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.
Strategy
THINK
Set the direction. We work inside the team to pinpoint where data and AI create real value, then sequence a roadmap the business can execute, grounded in what the data can support today.
Data & AI Strategy
Turn AI ambition into a board-ready roadmap: where data and AI create measurable ROI, sequenced by value and grounded in what the data can actually support, on a clear path to the agentic enterprise.
Invest with confidence and know exactly what to build next. We work with CEOs and CTOs to turn 'we need an AI strategy' into a costed, sequenced roadmap, grounded in current data readiness and focused on the use cases with the clearest return.
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.
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 the reporting and self-service layer natively in Snowflake: Snowsight dashboards and Streamlit apps for the views teams live in, with Cortex Analyst answering questions over governed Semantic Views and Snowflake CoWork (the personal AI agent) letting business users explore and act in plain language. Anyone who needs insight can find it themselves: no waiting on the data team, no exporting to spreadsheets.
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 it cloud-native on Snowflake, sized for the AI workloads on the horizon rather than just the reporting teams run today, with security and governance built in from the start.
Data Modernization & Migration
Get off costly legacy platforms without the risk. We migrate Teradata, Oracle, Netezza, Hadoop, and cloud warehouses onto one governed Snowflake foundation, with automated SQL translation and validation at every step.
Retire the licenses, hardware, and brittle pipelines holding you back. We modernize legacy estates onto Snowflake the low-risk way: a dependency assessment first, then automated schema and SQL translation, row-level parity validation so results match before you cut over, and Openflow and dbt pipelines that keep data fresh from day one. The result is not a lift-and-shift of old problems, it is a governed, Cortex-ready foundation your analytics and AI can build on.
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. We build automated, secure pipelines that pull every source (APIs, databases, flat files) into the warehouse reliably and on schedule, so teams work from data they can trust and AI workloads have clean, current inputs.
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 embed dashboards and reporting directly into applications, client portals, and partner interfaces, so the insight lives where users already work and the product stands apart from competitors.
Enablement
GROW
Compound the value. The team scales AI use cases and agents into production with our people alongside, and keeps improving them long after we step back.
Capability Development
Compound the advantage: we embed with the team and build the in-house fluency to scale AI use cases long after launch.
Capability that outlasts the engagement. Our certified practitioners embed alongside in-house teams, coaching through real delivery and building fluency at every level (from executive data literacy to hands-on tool training for analysts and engineers) so the team keeps improving on its own.
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.
Foundations & first value
Scale & self-service
Compounding advantage
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.
A fixed 8–16 week arc
Steering & transparency
De-risked by design
Built to hand over
Security and governance are built into every phase: see how we keep enterprise data safe.
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.
A defined scope, timeline, and price agreed up front. Best when the outcome is clear and budget certainty matters from day one.
How it works: We scope the work in a short, paid discovery, then commit to a price and a date.
- Scoped statement of work
- Milestones with decision gates
- Change control if scope moves
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
Embed our certified practitioners alongside the in-house team. 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
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
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
A SnowPro-certified team 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.
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 Snowflake partner status does Viewnear hold?+
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 hand in hand with Snowflake itself: aligned with the client's Snowflake account team on architecture and delivery, with early visibility into new capabilities (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 Viewnear?+
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 typical first production build runs 8–16 weeks, depending on data volume, source complexity, and the use cases in scope. What keeps that real: paid 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 does Viewnear de-risk a large engagement?+
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 Viewnear migrate an existing data warehouse to Snowflake?+
Yes. Migration is one of our most common engagements (Teradata, Oracle, Hadoop, SQL Server). We manage the full technical delivery and program governance.
Can Viewnear integrate Snowflake 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.
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 outcomes, not hours.
Does Viewnear 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 does Viewnear keep data secure?+
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.
Does Viewnear use third-party tools or stay native to Snowflake?+
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.
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.