Full Transformation
From scattered data to agents running in daily operations, with the data foundation built first and the same embedded team delivering each stage.
A full transformation takes an organization from scattered data to agents running in daily operations. It brings together our Data + AI advisory, the data foundation on Snowflake and agentic engineering, delivered by a team embedded in the client's working day: a delivery lead, an architect and SnowPro-certified engineers on US business hours from Austin and Monterrey. The work runs foundation first, then intelligence, then operations, one use case at a time, and after go-live the client's team runs what was built or Viewnear operates and extends it as a managed service.
What a full transformation delivers
Each stage builds on the one before it, and the team that runs the first workshop also takes the first agent into production.
- Advisory to start
Workshops and an operations diagnosis, a readiness assessment with the KPIs to track, use cases ranked by value and an architecture blueprint, the same work as our Data + AI advisory. The first use case comes out of it with an owner, a baseline and a price.
- The data foundation
The sources the first use cases need arrive in Snowflake through Openflow, Snowpipe Streaming and Zero-Copy Integrations, modeled in dbt into Medallion layers, with lineage and access policies in Horizon Catalog from the first table. Cloud architecture and data engineering cover each layer in depth.
- Intelligence people can query
A semantic layer with business context in Semantic Views, plain-language answers through Cortex Agents and Snowflake CoWork, and AI search over the organization's documents, built as in AI analytics.
- Agents in daily operations
We bring agentic solutions in and around Snowflake: agent architecture on governed data, workflow design with approval points, then engineering, evaluation and deployment into the ERP, the CRM and the other systems of record, as in agentic engineering.
- An embedded delivery team
A delivery lead who owns the plan and runs steering with your sponsor, an architect who owns the design, and SnowPro-certified engineers who build it, inside your working day, sprint calendar and repositories.
- Go-live with your team ready
Each use case goes live with its evaluations, documentation and runbooks, and your engineers and analysts pair on the build throughout, so they know how each layer and each agent works.
What it looks like in your stack
The work runs in your Snowflake account, repositories and CI, under your access controls, and the results reach the tools your people already use.
- Advisory to start
- The data foundation
- Intelligence people can query
Three stages, each with a case in production
The program follows the order in which data and AI initiatives tend to stall: the data foundation, then intelligence people can query, then agents in operations.
01Vision → Foundation
The sources the first use cases need arrive in Snowflake reconciled and documented, with access policies and business definitions in place.
7 weeksto a governed, real-time data foundation for a university group with 20,000+ studentsRead the case02Foundation → Intelligence
A semantic layer, plain-language answers and AI search over documents change how people find and check the numbers.
60% → 95%claims-document classification accuracy for an insurance processorRead the case03Intelligence → Operations
Agents act in the systems of record, and a person approves the steps that matter.
15–19 hrsof administrative time back every week at Magnolia DoorsRead the case
How a full transformation runs
Engagements follow the same four steps. Each sprint delivers working software on your data, and each further commitment rests on what already runs in production.
- 01
Discovery
A short engagement that fixes scope and price and shows the current state of your data.
- 02
Use-case sprints
Each sprint delivers a working increment into your environment, one use case at a time.
- 03
Proof before scale
Each use case proves out in production before the next one is committed.
- 04
A practice you keep
Your team builds alongside ours, so the skills stay in-house.
Each stage starts once the one before it runs in production, so the decision to fund the next stage rests on what the last one did. Steering reviews and a shared backlog keep the sponsor in control of scope, budget and priorities at each decision gate.
Teams that already have part of the work in hand can start with Data + AI advisory or agentic engineering on its own, and organizations that want a first read on where they stand start with a Data + AI readiness session.
Embedded on your hours, from Austin and Monterrey
A transformation runs on daily decisions: a definition to settle, a source to prioritize, an approval rule to agree for an agent. Leadership works from Austin, Texas, and SnowPro-certified engineers work nearshore from Monterrey, Mexico, on US business hours, for clients across the USA and LATAM, so those decisions are made the day they come up.
The team joins your standups, sprint reviews and steering meetings, works in your Snowflake account and repositories under access you grant, and works in English or Spanish. The engineers are named, and the sponsor meets them before signing. Viewnear is a Snowflake Premier & Select Partner and Snowflake CoCo Preferred Partner, and our engineers hold 10 SnowPro certifications.
Frequently asked questions
What does a data and AI transformation include?
The advisory work (an operations diagnosis, a readiness assessment with KPIs, use cases ranked by value and an architecture blueprint), the data foundation on Snowflake, the semantic layer and AI analytics on top of it, and agents that act in the systems of record with a person approving the steps that matter. One delivery team carries all of it, and each use case reaches production before the next one is committed.
Where does a transformation start when the data is still scattered?
With the foundation, scoped to the first use case. The sources that use case needs come into Snowflake first, with access policies and business definitions designed in, and later use cases reuse that work. A multi-campus university group started there, with academic records and learning events from campuses in Miami and Latin America brought into a single real-time source.
How is a full transformation priced?
Discovery fixes the scope and the price of the first stage before the build starts. From there the program can run as a fixed-cost build, time and materials, or a dedicated team, and our pricing sets out each model. Snowflake bills separately on consumption (compute credits plus storage), and the build sets budgets and resource monitors so that run cost follows the work.
How do we know an agent is right before it acts?
Each agent is evaluated before it scales, against questions and cases with known answers, using the Semantic Views the business already reports from. In production, agents act under the Snowflake roles of the person who asked, AI Agent Identity marks each query an agent runs, and the actions that change a system of record wait for a person's approval, as the coordinator's approval does at Magnolia Doors.
What happens after go-live?
The build is documented as it goes, with runbooks and evaluations for each use case, and your team runs it from there. Viewnear can also keep operating and extending it, with monitoring and cost optimization, SLAs and a named contact, and a roadmap of enhancements to Snowflake and the agents.
Work already in production
Each case study sets out the problem, what we built and the numbers, for clients across the USA and LATAM.
Read the case studiesTalk to the people who would build it
Thirty minutes with someone who would own the work, and a straight answer on whether we are the right fit.
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