AI-Ready Data Services: RAG Pipelines, Embeddings and Feature Stores
Get your documents and data clean, searchable and safe so AI gives correct answers.
Stack we work in
- Python
- LangChain
- Hugging Face
- Milvus
- Unity Catalog
- Apache Airflow
- Apache Kafka
- PostgreSQL
- Projects delivered
- 680+
- Clients served
- 310+
- Client retention
- 98%
- Certified engineers
- 20+
- Building since
- 2018
Figures as of Oct 2026
Trusted by
- Aralia
- Rosa Pistrina
- Lashbo
- Micromunchi
- Payroll Ninja
- POD
- Petdx
- Yokey
- Spin Up
- Starwear
- Udlins
- Vegpal
- Wander Wings
- Wish It
- Wisl
Signs your data is not ready for AI
AI-ready data services prepare your documents and data so AI assistants, agents and models give accurate answers and stay within your access rules.
Read the full overview
Most AI projects stall on data, not models: files scattered across SharePoint and drives, scanned PDFs, duplicated records and no control over who may see what. We build document ingestion and parsing pipelines, chunking and embedding pipelines, and vector indexes in pgvector, Pinecone, Weaviate, Qdrant, Databricks Vector Search or Snowflake Cortex Search for retrieval-augmented generation (RAG). For machine learning we build feature stores and training datasets. Permissions, PII handling, lineage and freshness are designed in, so AI only sees what each user is allowed to see.
The demo worked, production did not
An AI pilot looked good on ten documents but gives poor answers on the full library.
Answers from old documents
The assistant quotes policies or prices that changed months ago.
Access rules ignored
Nobody is sure whether the AI could show salaries or client files to the wrong person.
Documents locked in scans
Key knowledge sits in scanned PDFs and spreadsheets that the AI cannot read properly.
No way to measure accuracy
People argue about whether answers are good, with no test set or scores.
Outcomes
What you get
- Your documents and data searchable by AI with sources cited
- AI answers limited to what each user is allowed to see
- Personal data detected and masked before it reaches a model
- Indexes that stay in sync as documents change
- Measured retrieval accuracy, not guesswork
What we deliver
What you receive
AI data readiness report
Sources, quality, access rules and gaps for each planned AI use case.
Ingestion pipelines
Connectors, parsing and incremental sync for your document and data sources.
Vector indexes
Chunked, embedded and indexed content with metadata and permissions attached.
Retrieval evaluation set
Test questions, expected sources and accuracy scores you can rerun after every change.
Governance controls
Permission-aware retrieval, PII masking, lineage and audit logs.
Feature store (where needed)
Shared, versioned features for machine learning models.
Document ingestion
- Connectors for SharePoint, Google Drive, Confluence, websites and databases
- Parsing PDFs, scans, tables and images with OCR and layout models
- Cleaning, deduplication and metadata extraction
- Incremental sync so new and changed documents are picked up
RAG & vector pipelines
- Chunking strategies tuned to your content
- Embedding pipelines with OpenAI, Cohere, Voyage or open-source models
- Vector search on pgvector, Pinecone, Weaviate, Qdrant or Elasticsearch
- Hybrid keyword and vector search with reranking
- Retrieval evaluation with test questions and accuracy scores
Built with
- pgvector
- Pinecone
- Weaviate
- Qdrant
- Elasticsearch
Feature stores & ML data
- Feature stores with Feast, Databricks or SageMaker Feature Store
- Training and evaluation datasets with versioning
- Point-in-time correct features to avoid data leakage
- Real-time features from streaming data
Built with
- Feast
Governance for AI
- Permission-aware retrieval that respects source access rights
- PII detection, masking and redaction
- Lineage from answer back to source document
- Freshness, quality and drift monitoring
- Policies aligned with the EU AI Act and privacy law
- 01IngestBatch, CDC and streams
- Apache Kafka
- 02StoreWarehouse and lakehouse
- Databricks Vector Search
- Snowflake Cortex Search
- Databricks Feature Store
- PostgreSQL
- 03TransformModels and tests
- Python
- 04OrchestrateScheduling, CI/CD, catalog
- Apache Airflow
- Unity Catalog
- Microsoft Purview
Why teams choose Equitysoft for AI-ready data
Data and AI in one team
Our data engineers and AI developers work together, so the pipelines, indexes and the assistant on top fit each other.
Permissions respected
Retrieval follows source access rights, and personal data is detected and masked before it reaches a model.
Accuracy measured
A test set of real questions, scored with Ragas and Langfuse, shows whether retrieval improves with each change.
Security sign-off early
Your security team reviews the design before production data is used, and we work under NDA. 50+ engineers, 20+ of them certified on cloud and data platforms.
How we work
Assess
We review the use cases, the documents and data they need, their quality, and the access rules that apply.
You receive AI data readiness review
Design
Sources, parsing, chunking, embedding model, vector store and permission model are agreed.
You receive Ingestion, retrieval and governance design
Pilot
We build a working pipeline for one use case and measure retrieval accuracy with test questions.
You receive One use case live on real data
Build
Connectors, incremental sync, indexes and monitoring are built as code.
You receive Production pipelines and indexes
Govern
Permission checks, PII masking, lineage and logging are in place before wider rollout.
You receive Access, PII and audit controls
Scale
We add sources and use cases, reusing the same pipelines and controls.
You receive More sources and use cases
Tech stack
Technologies we use
AI and machine learning
Data platforms
Pipelines and orchestration
Databases
Languages
Cloud and DevOps
Other tools
Engagement models
How we can work together
Project-Based Delivery
A defined scope, timeline and deliverables, from design through go-live.
Fixed-Price Implementation Projects
An agreed price for a clearly scoped implementation, such as a migration or a new warehouse.
Contract and Subcontract Engineering
Our engineers work under your contract, or as a subcontractor to consulting firms and system integrators.
Dedicated Data and AI Engineers
Data engineers, data scientists and analysts who join your team and work in your tools and processes.
Remote Consulting and Development
Consultants and developers working remotely, with overlap in your working hours.
Architecture and Technical Advisory
Architecture reviews, platform choices and design guidance for your own team.
Production Support and Maintenance
Monitoring, incident handling and ongoing fixes for the pipelines, models and dashboards you run in production.
Proof of Concept and Assessments
A short, fixed-scope engagement to test an approach on your data, prove a model or plan a migration.
Questions, answered
It means your data is collected, cleaned, organized and governed so AI can use it safely: documents are parsed into clean text, records are deduplicated, data is current, and access rules are known and enforced. Without this, AI tools give wrong or out-of-date answers, or show people information they should not see.
Retrieval-augmented generation (RAG) lets an AI model answer from your own documents: it searches your content, then answers using what it finds. The answer can only be as good as what the search returns, so parsing, chunking, embeddings and search quality decide most of the result.
pgvector on PostgreSQL, Pinecone, Weaviate, Qdrant, Milvus, Elasticsearch and Azure AI Search, plus the built-in options on Databricks, Snowflake, AWS and Google Cloud. If you already run PostgreSQL or a data platform, its built-in vector search is often enough.
Yes. We use OCR and layout-aware parsers such as Docling, Unstructured, Azure AI Document Intelligence and Amazon Textract to extract text, tables and structure, and we test extraction quality on a sample before processing everything.
Permissions from the source system, such as SharePoint or Google Drive, are stored with each chunk and checked at search time, so a user's question only retrieves content they already have access to.
We detect and mask or remove personal data with tools such as Microsoft Presidio before it is embedded or sent to a model, keep data in your chosen region, and log what was retrieved for audit.
A feature store keeps the inputs used by machine learning models, such as a customer's spend in the last 30 days, consistent between training and live predictions. It is useful when several models share features or need real-time values. For one or two models, well-designed tables may be enough.
We build a set of real test questions with known answers and measure how often the right content is retrieved and cited, using tools such as Ragas and Langfuse. Changes to chunking or models are compared against that baseline.
Incremental pipelines pick up new, changed and deleted documents on a schedule or as events, and re-embed only what changed. Freshness monitors alert if a source stops syncing.
A readiness review takes 1 to 3 weeks. A pilot pipeline for one use case usually takes 3 to 6 weeks, and production rollout across more sources takes 2 to 4 months.
It depends on the number and type of sources, document volume and security needs. Model, vector database and cloud usage are billed to your own accounts. As a guide, a pilot RAG data pipeline usually costs $10,000 to $30,000, and a production setup across several sources costs $30,000 to $90,000 or more (USD).
We start with a short call and an AI data readiness review of the sources behind one use case. We need a sponsor, read access to sample documents, and a contact in security. After launch we can hand over with runbooks or keep the indexes in sync under a support agreement.
More about our AI-Ready Data
Working togetherHow we work with your team
Data before models
We fix the data the AI depends on before tuning prompts or swapping models.
Security sign-off early
Your security and privacy teams review the access and PII design before rollout.
Measured progress
Every change is checked against the evaluation set, so quality goes up, not sideways.
Works with your AI team
We build the data layer for your own AI developers or our AI team, whichever you prefer.
Where AI-ready data fits best
Professional services
Contracts, proposals and knowledge bases made searchable with access by client and team.
Financial services
Policies, product documents and procedures for staff assistants, with audit trails.
Healthcare
Clinical guidelines and operational documents, with strict control of patient data.
Manufacturing
Manuals, maintenance logs and specifications for field and support teams.
Talk to an AI data engineer
Tell us which AI use cases you have in mind and where the data lives today.