01
Data readiness review
A map of source systems, ownership, access, freshness, quality, sensitivity, and implementation blockers.
Service 07
Data and AI engineering for reliable implementation
Make private data useful, governed, and AI-ready.
Aatvi builds the data foundations AI systems need: pipelines, clean operational models, retrieval indexes, analytics layers, permission-aware access, observability, and quality checks. The goal is to make private data useful without pretending messy data can be fixed by a model alone.
Data flow and permission design before model integration.
Retrieval and analytics systems built for traceability, freshness, and ownership.
Practical quality checks that show where AI outputs are grounded and where they are not.
What this includes
Deliverables a buyer can inspect.
Every service page is written around concrete artifacts. The work should be easy to evaluate before, during, and after the engagement.
02
Pipeline and retrieval architecture
Ingestion, transformation, indexing, metadata, permissions, source citations, and update cadence.
03
Quality and observability
Checks for freshness, completeness, retrieval quality, failed jobs, latency, and user-visible failure modes.
04
AI-ready data layer
A reliable substrate for RAG, copilots, agents, analytics, and operational intelligence.
Risk reduction
What this service is designed to prevent.
Good AI services are not just capability lists. They reduce specific failure modes that buyers already feel.
Ungrounded AI
AI systems produce unreliable answers when retrieval, metadata, and source permissions are weak.
Stale decisions
Dashboards and copilots lose trust when data freshness and pipeline failures are not visible.
Permission leaks
Private-data AI needs access boundaries so users do not receive information they should not see.
Field guides
Read the supporting playbooks.
Process
How Aatvi delivers data engineering.
Inventory
We map source systems, owners, schemas, access paths, data sensitivity, and current usage.
Model
We design the operational data model, retrieval strategy, metadata, and permission approach.
Build
We implement ingestion, transformation, indexing, checks, observability, and API or product surfaces.
Validate
We test data quality, retrieval quality, freshness, and user-facing answers before expanding scope.
Who this is for
Teams that want RAG, copilots, or analytics but have scattered private data.
Organizations where data exists but is stale, duplicated, permissionless, or hard to trust.
Product teams that need ingestion, retrieval, citations, and observability.
Operations teams that need a single useful view across multiple systems.
Who this is not for
Projects expecting a model to compensate for inaccessible or unowned data.
Data lakes with no product, workflow, or decision problem attached.
One-time dashboard work that no one will maintain or use.
Comparison
How this differs from a generic service engagement.
Concern
Generic approach
Aatvi approach
Data quality
Move data first and ask quality questions later.
Make freshness, ownership, and reliability visible from the start.
AI readiness
Treat RAG as a vector database choice.
Treat RAG as ingestion, metadata, permissions, retrieval quality, citations, and evals.
Operations
Deliver dashboards without maintenance paths.
Build data products with checks, logs, owners, and failure modes.
Frequently asked
Common questions about data engineering.
Do we need perfect data before using AI?
No, but you need to know what data is reliable, what is stale, what is sensitive, and where the system should refuse or escalate.
Can you build RAG over private company data?
Yes. We design ingestion, retrieval, permissions, citations, evals, and observability so private-data answers are grounded and reviewable.
Do you replace our existing data stack?
Not by default. We start with the systems you have, then add or change pieces only where the implementation needs it.
What makes data AI-ready?
AI-ready data is accessible, current enough for the task, permission-aware, documented, observable, and tied to a real product or workflow need.
Related services
Other paths that may fit the problem.
Next step
Bring the problem closest to launch pressure.
We will help decide whether the right first step is an audit, roadmap, build sprint, design sprint, or a narrower technical review.