ACCREDITED DATABRICKS CONSULTING PARTNER · DATA + AGENTIC AI

Your
enterprise
data. Ready
to act.

KNS Technologies is an AI and data intelligence solutions expert helping enterprises build the governed data foundation, context and agentic systems required to move from promising POCs to production.

Scope a Databricks use case
See the architecture

89%

of manufacturers are investing in GenAI including LLMs

75%

of manufacturing CEOs say advanced GenAI will create competitive advantage

76%

of manufacturers expect AI to boost efficiency by more than 25% over two years

CUSTOMER VALUE DRIVERS

Unify the platform.
Multiply the value.

KNS value framework centers on three outcomes: lower cost and complexity, faster AI-driven innovation, and
lower risk across data and AI.

1

Reduce cost & complexity

Unify data and AI workloads on one open foundation, reduce duplicated tools and data movement, and simplify the operating model.

2

Accelerate AI-driven innovation

Break down silos so data, analytics and AI teams can collaborate on governed data and move faster from experimentation to production.

3

Mitigate risk across Data + AI

Embed access control, monitoring, lineage and governance into the platform so trustworthy AI can scale without slowing innovation.

WHY DATA PROGRAMS STALL

The problem is rarely “not
enough AI.”

It is usually fragmented data, fragmented tooling and fragmented control — made worse by real-time scale and
the pressure to productionize AI.

Disparate data sources

Enterprise data is spread across clouds, on-prem systems, applications, documents and real-time streams.

Fragmented tooling

Multiple disconnected ETL, warehouse, AI and BI tools increase complexity, handoffs and time to value.

Real-time pressure

Factory, sensor, mobile and transactional streams require low-latency ingestion and processing.

Scale, quality & governance

Pipelines must scale reliably while preserving data quality, lineage, security and access control.

KNS CAPABILITY

KNS combines deep
enterprise experience in
data warehousing, systems
integration, business
intelligence, analytics
engineering,
web/ mobile/ mainframe
delivery and outcome-led
product strategy. That
foundation is now applied
to Databricks
implementation and
Agentic AI

01
Discover
Prioritize use cases by business value, data readiness, feasibility and measurable success criteria.
02
Validate
Assess source systems, quality, velocity, unstructured content, governance and security constraints.
03
POC
Prove grounded quality, latency, cost and usability with representative enterprise data.
04
Pilot
Integrate with real users, workflows and tools; add identity, security and operational controls.
05
Production
Industrialize pipelines, CI/CD, model serving, monitoring, access controls and support handover.
06
Scale
Reuse governed data products, retrieval patterns and agent components across the next wave of use cases.

CONTEXT · CONTROL · COST · CHOICE

One open foundation for
data, AI and agents.

Build enterprise context for AI, govern Data + AI in one place, control AI costs, and retain freedom across
clouds, models and data formats.

Context

Connect AI to enterprise data and business semantics.

Control

Govern Data + AI consistently with Unity Catalog and AI Gateway.

Cost

Consolidate workloads and optimize performance and serving economics.

Choice

Keep data open and preserve freedom across clouds, tools and model families.

TYPICAL KNS IMPLEMENTATION BLUEPRINT

OPEN INFRASTRUCTURE

Open lakehouse formats · Any cloud · Any model · Any data

AGENTIC DATA

Lakeflow · Lakehouse · Lakebase · Streaming · CDC · Bronze / Silver / Gold

.

Lakeflow
Lakehouse
Lakebase

UNIFIED GOVERNANCE + AI CONTROL

Unity Catalog · Unity AI Gateway · lineage · identity · monitoring · auditability · cost control

AGENTIC DEVELOPMENT + WORK

Agent Bricks · Genie · governed tools · multi-agent workflows · Model Serving · AI/BI

AGENT
TOOLS
ACTION

Enterprise sources → reliable data engineering → governed context → measurable agent action

MANUFACTURING DATA + AI

Create shared value
across the business.

Connected products, smart manufacturing and end-to-end supply chain visibility provide a practical portfolio
for Databricks and Agentic AI implementation.

Connected Products

  • Verification, validation & simulation
  • Predictive maintenance
  • Product feature usage
  • Warranty cost optimization
  • Predictive field service scheduling

Smart Manufacturing

  • AI-assisted quality control
  • Production OEE monitoring
  • Predictive maintenance
  • Dynamic production scheduling
  • Worker safety
  • Energy efficiency

End-to-End Supply Chain

  • Demand forecasting
  • Inventory management
  • Logistics control tower
  • Pricing analytics
  • ESG data sharing
  • Financial consolidation

GenAI use cases expected to bring the most value

Supply chain optimization
52%
Quality control
39%
Automation & efficiency
20%
Real-time insights
19%
Predictive maintenance
19%
Personalization & CX
16%

Databricks platform proof points

John Deere

8PB+

structured + unstructured data ingested

3,000+ use cases supported; over $30M infrastructure savings, $35M productivity gains and $30M in deployed use cases.

AT&T

$266M

value enabled by Databricks

Unified access across 60+ business units, 100+ ML models in production for fraud prevention, and an 80% reduction in fraud.

Atlassian

60%

decrease in costs

Databricks powers virtually all business decisions; delivery times reduced 30% versus prior infrastructure.

JetBlue

Real-time

data + LLM innovation

Real-time weather, IoT and FAA data power a digital twin supporting safer, more efficient operations and better passenger experiences.

START A CONVERSATION

What
should
your data
and AI do
next?

Tell us the business outcome, the data you
have, and where you are in the journey. KNS
will help frame the shortest credible path to
value.

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