Data Pipelines for AI and RAG
A data pipeline for AI moves documents and records from your systems into a clean index that language models can search, with access rules and scheduled refresh. Remolda builds one production pipeline in six weeks for $9,800 CAD + HST.
In short
- Price: $9,800 CAD + HST for the six-week AI Pilot Sprint: one production pipeline from your sources to an AI-ready index or dataset.
- Covers the full path: extract, clean, remove duplicates, mask personal information, chunk, embed, index, refresh.
- Access rules travel with the data, so an AI assistant shows each user only what they may see.
- Built on your cloud: Azure AI Search, Microsoft Fabric or Data Factory, AWS services, or PostgreSQL with pgvector.
- Quality checks run on every refresh; failures alert the owner and stop bad data from reaching the model.
Your situation
Pipeline work starts when the data behind an AI tool is scattered, stale or open to everyone. Typical cases:
- The assistant gives outdated answers. Old versions of policies sit next to current ones.
- Documents live in ten places. SharePoint sites, shared drives, email attachments, a legacy system.
- Access rules get lost. Once content is copied into an index, everyone can see everything.
- Every refresh is manual. Someone re-exports files when they remember.
What the pipeline does
| Stage | What happens | Check |
|---|---|---|
| Extract | Pulls documents and records from sources on a schedule | Counts compared with the source |
| Clean | Removes duplicates and old versions, fixes formats, runs OCR | Rejected items logged |
| Protect | Masks personal information the use case does not need | Sample reviewed |
| Chunk and embed | Splits into passages, creates embeddings | Retrieval tested on real questions |
| Index | Stores in a search or vector index with access metadata | Permission tests per user group |
| Refresh | Updates only what changed | Alerts on failure |
The same pipeline feeds an internal AI assistant, an AI feature in your product through LLM API integration, or structured datasets for dashboards and predictive analytics.
What the AI Pilot Sprint includes
Source inventory. What exists, where, who owns it, which versions are current.
One pipeline in production. From your sources to an index or dataset in your cloud.
Retrieval test set. Real questions with expected source passages, scored on every change.
Privacy documentation. Fields masked, index location, retention; Law 25 s. 3.3 calls for a privacy impact assessment when an information system handling personal information is acquired, developed or overhauled.
Runbook and monitoring. Refresh schedule, alerts, how to add a source.
How long it takes
Six weeks from kickoff. In our experience the usual split is two weeks for access and inventory, two weeks for build and retrieval testing, and two weeks of scheduled runs with fixes. Source system access and document ownership questions set the pace.
What it costs
The AI Pilot Sprint is $9,800 CAD + HST, fixed, for one pipeline. Cloud and embedding usage are billed by the vendor. For hosting and data residency choices, see private AI in Canadian cloud regions. All packages are on the pricing page.
Why Remolda for AI data pipelines
- Built for retrieval quality. Tested on real questions.
- Permissions preserved. Access rules move with the data.
- Privacy by design. Masking and documentation in the pipeline.
- Your stack. Azure, AWS or PostgreSQL, in your account.
- Fixed price. One pipeline, six weeks.
How the work runs
The Sprint covers Audit and Implement in the Remolda Cycle (Audit → Strategy → Implement → Empower → Evolve); new sources are added in Evolve.
- Scope call. 30 minutes: sources, use case, cloud.
- Inventory and access. Owners and current versions confirmed.
- Build. Pipeline and index with quality checks.
- Scheduled runs. Retrieval tests on each refresh.
- Review. Retrieval scores and freshness, then go / adjust / stop.
Frequently asked questions
What is a data pipeline for AI?
It is the automated process that takes data from source systems, cleans and structures it, and delivers it in the form an AI system needs: a search or vector index for retrieval, or a prepared dataset for analytics and prediction. It runs on a schedule and checks quality each time.
What is a RAG pipeline?
RAG, retrieval-augmented generation, gives a language model relevant passages from your own content at question time. The RAG pipeline prepares that content: it splits documents into passages, creates embeddings, stores them in an index and keeps them up to date.
How much does a data pipeline for AI cost?
Remolda's six-week AI Pilot Sprint is $9,800 CAD + HST for one production pipeline with quality checks, documentation and handover. Cloud services and embedding usage are billed by the vendor.
Do we need a vector database?
For retrieval over many documents, usually some form of vector or hybrid search. It can be a managed service such as Azure AI Search, or PostgreSQL with the pgvector extension you already run. We choose by volume, cost and your stack.
How do you handle personal information?
Fields that the AI use case does not need are removed or masked in the pipeline. What remains is documented under PIPEDA and, for Quebec, Law 25, including where the index is stored.
Which sources can you connect?
SharePoint and OneDrive, file shares, databases, CRMs, ERPs, ticketing systems and most tools with an API or export. Scanned documents go through OCR first.
How long does it take?
Six weeks from kickoff. In our experience, source access and agreement on which documents are current take the first two weeks.
Sources
- Microsoft Learn — Retrieval-augmented generation in Azure AI Search
- Microsoft Learn — What is Azure Data Factory?
- Microsoft Learn — What is Microsoft Fabric?
- LégisQuébec — Act respecting the protection of personal information in the private sector (P-39.1), s. 3.3
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