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Snowflake data and AI services, from strategy to the agentic enterprise.

Strategy, engineering, and enablement under one accountable team, building two capabilities that stay in-house: a data practice decisions can trust and an AI practice that ships to production, across THINK, BUILD, and GROW.

Trusted by teams across the Americas
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Engineering

BUILD

Make it real. We build it alongside the team: the governed foundation on Snowflake that AI actually needs, then the pipelines, models, and agents that run on it, integrated with the systems the business runs on.

Engineering02

AI Analytics & Agents

Put governed AI to work: Cortex Analyst and Snowflake CoWork agents that turn governed data into cited, decision-ready answers, embedded where leaders already work.

Put answers in the hands of the people making decisions. We build self-service analytics and AI agents on Snowflake: Snowsight dashboards and Streamlit apps for the views teams live in, Cortex Analyst answering plain-language questions over governed Semantic Views, and Snowflake CoWork letting business users explore and act. This is the business intelligence layer rebuilt so a question returns an answer instead of a ticket: no waiting on the data team, no exporting to spreadsheets.

ToolsCortex Analyst · Snowflake CoWork · Snowsight · Streamlit
Engineering03

Cloud Architecture & Data Foundation

The AI-ready foundation: governed, built on Snowflake, and ready to scale, the single source of truth every model and agent depends on.

Every team gets one fast, scalable foundation to build on. We design and deliver a governed Snowflake data foundation, with cloud architecture sized for the AI workloads on the horizon, not just the reporting teams run today, and security and governance built in from the first table: the single source of truth every model, dashboard, and agent depends on.

ToolsSnowflake · Openflow · Iceberg
Engineering04

Data Engineering & Pipelines

Always-current, trusted data: governed pipelines that unify every source (ERP, CRM, SaaS, and files) so analytics and AI run on inputs worth staking decisions on.

No more chasing numbers across systems. Our Snowflake data engineering services deliver automated, governed pipelines that pull every source (ERP, CRM, SaaS, APIs, databases, flat files) into Snowflake reliably and on schedule, so teams work from data they can trust and AI workloads run on clean, current inputs. Built by a nearshore team in US time zones, priced to a defined outcome rather than the hours.

ToolsSnowflake · Openflow · dbt
Engineering05

Embedded Analytics

Differentiate the product: Cortex-powered data products embedded into apps and client workflows, turning insight into a competitive edge.

Make analytics a feature customers pay for. We build embedded analytics on Snowflake: dashboards, reporting, and Cortex-powered answers delivered inside your applications, client portals, and partner interfaces, so insight lives where users already work and the product stands apart from competitors. The result is a set of customer-facing data products backed by a governed foundation your team keeps.

ToolsStreamlit · Cortex

Where this is heading

Ready for agents that act.

An AI practice ready for agents does not start with agents. It starts with governed data and trusted context: one layer where data, business context, models, and workflows come together. We build that layer with the team, so when the agents act, they act on numbers the business trusts.

See the AI we put into production in our case studies, and how we pick the model for each job on data & AI.

Our thesis: the agentic enterprise runs on one governed layer of data and context

Read the thesis

Business impact

What changes, and when

A practical view of the value an engagement returns: by horizon, not by feature list, with the targets we agree up front.

8–16
Weeks to first production value
10
SnowPro certifications held
Premier
Snowflake partner tier
First 8–16 weeksH1

Foundations & first value

A governed Snowflake foundation stood up, priority data flowing, and the first production dashboards live: value from sprint one, not after a year-long build.
6–12 monthsH2

Scale & self-service

Analytics and AI use cases rolled out across teams, self-service adopted, and manual reporting retired: decisions run on current, trusted numbers.
18+ monthsH3

Compounding advantage

New use cases shipped in weeks, run cost tuned, and a team fluent enough to keep extending it on their own: data and AI become a durable competitive edge.

How engagements run

Delivery leaders can govern

We de-risk the engagement itself (clear timelines, steering, and a clean handover) so the buy is as low-risk as the outcome is valuable.

Timeline

A fixed 8–16 week arc

An initial build is scoped to reach production in 8–16 weeks: a discovery fixes scope and price up front, and every sprint closes with working software, so value lands from sprint one.
Steering

Steering & transparency

Regular steering reviews, a shared backlog, and clear decision gates keep sponsors in control of scope, budget, and priorities throughout.
Proof

De-risked by design

We prove the approach with a focused proof of concept before the full build, so the commitment to scale rests on evidence, not a slide deck.
Handover

Built to hand over

Documentation, enablement, and a transition plan in every engagement, so the team runs and extends the work confidently.

Security and governance are built into every phase: see how we keep enterprise data safe.

Need the whole implementation scoped, not just a service picked?

Consulting & implementation

What we build on

Why we build Snowflake-native

Openflow to Horizon Catalog to Cortex: we lead with Snowflake-native products over third-party tools, so there is one governed copy of the data, one lineage to audit, and one trusted context every AI agent relies on. dbt is the one external framework we run, natively against Snowflake.

See the full stack, layer by layer, with what's GA and what's ahead

Explore the platform

How we work

Engagement models

Flexible ways to partner with us, matched to the shape of the problem, from a fixed-scope build to an embedded team or an ongoing managed service. Whichever model fits, the way we deliver doesn't change.

Fixed cost01

A defined scope, timeline, and price agreed up front: fixed-outcome delivery, priced to the result rather than the hours. Best when the outcome is clear and budget certainty matters from day one.

How it works: We scope the work in a short, discovery, then commit to a price and a date.

  • Scoped statement of work
  • Milestones with decision gates
  • Change control if scope moves
Best forDefined foundation builds & migrations
Time & materials02

Flexible, iterative delivery billed by effort against a shared backlog. Ideal for evolving requirements and discovery-led work.

How it works: We deliver sprint to sprint against a prioritized backlog the business controls.

  • Prioritized, shared backlog
  • Sprint demos & burn reporting
  • Stop or pivot any sprint
Best forDiscovery, POCs & evolving scope
Dedicated team & staff augmentation03

Embed our certified practitioners alongside the in-house team, as a dedicated team or a team extension. We accelerate delivery while leveling up their capability.

How it works: SnowPro-certified engineers join the team, its tools, and its ways of working.

  • Snowflake depth at every level
  • Knowledge transfer built in
  • Scale up or down monthly
Best forScaling an existing team fast
Managed services & support04

Ongoing run, optimization, and enhancement once the practice is live, so it keeps compounding while the in-house team grows into it.

How it works: A retained team monitors, tunes cost and performance, and ships enhancements.

  • Monitoring & cost optimization
  • SLAs and a named contact
  • A roadmap of enhancements
Best forRunning & growing a live Snowflake estate

Constant across every model

The model flexes. The standard doesn’t.

However an organization chooses to engage, every Viewnear engagement is delivered to the same standard: the things that make the difference between a build that ships and one that stalls.

One certified team, with depth at every level

One accountable team: the people who scope the work are the ones who deliver it.

Verified Snowflake depth

SnowPro-certified engineers with a verified Snowflake delivery record behind every decision, from strategy through production.

Priced to outcomes

Scope and price agreed up front, whichever model fits.

Governance built in

Security, lineage, and access control designed in from the first table, not bolted on.

Handover and enablement

Full handover, documentation, and enablement so the team runs it confidently.

Integrated with the enterprise

Data products that connect to and from the systems the business runs on: ERP, CRM, and customer-facing apps.

JC RodriguezRené TreviñoCarlos EgremyKaren BerberAydhé MotaEduardo Javier Ramos

One accountable team. Meet the leadership team →

What drives cost

Engagements are scoped on data volume and source complexity, the number of analytics and AI use cases, team size, and timeline. We agree scope and price up front (whichever model fits) so there are no surprises.

Not sure which model fits? Tell us the problem and we’ll recommend one.

FAQ

Common questions

What is a Snowflake Premier Partner, and how can that status be verified?+

Viewnear is a Snowflake Premier Partner and a Snowflake CoCo Preferred Partner, with SnowPro-certified engineers and a verified delivery track record across the Americas.

What does Premier status actually unlock?+

Depth and a direct line to Snowflake. Premier status reflects certified delivery across the full Snowflake stack, and it means we work alongside the client's own Snowflake account team on architecture and delivery, across the current stack (Cortex, Openflow, Horizon Catalog, CoCo, and CoWork). We partner with Snowflake to carry each project to a successful outcome.

How does Snowflake pricing work, and can it be bought through a partner?+

Snowflake is consumption-based: the business pays for the compute (credits) and storage it actually uses, so run cost flexes with the work. Snowflake capacity can be procured through Viewnear for simpler commercial terms and account management under one accountable partner.

How long does a Snowflake implementation take?+

A first production build is scoped at 8–16 weeks, depending on data volume, source complexity, and the use cases in scope. What keeps that real: a discovery that fixes scope up front, use-case-driven sprints with working software at every demo, and proof running in parallel with the build, so value shows from sprint one.

How can a large Snowflake engagement be de-risked?+

We prove the approach with a focused proof of concept before we scale, run regular steering reviews with clear decision gates, and build enablement in from day one, so in-house teams can run and extend the work without us.

Can an existing data warehouse be migrated to Snowflake?+

Yes. We migrate from Teradata, Oracle, Hadoop, and SQL Server, and manage the full technical delivery and program governance.

Can Snowflake be integrated with ERP, CRM, and operational systems?+

Yes, in both directions. Openflow and Zero-Copy Integrations bring data in from systems like SAP, Salesforce, and Workday, and we deliver insight back out through Snowsight, Streamlit apps, APIs, and agents embedded where teams work.

Where are nearshore Snowflake delivery teams based?+

Our nearshore delivery center is in Monterrey, Nuevo León, Mexico, with leadership in Austin, Texas. Most engineering sits in Monterrey and leadership in Austin, with some roles remote across the Americas. We serve clients across the Americas: Canada, the United States, Mexico, LATAM, and the Caribbean.

What time zone does the nearshore team work in?+

Monterrey holds Central Standard Time (CST) all year, because Mexico no longer observes daylight saving time. US Central switches to CDT from March to November, so in those months the Monterrey clock reads an hour earlier than yours. The team works your business hours either way, so the working day overlaps end to end and questions get answered the same day instead of overnight.

How far is Monterrey from the United States, and can the team work onsite?+

Monterrey is about 140 miles from the Texas border, with nonstop flights to major US cities in one to four hours. That makes onsite work practical rather than ceremonial: discovery workshops, architecture sessions, and steering reviews can happen in your office without a two-day trip on either side.

Can we visit the Monterrey delivery center?+

Yes. Clients are welcome at our Monterrey office, and visits are common at the start of an engagement: meet the engineers who will build the work, walk the architecture on a whiteboard, and see how the team runs day to day.

How is nearshore different from offshore for a data and AI build?+

Snowflake and AI work is iterative: profile the data, model it, test a use case, look at the result, adjust. That loop is fast when a blocker raised at 10am is resolved by lunch, and painful when every round trip waits overnight. Nearshore keeps that loop inside a single working day, with live pairing and sprint reviews your team can actually attend. Offshore can still win on pure rate card; it rarely wins on time to a working result.

Does the team work in English or Spanish?+

Both. Every engineer is English-proficient and the team is fully bilingual, so working sessions, documentation, and enablement run in whichever language your team thinks in. There is no translation layer between you and the people doing the work.

What engagement models are available?+

Three, and they can be combined: fixed-outcome delivery priced to a defined result, flex capacity when the scope keeps moving, and an embedded or dedicated team that works inside your sprints, repos, and standards. Team extension and staff augmentation buyers usually land on the embedded model. All three are measured on outcomes rather than hours.

Does the work stay inside our environment and under our controls?+

Yes. The build runs in the client's Snowflake account, repositories, and CI, under the client's access controls and change process. Engineers work as named identities with least-privilege access, under signed confidentiality terms, and Horizon Catalog lineage keeps an audit trail of what changed.

How is an engagement priced?+

Engagements are scoped on data volume and complexity, the number of analytics/AI use cases, team size, and timeline. We agree scope and price up front and offer fixed-outcome, flex-capacity, and embedded-team models: a partner measured on the outcome, not the hours billed.

Do Snowflake partners publish standard pricing?+

No. No two data estates are the same, so we price to the work. Share the goals and constraints, and we'll come back with a model, a plan, and a price.

How is data kept secure during a Snowflake engagement?+

We build on Snowflake's certified platform and extend it with least-privilege access, Horizon Catalog lineage and PII classification, Horizon Context so every person and AI agent works from the same trusted business context, data residency by region, and audit-ready controls, all configured to the client's sector.

Should a Snowflake build stay native, or add third-party tools?+

We lead with the Snowflake-native stack (Openflow, Snowpark, Horizon Catalog, Cortex, Snowsight, Streamlit, plus the Snowflake CoCo coding agent and CoWork AI agent) so governance and AI context (Horizon Context) stay in one place. dbt is the one external framework we run, natively against Snowflake.

What agreements govern a Viewnear data and AI engagement?+

A separate written agreement governs every Viewnear engagement and sets out scope, fees, timelines, and obligations; where it conflicts with the website terms, the engagement agreement prevails. Fixed-cost work is documented as a scoped statement of work with milestones, decision gates, and change control if scope moves, while time and materials work runs against a prioritized backlog the business controls. A short discovery fixes scope and returns a firm price before a build is committed, and engineers work under signed confidentiality terms.

How does Viewnear handle a vendor security review or security questionnaire?+

Viewnear answers a vendor security review by walking through how each requirement is met, whether that is a single item such as data residency, certifications, or audit, or a full questionnaire. The published starting point is Viewnear's Security and Trust page, which splits the answer in two: the environment inherits Snowflake's independently audited certifications (SOC 2 Type II and ISO 27001 across editions, with PHI and cardholder data requiring Business Critical or VPS), and Viewnear applies its own delivery controls on every engagement. Those controls are least-privilege access, lineage and auditability, PII classification, data residency by region, secrets and key management, and secure delivery practices, which mean code review, least-privilege delivery accounts, and environment separation.

Does data leave the client's region when Snowflake delivery happens from Mexico?+

Data stays in the client's own Snowflake account, in the Snowflake region the client's policy requires: Viewnear deploys in the Snowflake region required across the Americas, and nothing about the delivery model requires data to be copied out of it. Engineers based in Monterrey, Nuevo León and in Austin, Texas reach that account as named identities with least-privilege access, under the client's access controls and change process. Horizon Catalog lineage and access history log every access, and sensitive data is classified and masked with tagging and row and column policies from the first table.

What happens at the end of a Viewnear engagement?+

A Viewnear engagement ends on a documented handover: runbooks, documentation, and enablement sessions ship with the build, plus a transition plan that names who runs what once Viewnear steps back. The work already sits in the client's own Snowflake account, repositories, and CI, so the in-house team runs and extends it from there. Ongoing optimization, cost tuning, and support stay available for teams that want them.

How much of the US working day does the Monterrey delivery team overlap?+

The Monterrey team works the client's business hours rather than local ones, so an engagement on Central, Eastern, or Mountain time gets a working day that lines up with it. Standups, sprint reviews, and steering meetings all sit inside the client's working day, so a blocker raised in the morning gets an answer that day instead of clearing overnight. Monterrey holds Central Standard Time year round, an hour behind US Central from March to November, and the working schedule absorbs the difference.

How are the usual offshore risks handled in a nearshore engagement?+

Viewnear answers offshore risk with three mechanisms, not assurances. Continuity: one committed team carries the work from scope to run, with no re-staffing mid-engagement and no delivery pyramid billed by the hour, and the SnowPro-certified people who scope the work are the people who build it. Visibility: regular working sessions, a shared backlog, and clear decision gates keep scope, budget, and priorities with the client's sponsors. Control: the build runs in the client's own Snowflake account, repositories, and CI, under access the client grants and can revoke, with Horizon Catalog lineage keeping an audit trail of what changed.

Who owns the code and data models an engagement produces?+

The client owns everything an engagement produces: the code, the data models, the transformations and the documentation, with Viewnear retaining no rights in the work product. The work is built inside the client's own Snowflake account, repositories and CI from the first commit, so ownership is a matter of record rather than a transfer at the end. Snowflake's own migration tooling and any third-party software keep their own licences.

Let's stand up a lasting data & AI practice.

Tell us where the organization stands (migrating, scaling, or shipping AI) and we'll map the fastest path to use cases in production, run by in-house teams.