mapping → chunking & metadata tagging → vectorization → vector DB load → reconciliation → governance.
- Why it matters: retrieval-augmented AI is only as trustworthy as its source data — dirty, duplicated or mis-mapped data produces wrong, ungrounded answers.
- Output: a governed vector knowledge base, a source-to-target data map, and a reconciliation report you sign off.
- Next step: a short scoping call — tell us about your data sources.
Why AI needs clean, prepared data
"Garbage in, garbage out" is sharper for AI than for any reporting system. A RAG knowledge base retrieves chunks of your data at query time and lets the model answer from them — so errors in the source become confident, well-written wrong answers for a customer or employee. Data cleaning for AI exists to stop that at the root.
The failures we prepare against are specific:
- Duplicates and near-duplicates that let the model cite the same fact in three slightly different versions.
- Stale, superseded records — an old price, an expired policy — with nothing marking them out of date.
- Contradictions across systems, where the CRM and ERP disagree and the model picks one at random.
- Unstructured noise — scan artefacts, headers, footers, boilerplate — that crowds out the real answer.
- Unredacted PII and secrets that should never enter a searchable index.
The AI data migration pipeline: stage by stage
Migration is a repeatable pipeline, not a one-off export. Each stage has a defined output that the next stage depends on, which is what makes the result auditable and the completeness provable. We tune the exact tools and thresholds to your sources, data sensitivity and the AI use case.
| Pipeline stage | Output it produces |
|---|
| Discovery & data inventory | Source inventory, data map, PII/sensitivity classification, scope and risk register |
| Extraction & document parsing | Raw text and structure pulled from PDFs, Office files, HTML, scans (OCR) and database exports |
| Cleaning & normalization | De-duplicated, de-noised records with consistent dates, units, encodings and casing |
| Field mapping & schema alignment | Source-to-target field map, a unified schema, and documented transformation rules |
| Chunking & metadata tagging | Right-sized text chunks tagged with source, owner, date, permissions and topic |
| Vectorization (embeddings) | Embeddings generated by a chosen model, paired with their metadata payloads |
| Vector DB load & indexing | Indexed collections in Qdrant, pgvector or Weaviate, ready for hybrid search |
| Reconciliation & validation | Completeness and accuracy report, retrieval evaluation scores, and sign-off |
| Governance & handover | Access controls, data lineage, a refresh pipeline and an operations runbook |
How we run the pipeline
The table shows the shape; the detail below is where data migration services succeed or quietly fail.
Discovery and document parsing
We start by inventorying every source — databases, shared drives, wikis, email exports and ticket archives — and classifying each for sensitivity before a byte moves. Extraction then pulls content from its container: native text from databases and spreadsheets, structured text from PDFs and Office files, and OCR for scanned files, preserving headings, tables and structure so meaning survives the move.
Cleaning, field mapping and metadata tagging
Cleaning de-duplicates, removes boilerplate and noise, and normalizes formats so the same fact reads the same way everywhere. Field mapping aligns each source field to a unified target schema with explicit transformation rules — the step that reconciles a CRM's account_name with an ERP's customer. Every chunk is then tagged with metadata: source system, document owner, effective date, access permissions and topic, which powers filtered, permission-aware retrieval rather than flat keyword search.
Vectorization and the vector database
Vectorization converts each cleaned, tagged chunk into an embedding — a numerical representation of its meaning — using an embedding model chosen for your language, domain and privacy needs. Where data cannot leave your boundary, we run open embedding models on your own local LLM infrastructure so nothing is sent to a third party. The embeddings and their metadata are loaded into a vector database and indexed for fast similarity and hybrid search. We commonly deploy one of three:
- pgvector — when you already run PostgreSQL and want vectors to live beside your relational data with no new system to operate.
- Qdrant — a purpose-built vector database with strong metadata filtering and payload handling, ideal for large, permission-aware collections.
- Weaviate — when you want built-in hybrid (keyword + vector) search and a flexible schema across mixed content types.
Choosing one is a vector database migration in its own right — collection design, distance metric, index parameters and a re-embedding plan all have to be decided deliberately, because re-embedding later means regenerating every vector.
Reconciliation testing: proving completeness
A migration is done not when it runs without errors but when you can prove nothing was lost or corrupted. Reconciliation compares source and target by record counts and checksums, samples records for field-level accuracy, and confirms sensitive data was redacted as required. On top of that we run retrieval evaluations as part of AI testing and QA — a labelled set of real questions checked for whether the right chunk is retrieved and stays grounded — so completeness is measured by AI behaviour, not just row totals.
Governance and ongoing refresh
Data is not static, so the pipeline ships with governance: documented lineage from every chunk back to its source, role-based access so retrieval respects who may see what, and a scheduled refresh that re-ingests changed records and retires stale ones. Where an enterprise AI gateway sits in front of your models, we wire PII redaction and audit logging into the same boundary, so the controls that protected the data during migration keep protecting it at query time.
Sources we migrate: CRM, ERP, documents and databases
Most organisations do not have one source — they have a dozen. We routinely migrate from CRM platforms (such as Salesforce, HubSpot or Dynamics), ERP and finance systems (such as SAP, NetSuite or Oracle), SQL and NoSQL databases, document stores and SharePoint, knowledge wikis, support and ticketing systems, and shared drives. CRM and ERP data is especially valuable and especially messy: it is structured but full of duplicate accounts, free-text notes and inconsistent codes, so it needs careful field mapping and de-duplication before it becomes reliable AI context. Once migrated, that knowledge becomes the foundation for AI agents that answer from your records instead of guessing.
Who runs your migration — and why trust it
SnapSiteBuild is an AI Centre of Excellence founded by Krish Chimakurthy, who brings close to two decades of IT delivery, software testing and project management alongside hands-on AI engineering. Data migration is where that heritage pays off: it is a testing-and-traceability discipline, and we treat it as one — documented data maps, reconciliation evidence and clear sign-off gates.
What makes the approach different is that the proof is built in, not promised. We do not publish invented client logos or accuracy figures; we report your migration's measured reconciliation results against thresholds you approve, and you keep every artefact. A typical engagement leaves you with:
- A source inventory and source-to-target data map with transformation rules.
- A cleaned, de-duplicated dataset with PII classified and handled.
- A loaded, indexed vector database ready for retrieval.
- A reconciliation and retrieval-evaluation report you sign off.
- A governance runbook and a refresh pipeline for keeping data current.
Migration runs as one track within SnapSiteBuild's broader AI services, delivered with the same discipline as the rest of an AI project — so the data layer is production-grade, not a side experiment.
Frequently asked questions
- Why does AI need clean data instead of raw exports?
- Because retrieval-augmented AI answers directly from whatever it retrieves. Duplicates, stale records, contradictions and noise all surface as confident, well-phrased wrong answers. Cleaning, de-duplication, normalization and metadata tagging remove those failure modes before the data is embedded — the single biggest driver of grounded, trustworthy AI output.
- What is the AI data migration pipeline?
- A staged process: discovery and inventory, extraction and document parsing, cleaning and normalization, field mapping to a unified schema, chunking and metadata tagging, vectorization into embeddings, loading into a vector database, reconciliation testing, then governance and handover. Each stage has a defined output the next depends on, so the result is auditable and completeness is proven, not assumed.
- Which vector databases do you use — Qdrant, pgvector or Weaviate?
- All three, chosen to fit your stack. pgvector keeps vectors inside an existing PostgreSQL database with nothing new to run; Qdrant is a dedicated vector engine with strong metadata filtering for large, permission-aware collections; Weaviate offers built-in hybrid keyword-plus-vector search across mixed content. We pick the index type, distance metric and collection design around your volume, latency and privacy needs.
- How do you validate that the migration is complete?
- With reconciliation testing. We compare source and target by record counts and checksums, sample records for field-level accuracy, and confirm PII was redacted as required. We also run retrieval evaluations on a labelled question set to check the right content is retrievable and stays grounded. You receive a completeness and accuracy report and sign off before go-live.
- Can you migrate data from our CRM and ERP systems?
- Yes. We routinely migrate from CRM platforms like Salesforce, HubSpot and Dynamics and from ERP and finance systems like SAP, NetSuite and Oracle, alongside databases, document stores, wikis and ticketing systems. CRM and ERP data needs particular care — duplicate accounts, free-text notes and inconsistent codes — so field mapping and de-duplication are central to making it reliable.
- What are embeddings, and what happens when the model changes?
- An embedding is a numerical representation of a chunk's meaning, produced by an embedding model, that lets the vector database find content by similarity rather than exact keywords. Embeddings are tied to the model that created them, so upgrading the model means an embeddings migration — regenerating every vector and re-indexing. We plan for that up front so a future model change is a routine refresh, not a rebuild.
Ready to make your data AI-ready?
If you have years of documents, spreadsheets, CRM and ERP records you want AI to answer from accurately, we will scope a migration pipeline and reconciliation plan around your sources. Contact SnapSiteBuild to get started.
Contact SnapSiteBuild: Customersupport@snapsitebuild.com