Vaulti Lake

Unify enterprise data into intelligence

Bring structured and unstructured enterprise data into one connected, queryable intelligent layer — ready for analytics, reporting, and AI.

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vaulti-lake · query
> Show revenue by region, Q4 2024
Query executed · 3 sources joined
✓ Connecting operational databases
✓ Retrieving financial datasets
✓ Combining regional records
RegionRevenueYoY
NA$142M12%
EMEA$98M8%
APAC$67M24%
Joined from 3 sources · finance_db · ops_warehouse · sales_api
Why Vaulti Lake

One layer across every source

A connected, queryable layer that unifies structured and unstructured enterprise data — ingested, normalised, and ready for intelligence.

Unify Across Sources

Connect enterprise databases, documents, APIs, and operational systems into one queryable data layer.

Docs
DB
API
Systems
Tools
Live

Normalise Enterprise Data

Structure, standardise, and refine enterprise data for reliable querying, analytics, and operational intelligence.

ING
NRM
QRY

Enable Queryable Intelligence

Transform fragmented enterprise data into connected insights, operational visibility, and AI-ready business intelligence.

NA
$142M
EMEA
$98M
APAC
$67M
Q4 2024 3 sources joined
Layered Data Architecture

From raw data to queryable intelligence

A three-tier architecture — raw ingestion, refined normalisation, and trusted gold-layer datasets ready for analytics and AI.

Bronze

Raw ingestion

Operational records, APIs, databases, and external sources land in connected raw data layers — captured exactly as ingested.

✓
Retrieved CRM operational dataset
8:09 PM · Customer Records
!
Detected duplicate procurement records
8:07 PM · Vendor Operations
✓
Synced regional sales datasets
8:05 PM · Retail Analytics
✓
Connected financial reporting source
8:03 PM · Reporting Platform
Silver

Refined & structured

Clean, standardise, and structure enterprise datasets for reliable operational querying and downstream processing.

Raw
›
Refined
›
Structured
Gold

Query-ready intelligence

Trusted, query-ready datasets prepared for analytics, AI workflows, and operational decision-making.

ING
NRM
REF
QRY
Capabilities

Queryable intelligence across connected systems

Scroll through four core capabilities — each one stacks into view as you go.

01 / Multi-source ingestion

Ingest documents, APIs, databases and systems

Connect every operational system into one intelligence layer — no schema rewrites, no centralisation projects.

View ingestion guide
Lake Query 4 systems connected
A
Show revenue performance across all EMEA retail systems.
✓ Connecting operational databases...
✓ Retrieving financial datasets...
✓ Combining regional records...
Revenue data retrieved successfully across 4 connected systems — EMEA retail performance reconciled.
EMEA retail · 4 sources
02 / Data normalisation

Standardise structured and unstructured data

Refine messy enterprise data into consistent, query-ready datasets — reliable for reporting, AI workflows and downstream applications.

Explore normalisation rules
Lake QueryCRM scan complete
A
Which customer records contain incomplete compliance fields?
✓ Reviewing CRM datasets...
✓ Comparing compliance records...
✓ Identifying inconsistent fields...
128 customer records contain incomplete compliance verification fields across connected CRM datasets.
CRM · compliance_records
03 / Cross-system querying

Query distributed datasets simultaneously

Run one query across departments, operational platforms and distributed business systems — no per-system pipelines required.

View query patterns
Lake Query2 systems joined
A
Compare supplier costs across procurement and logistics systems.
✓ Connecting procurement databases...
✓ Retrieving logistics datasets...
✓ Matching supplier records...
Supplier freight costs increased 14% across APAC logistics operations vs. last quarter.
procurement_db × logistics_api
04 / Analytics-ready layers

Query-ready datasets for analytics and AI

Prepare structured, reliable datasets that downstream analytics tools, AI workflows and operational dashboards can consume directly.

See analytics integrations
Lake QueryQ4 metrics ready
A
Which regions showed the highest operational growth in Q4?
✓ Reviewing operational datasets...
✓ Combining regional metrics...
✓ Ranking growth performance...
EMEA operations recorded the highest quarter-over-quarter operational growth, followed by APAC.
ops_warehouse · Q4 layer
PDF procurement_q4
XLS supplier_review
DOC board_summary

Built for operational visibility

Connect business systems for reporting, analytics, operational visibility, and AI-powered decision-making.

Explore Vaulti Lake
Use Case · Retail & E-Commerce

Unify sales, inventory and customer data

Bring sales, inventory, logistics, and customer datasets into one layer for connected operational visibility and performance analysis.

✓Real-time inventory across stores and warehouses
✓Unified customer view across channels
✓Same-day sales reporting across regions
See retail patterns
▸ Retail Analytics · Lake
Inventory turn by store, last 30 days?
EMEA stores averaged 4.8x turn rate, with London-3 leading at 6.2x.
lake.query · inventory + sales
Use Case · Financial Services

Connect transactional and compliance data

Unify transactional, compliance, and operational datasets for unified reporting, governance, and risk analysis workflows.

✓Cross-system reconciliation in minutes, not days
✓Risk exposure visible across portfolios
✓Audit-ready compliance queries
Financial services use cases
▸ Risk Analytics · Lake
FX exposure across active trades, EMEA?
EMEA portfolios hold $84M in unhedged EUR-USD exposure, concentrated in 3 desks.
lake.query · trading + risk_db
Use Case · Healthcare Operations

Query operational data across facilities

Query operational records, reporting systems, and healthcare datasets across distributed enterprise environments — securely and at scale.

✓Cross-facility operational reporting
✓HIPAA-aligned secure querying
✓Resource utilisation visibility
Healthcare patterns
▸ Healthcare Ops · Lake
Bed utilisation across regional hospitals?
Average occupancy is 87% across 12 facilities; 3 sites approaching capacity threshold.
lake.query · 12 facilities joined
Use Case · Supply Chain & Logistics

Unify procurement, warehouse and shipment data

Combine procurement, warehouse, shipment, and operational datasets into one connected intelligence environment.

✓End-to-end shipment traceability
✓Cross-supplier cost comparison
✓Disruption detection across the chain
Supply chain use cases
▸ Supply Chain · Lake
Supplier delivery performance, last quarter?
On-time delivery dropped to 89%; two APAC suppliers account for most of the slip.
lake.query · procurement + logistics
Use Case · Enterprise Reporting

Cross-department reporting without spreadsheets

Combine cross-department enterprise datasets for forecasting, operational reporting, and business performance analysis.

✓Single source of truth for the C-suite
✓Always-fresh forecasts and pipelines
✓BI-tool ready datasets out of the box
Reporting patterns
▸ Enterprise Reporting · Lake
Revenue by region with YoY growth, Q4?
NA $142M (+12%), EMEA $98M (+8%), APAC $67M (+24%).
lake.query · 3 sources joined
Use Case · AI & Analytics Teams

Clean, query-ready data for ML and analytics

Prepare structured enterprise datasets for machine learning, automation, and AI-driven operational intelligence initiatives.

✓Feature-engineered datasets, ready to train
✓Version-controlled data layers
✓Direct integration with Vaulti SDK and notebooks
AI & ML use cases
▸ AI Team Notebook · Lake
Training dataset for churn model, last 18 mo?
2.4M rows ready in gold layer; 47 features normalised across 5 systems.
lake.layer · gold · churn_v3
Process

How Vaulti Lake works

From raw enterprise data to query-ready intelligence — five steps from ingest to execution.

Step 01 · Ingest

Connect every operational data source

Pull from APIs, databases, files, and operational systems — Vaulti Lake handles connectors, scheduling, and incremental sync.

connect sources → finance_db, sales_api, ops_warehouse
100+ connectors incremental sync schema-on-read
Step 02 · Normalise

Standardise and clean enterprise data

Type-cast columns, harmonise schemas, dedupe records, and enforce data quality rules — every dataset becomes reliable for downstream use.

crm_customers
deduped · 8.2K → 7.9K rows
sales_records
normalised · currency standardised
ops_metrics
128 incomplete records flagged
Step 03 · Unify

One queryable layer across every source

Combine structured rows and unstructured documents into one connected intelligence layer — no manual joins, no per-system pipelines.

DOC
policy_docs
DB
finance_db
API
sales_api
SYS
ops_warehouse
Step 04 · Query

Retrieve insights with one cross-system query

Run a single query — Vaulti Lake pulls from every connected source and returns a unified, source-cited result.

Q4 revenue: NA $142M (+12%), EMEA $98M (+8%), APAC $67M (+24%). EMEA leads quarter-over-quarter growth.
F finance_db · revenue.q4 S sales_api · /regions O ops_warehouse · perf
Step 05 · Execute

Power downstream analytics and AI

Connected datasets feed your BI tools, ML pipelines, and operational dashboards directly — Vaulti Lake is the layer underneath.

Push the gold layer to the BI tool.
Q4 revenue dataset published to Tableau workspace · 47 cols, 2.4M rows.
Make it available to the ML team for the churn model.
Notebook access granted; dataset versioned as churn_v3.
Layer published · 2 downstream consumers

Build a queryable intelligence layer

Build connected intelligence layers for analytics, reporting, and AI-powered operational visibility.

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  • Access information across documents, enterprise data, and business systems
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  • Scale enterprise knowledge access without compromising data security
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