How do you keep a warehouse migration from disrupting reporting?

We run old and new in parallel and reconcile outputs row by row until the numbers match. Business users only move once their reports have been proven identical on the new platform.

Can Snowflake support our AI workloads too?

Yes — Cortex functions, vector columns, and Snowpark let you run inference and semantic search next to the governed data rather than exporting it elsewhere.

How quickly can an engagement start?

Discovery usually begins within one to two weeks of scope agreement, and a delivery team is typically mobilised within three to four weeks depending on the skills involved.

Can you work alongside our in-house team?

Yes — most of our engagements are blended. We work in your repositories, ceremonies, and tooling, and we plan for knowledge transfer from the start so capability stays with you.

How are engagements priced?

Fixed price for well-bounded projects, monthly capacity for dedicated pods, and outcome-linked SLAs for managed services. We are explicit about assumptions and what would change the number.

NeonAITech services — AI and data engineering, product engineering, cloud and DevOps, managed operations, security and quality

Cloud Data Warehousing on Snowflake

Elastic analytics with spend you can actually forecast.

Book Free Consultation All Services

30%+

Typical credit-spend reduction

10x

Faster analytical query response

Zero

Data loss on validated cutover

Overview

What Cloud Data Warehousing on Snowflake means at NeonAITech

We implement, migrate to, and optimise Snowflake — modelling, ELT, role-based security, and warehouse sizing — so business teams get fast answers while finance gets predictable credit consumption instead of surprise invoices.

Tools & Platforms

Snowflake
dbt
Fivetran
Airflow
Snowpark
Power BI

We are not tied to a single vendor — the stack follows the problem, your existing estate, and your team’s skills.

What We Deliver

Migration & Modernisation

Phased moves from Teradata, Oracle, SQL Server, or Redshift with automated code conversion and parallel-run validation.

Modelling & ELT Engineering

dbt-driven transformation layers, dimensional and data-vault models, and tested, documented, version-controlled logic.

Performance & Credit Optimisation

Warehouse sizing, clustering, result caching, and resource monitors that typically remove a third of avoidable spend.

Capabilities

Inside the Engagement

Snowpark and native application development

Secure data sharing and clean rooms

Row- and column-level security policies

Zero-copy cloning for safe environments

Cortex AI functions on warehouse data

How We Work

A delivery rhythm you can see into

Every Cloud Data Warehousing on Snowflake engagement runs the same four phases, with AI used wherever it removes effort rather than adds novelty.

01
Discover
We map the current state, agree the outcome, and size the work — so scope is a shared decision, not a surprise.
02
Design
Architecture, delivery plan, and success measures are set before build, with costed options where trade-offs exist.
03
Build
Short increments with working output you can review, steer, and stop — never a black box until go-live.
04
Operate
We measure against the agreed outcomes, hand over documentation, and stay on for support where you want it.

Engagement Models

Buy it the way that fits

Fixed-Scope Project

A defined outcome, timeline, and price. Best when requirements are clear and the deliverable is well bounded.

Dedicated Pod

A cross-functional team working to your backlog and priorities, scaling up or down with a month’s notice.

Managed Service

Ongoing ownership against agreed SLAs, with a share of capacity reserved for continuous improvement.