Pricing & engagement
Clear scope. No surprises.
Engagements are scoped to the data and goals at hand, and priced up front, whichever model fits. Here's how we structure the work and what shapes the investment.
How we work
Engagement models
Flexible ways to partner, matched to the shape of the problem.
A worked example
The shape of a typical first engagement: a paid discovery fixes scope and price, sprint demos show working software from the first weeks, and a governed foundation reaches production in 8–16 weeks, with a named team the sponsor meets before signing and in-house people in the work from sprint one.
What drives cost
Priced to the work, not a sticker
We don't publish one-size pricing because no two engagements are the same. Cost is shaped by:
- Data volume and the number/complexity of source systems
- How many analytics and AI use cases are in scope
- Team size and target timeline
- Governance, compliance, and security requirements
Practice adoption, by horizon
- First 8–16 weeks
Foundations & first value
The data practice stands up: scope fixed in a paid discovery, governance designed in from the first table, priority data flowing, and the first governed data products in production.
- 6–12 months
Scale & self-service
The AI practice ships: use cases spread across teams and each new one reaches first insight 60% faster. Self-service takes hold; manual reporting retires.
- 18+ months
Compounding advantage
Both practices are in-house: new use cases ship in weeks at 40% lower run cost, and the handover is real. The team runs and extends the work without us.
Proof
What an engagement looks like
From hand-sorted documents to 95% accurate claims classification in seconds
A claims processing company was sorting documents from many insurance providers by hand, causing delays, errors, and lost files. Using Snowflake Cortex AI functions like AI_EXTRACT, Viewnear automated classification and data extraction, lifting accuracy from 60% to 95% and cutting per-document handling to four seconds.
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Real-time insight into 20,000+ students across campuses, live in seven weeks
A group of universities serving 20,000+ students across Miami and Latin America had academic records and LMS learning events siloed across campuses. Viewnear built a governed, real-time student data pipeline on Snowflake: a governed environment, native Blackboard Data Share integration, and real-time Caliper event streaming, unifying academic and learning-activity data into one governed source.
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One trusted golden record across eight business domains
A commercial-vehicle dealer group ran sales, service, parts, and the back office on separate systems, with no single version of a customer, vehicle, part, or supplier. Viewnear built a corporate master data foundation on Snowflake: a progressive multi-source golden record across eight business domains, using a Medallion architecture, Openflow ingestion, dbt transformations, and per-domain Snowflake CoWork agents.
Read moreLet's scope it together.
Tell us the goals and constraints, and we'll come back with a model, a plan, and a price ready for the board.