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Under the Hood: grounding CoWork with Cortex Sense — not just prompting it

Your agent isn't wrong because your data is dirty. It's wrong because it doesn't know what your data means. An LLM can read your schema perfectly — table names, column types, row counts — and still have no idea that net revenue means gross revenue after discounts, that the fiscal year starts in February, or that "active customer" excludes anyone who churned last quarter. That gap between the schema an agent sees and the business meaning it doesn't is where confident, wrong answers come from. Closing it is now the single highest-leverage thing a data team can do for AI.

That's also the thesis Snowflake built its entire Summit 2026 agentic story around.

What actually changed at Summit 2026

Two things matter for anyone running agents on Snowflake:

Snowflake Intelligence is now CoWork. Same product lineage — the personal work agent that decomposes a question, researches across structured and unstructured data, and returns a cited answer — new name. If you saw Episode 3, this is the thing you already built against. Existing deployments migrate automatically.

Cortex Sense is the headline, and it's about accuracy, not features. Cortex Sense is a runtime context-enrichment layer: it automatically assembles business context — query history, object metadata, BI dashboards, and Horizon Context semantic views — and feeds it to CoWork and CoCo at query time, with no manual configuration. Snowflake's own internal benchmark puts the difference starkly: 47% accuracy on complex enterprise queries without it, 83% with it— and just 23% for frontier coding agents wired up through Snowflake's MCP connector alone. The message Snowflake is sending could not be clearer: context, not the model, determines agent quality.

We agree with that framing. But there are two catches, and they're exactly where a data team's real work lives.

The two catches nobody puts on the keynote slide

Catch #1 — Cortex Sense is private preview (as of June 2026). CoWork is shipping to enterprises now; Cortex Sense is not generally available yet. So the default CoWork deployment today operates closer to that 47% baseline, withoutSnowflake's own context infrastructure at production readiness. You can't wait for the feature to flip on and rescue answer quality before your stakeholders start trusting (or distrusting) the agent.

Catch #2 — even at GA, Cortex Sense is only as good as what's underneath it. Read the description again: it assembles context from your semantic views, metadata, and dashboards. If those definitions are missing, ambiguous, or contradictory, Cortex Sense faithfully assembles ambiguous context. And there's a deeper trap that governance alone never solves: access control is not correctness. RBAC enforces who can query the revenue table; it says nothing about whether that table is accurate, consistently defined, or current. An agent querying a revenue figure with an upstream ingestion error will return a confident, beautifully-cited, wrong number — and every guardrail will have done its job.

So the work is the same whether Cortex Sense is in preview or GA: you build the governed context layer and you make sure the data beneath it is actually right. The good news is that this work is not throwaway — the semantic layer you build now is precisely the substrate Cortex Sense consumes later. You're not waiting for the feature; you're getting ahead of it.

Here's how we build it.

Under the Hood: the context layer, step by step

Step 1 — Put the business definitions in a governed semantic view

A semantic view is a schema-level Snowflake object that maps physical columns to business concepts — facts, dimensions, and metrics — and stores the definitions natively, under RBAC, where both Cortex Analyst and (eventually) Cortex Sense read them. This is where you kill ambiguity once, centrally, instead of in fifty different dashboards.

The canonical example is the one Snowflake itself uses: revenue is physically stored in a column called amt_ttl_pre_dsc, but the business always means gross revenue after discounts. You encode that once:

CREATE OR REPLACE SEMANTIC VIEW analytics.sales.revenue_model
  TABLES (
    orders AS prod.sales.orders
      PRIMARY KEY (order_id)
      WITH SYNONYMS ('sales', 'bookings')
      COMMENT = 'One row per order line. Source of truth for revenue.',
    unit AS prod.sales.business_unit_dim
      PRIMARY KEY (unit_id)
  )
  RELATIONSHIPS (
    orders_to_unit AS orders (unit_id) REFERENCES unit (unit_id)
  )
  FACTS (
    orders.gross_amount   AS amt_ttl_pre_dsc,
    orders.discount_rate  AS disc_rate
  )
  DIMENSIONS (
    unit.unit_name    AS unit_name WITH SYNONYMS ('business unit', 'segment'),
    orders.order_date AS order_dt
  )
  METRICS (
    orders.net_revenue AS SUM(orders.gross_amount * (1 - orders.discount_rate))
      COMMENT = 'Net revenue = gross revenue after discounts. Use this for ALL
                 revenue reporting. Never sum amt_ttl_pre_dsc directly.'
  )
  COMMENT = 'Governed revenue model. These definitions are the single source
             of truth for agents and BI alike.';

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Now anyone — human or agent — asks the question the same way and gets the same number:

SELECT * FROM SEMANTIC_VIEW (
  analytics.sales.revenue_model
  METRICS    net_revenue
  DIMENSIONS unit_name
);

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Step 2 — Write your comments like prompts, because they are

This is the part that separates "it compiles" from "the agent is actually right." In a semantic view, Cortex Analyst reads your COMMENT text as instructions, not documentation. The comment on net_revenue above isn't a note for a future engineer — it's telling the model which column is not revenue. Be that explicit everywhere: define what a metric means, when to use it, and what to avoid. If you don't write it down, the model guesses, and a guess is how you get a wrong answer on clean data.

Two more high-leverage moves on the same object:

• Synonyms so "business unit," "segment," and "BU" all resolve to one dimension. Agents fail constantly on vocabulary mismatch; this fixes it cheaply.
• Verified queries — known-good question/SQL pairs that anchor the model on your hardest or most political metrics:

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-- inside CREATE SEMANTIC VIEW, after the COMMENT clause:
AI_VERIFIED_QUERIES (
  net_rev_by_unit AS (
    QUESTION  'What was net revenue by business unit last quarter?'
    VERIFIED_AT 1717200000
    VERIFIED_BY '(owner = data-platform@yourco.com)'
    SQL 'SELECT * FROM SEMANTIC_VIEW (analytics.sales.revenue_model
           METRICS net_revenue DIMENSIONS unit_name)'
  )
);

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One discipline worth stating plainly: only add verified queries you have actually validated. One wrong example teaches the model a bad habit at scale.

Step 3 — Measure the lift on your KPIs, don't take 47→83 on faith

Snowflake's benchmark is theirs, on their data. Before you tell your CFO the agent is trustworthy, prove it on your questions. Snowflake ships a Cortex Agent evaluation framework for exactly this — define a dataset of real questions with expected answers, then score the agent against it:

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evaluation:
  agent_params:
    agent_name: "revenue_agent"
    agent_type: "CORTEX AGENT"
  run_params:
    label: "Baseline — before semantic layer"
    source_metadata:
      type: "dataset"
      dataset_name: "kpi_eval_set"
  metrics:
    - answer_correctness        # how close the answer is to ground truth
    - tool_selection_accuracy   # did it call the right tools? (public preview)
    - logical_consistency       # reference-free; consistency across the run

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Run it once before the semantic layer exists, run it again after. The delta is your evidence — and your regression test. Wire it into CI so a careless change to a metric definition can't silently re-break answer quality next month.

Step 4 — Fix the data the context layer points at

A perfect semantic layer over a stale or half-loaded table still produces a wrong answer, just a well-defined one. So the context work has a twin: source-to-report reconciliation, freshness checks, and catching the broken or partial feeds that quietly poison a metric. That's a whole topic — it's Episode 5 — but flag it now, because "the agent gave the wrong number" is at least as often an ingestion problem as a semantics problem.

What this means, by role

If you lead data or analytics: the semantic layer is no longer a BI nicety — it's the accuracy substrate for every agent you're about to be asked to deploy. Building it now pays twice: better Cortex Analyst answers today, and a ready-made context source for Cortex Sense when it GAs.

If you're the architect or lead engineer: treat semantic views as strict contracts, not flexible SQL. Model relationships explicitly, comment like you're prompting, anchor hard metrics with verified queries, and put an evaluation set in CI. This is the build work that makes the demo survive contact with production.

If you own the platform strategy (VP / CDO): the question your stakeholders are really asking is "can we trust this for a real decision?" The honest answer is "only as far as our governed definitions and our data quality go." That's a roadmap, not a blocker — and it's a far better place to invest than another model evaluation.

How we'd approach it

Most teams we talk to don't have a context problem they can see — they have a trust problem they can feel: two dashboards disagree, an agent answer doesn't match the board deck, nobody's quite sure which number is right. The fix starts with finding where the definitions diverge and where the data underneath is wrong, before pointing any agent at it.

That's the shape of our AI-readiness assessment — a fixed-scope first step that maps your sources, definitions, and the gaps between what your reports say and what your data actually contains, so the agents you ship are accurate by construction. If your CoWork answers are landing in the "confident but wrong" zone, that's the place to start.

FAQ

Why do AI agents give wrong answers when the data is correct? Because they understand the schema, not the business meaning. Without governed definitions for metrics, fiscal calendars, and segment rules, the model infers them — and inference is where confident, wrong answers originate.

What is Snowflake Cortex Sense? A runtime context-enrichment layer announced at Summit 2026 (June 2) that automatically assembles business context — query history, metadata, BI dashboards, and semantic views — and supplies it to CoWork and CoCo at query time. Snowflake's internal benchmark reports it lifts accuracy from 47% to 83% on complex enterprise queries. It is in private preview as of June 2026.

Do I have to wait for Cortex Sense to improve my agents' accuracy? No. Cortex Sense draws on your semantic views and metadata. Building a governed semantic layer now improves Cortex Analyst answers today and becomes the exact substrate Cortex Sense consumes when it reaches GA.

Is governance the same as accuracy? No. RBAC and governance control who can access data; they do not certify that the data is correct, consistently defined, or current. An agent can be fully governed and still return a wrong number from a table with an upstream error.

Semantic view or legacy YAML semantic model? For new work, semantic views — they're native schema-level objects with full RBAC, sharing, and catalog support. Legacy YAML semantic models still work with Cortex Analyst for backward compatibility.

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Notes & sources: Cortex Sense status and the 47%→83% figure are from Snowflake's own materials and product announcements (Snowflake Summit 2026, June 2); Cortex Sense is in private preview as of June 2026, so treat the figure as a vendor benchmark and validate on your own data. Semantic-view DDL and the comment-as-instruction behavior follow Snowflake's CREATE SEMANTIC VIEW and semantic-view documentation; semantic-view SQL is stricter than ordinary SQL, so validate any DDL against current docs for your account version. Cortex Agent evaluation metrics (answer correctness, tool-selection accuracy, logical consistency) are from Snowflake's Cortex Agent evaluations documentation.

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Selecting the right Snowflake consulting partner determines whether your data platform becomes a strategic advantage or an operational burden. The consulting partner you choose will shape your platform maturity, AI capability, and cost efficiency for years.

This ranking evaluates Snowflake consulting partners based on delivery capability, AI readiness, and enterprise credibility. Each firm has been assessed on its ability to deliver governed, cost-efficient Snowflake environments that support advanced analytics and machine learning workloads at scale.

Who is a Snowflake consultant?

Before we rank anyone, let's define what these people do.

A Snowflake consultant is a specialist (often a cross-functional team) that designs, implements, and operates on the Snowflake AI Data Cloud. Their work covers:

• Implementation. Clear layers for staging, integration, and presentation. RBAC that matches your teams. Orchestration across multiple warehouses.
• Integration. Openflow pipelines for batch, streaming, and unstructured data. Change data capture at the source. Solid dbt practices.
• Optimisation and FinOps. Right-sized warehouses with the Saving Calculator. Cache aware scheduling. Autosuspend and autoresume settings that fit your workloads.
• AI enablement and Data Governance. Governed Cortex use cases. Clear evaluation methods. Cost guardrails for safe scale.
• Enablement. Documentation, runbooks, and training that lower long-term consulting spend.

A credible Snowflake consultant works a simple loop: baseline → diagnose → design → prove the delta. They pull your ACCOUNT_USAGE and Query History, map spend to pipelines and users, fix anti-patterns, and prove before/after cost and performance with your telemetry.

Why Snowflake consulting partners matter in 2026

Internal teams lack the implementation experience that Snowflake consultants bring from dozens of production deployments. Poor implementation decisions made early compound over time, creating technical debt that becomes expensive to remediate. The platform's architecture demands deep knowledge of cloud data warehousing, query optimization, and cost management that most organizations do not maintain internally.

The stakes have increased significantly with AI workload requirements. Modern Snowflake services must support machine learning pipelines, large language model integrations, and retrieval-augmented generation patterns. Governance and security requirements now demand comprehensive data lineage, access controls, and audit capabilities from day one. Cost optimization expertise can reduce monthly Snowflake spending by 30 to 50 percent through proper warehouse sizing and query tuning.

A qualified Snowflake consulting partner implements these controls during initial architecture design rather than retrofitting them later. The cost differential between proactive governance and reactive compliance can reach millions in enterprise environments. Legacy migration experience prevents data loss and performance degradation during cloud transitions.

Looking for Snowflake consulting? Check our Snowflake Consulting services and see how we help you.

What defines a top Snowflake consultant in 2026

Elite Snowflake consulting expertise requires specific technical competencies and operational maturity. The best Snowflake consultants demonstrate architecture mastery across warehouses, data sharing, and Snowpark implementations. Cost optimization capability that reduces monthly spend by 30 to 50 percent through query tuning and warehouse right-sizing separates competent from exceptional firms. AI workload implementation covering Cortex AI, ML model deployment, and vector search integration has become mandatory in 2026.

Critical capabilities that define elite Snowflake consultants:

• Governance expertise with role-based access controls, data masking, and automated lineage documentation
• Security controls implemented as standard practice that satisfy industry regulators
• Documented runbooks and escalation paths with production incident resolution within defined SLAs
• Regular optimization reviews identifying cost reduction opportunities before clients request them

The best firms demonstrate strategic judgment about when not to use certain features. They push back on unnecessary complexity and recommend simpler patterns that deliver equivalent business value. This judgment comes from extensive implementation experience across multiple client environments and industry verticals.

Snowflake consulting partner comparison table

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The best Snowflake consulting partners in 2026

1. Snowstack

Snowstack operates as a Snowflake-first consulting firm delivering Platform Team as a Service. The firm compresses typical 12-month projects into 90-day engagements from discovery to production using their proprietary framework and how we deliver. Client outcomes include 30 to 50 percent cost reduction and 80 percent faster reporting cycles across pharma, financial services, and FMCG implementations.

Key differentiators:

• FinOps cost optimization and AI governance specialization with senior architects on every engagement
• Multi-petabyte data volume handling with systematic knowledge transfer to internal teams
• Rapid enterprise implementations handling discovery through production in compressed timeframes

2. Slalom

Slalom earned recognition as Snowflake's Global Data Cloud Services AI Partner of the Year 2025. The firm excels at integrating machine learning workflows into Snowflake environments and building real-time analytics dashboards with collaborative consulting methodology.

3. phData

phData maintains exclusive focus on data engineering and analytics with multiple Snowflake Partner of the Year awards including 2025 Americas recognition. The firm offers comprehensive Snowflake consulting spanning strategy, implementation, and managed services with hundreds of certified engineers.

4. Cognizant

Cognizant operates as Snowflake's Global Data Cloud Services Implementation Partner of the Year 2025. The firm brings Fortune 500 scale with proprietary Data Estate Migration toolkit for legacy warehouse transitions and global delivery capability.

5. Accenture

Accenture maintains Elite Snowflake partner status with full-service capabilities from strategy through managed operations. The firm has developed industry accelerators that reduce implementation timelines with particular strength in marketing analytics and advertising use cases.

6. Deloitte

Deloitte combines Big Four strategic advisory with technical Snowflake implementation capability. The firm's Insight Driven Organization framework aligns platform projects with business measurement systems and objectives, particularly in finance, retail, and public sector.

7. Krish Technolabs

Krish Technolabs operates as a certified Snowflake partner with expertise in AI-driven analytics and multi-cloud deployments. The firm delivers comprehensive Snowflake services with focus on predictive modeling and LLM-powered insights for enterprise datasets.

‍8. Wipro

Wipro operates as an Elite Snowflake partner with a dedicated Center of Excellence supporting over 100 client implementations. The firm brings strong execution capability with global delivery scale for complex enterprise deployments in banking, consumer goods, and manufacturing.

The questions you must ask before signing a Snowflake consultant

Technical Competence:

1. "Walk me through your Snowflake implementation methodology"
2. "Can you show me sanitized architecture diagrams from similar projects?"
3. "What's your approach to FinOps and cost optimization?"

Delivery Model:

1. "Who will actually be on my project team day-to-day?"
2. "What's your knowledge transfer approach?"

Pricing & Scope:

1. "What's included vs. out of scope?"
2. "What happens if the project runs over budget?"

Why choose Snowstack for high-impact Snowflake consulting in 2026

Choosing the right Snowflake partner is not easy. When you’re comparing Snowflake partners, it helps to talk to someone who isn’t trying to sell you a 12-month transformation on day one. If you’d like a second opinion on your shortlist, your current proposals, or whether you should even bring in a GSI vs a specialist, let’s chat. At Snowstack, we combine deep Snowflake expertise with proven delivery methods, transparent team structures, and a focus on long-term governance and optimization. Our Snowflake experts deliver production-ready environments in 90 days while larger consultancies require 12 to 18 months for equivalent capability.

Second is cost optimization delivered as core methodology rather than optional add-on. Every Snowstack engagement includes FinOps analysis that identifies 30 to 50 percent spending reduction through warehouse right-sizing, query optimization, and automated scaling policies. Most Snowflake consultants treat cost management as afterthought, creating expensive platforms that require subsequent optimization projects.

Third is AI readiness embedded in architecture from day one. Snowstack implementations support Cortex AI integration, vector search capabilities, and machine learning pipeline deployment without requiring platform redesign. Firms focused on legacy data warehouse patterns deliver environments that need expensive rework when organizations advance AI initiatives.

The Platform Team as a Service model provides ongoing senior architect access rather than transitioning to junior support resources post-implementation. This continuity ensures optimization opportunities get identified and implemented proactively. Industries with strict governance requirements including pharma and financial services benefit from Snowstack's compliance framework expertise built into initial architecture rather than retrofitted later.

Contact us to discuss your specific requirements!

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Let's be honest. Your current database was most likely built for monthly reports, not AI products that demand regular updates and reports all the time. This is the reason why, in 2025, really innovative and data-driven businesses continue their migration away from legacy databases like Oracle, Teradata, SQL Server, and on-premises MySQL/PostgreSQL toward modern cloud-native architectures. Snowflake has become the industry leader, powering analytics and AI workloads across finance, retail, technology, and enterprise sectors.

This guide breaks down 7 core Snowflake capabilities and shows how the right Snowflake consulting can turn them into best results for your teams.

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What is the legacy database challenge?

Before diving into Snowflake's capabilities, it's crucial to understand the limitations organisations face with traditional databases. Therefore, let’s consider the scenario of a global FMCG company operating in multiple regions, where we helped transform the data infrastructure from legacy on-prem systems to

With our expert Snowflake migration services, the company moved to Snowflake + dbt + Fivetran + Tableau as a modern data stack.

Talk to our Snowflake consultant →

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The 7 core Snowflake capabilities in 2025

1. Multi-cluster shared data architecture

The fundamental differentiator: Snowflake's three-layer architecture completely separates storage from compute resources.

Key benefits:

• Unlimited concurrency
• Auto-scaling virtual warehouses
• Near-zero locking and contention
• Pay-as-you-use compute

This means analysts, data scientists, and applications can work in parallel on the same datasets without contention.

Business impact:

You no longer have to buy extra storage just to get more compute. You scale up when you need power, scale down when you don’t, and you can see what that means for your bill in minutes with our FinOps savings calculator

2. Cross-cloud & multi-region replication

This Snowflake capability is critical for regulated industries (financial services, healthcare, insurance) and companies with international operations requiring data sovereignty compliance.

Snowflake delivers:

• Multi-cloud availability on AWS, Azure, and Google Cloud Platform
• Easy cross-region replication and failover
• Global application distribution
• Built-in disaster recovery without complex configuration

Plan residency, failover, and recovery during **platform architecture,** then implement Snowflake like a pro.

Business impact:

A global FMCG company can maintain synchronised data across North American, European, and Asian markets while meeting local data residency requirements. This is difficult to achieve with legacy on-premises databases.

3. Zero-copy cloning & time travel

Snowflake's innovative approach to data management enables instant environment creation with zero additional storage costs.

Game-changing features:

• Clone terabyte-scale databases in seconds without duplicating data
• Time Travel for historical queries and point-in-time recovery
• Safe dev/test environment provisioning without impacting production

Development teams can spin up complete production-like environments instantly for testing, while legacy databases require duplicated environments that consume massive storage and take hours or days to provision.

Business impact:

Data engineers can test complex transformations on production-scale data without risk, dramatically accelerating development cycles and improving data reliability.

4. Built-in governance & RBAC security

In 2025, data governance and security are business-critical requirements for compliance and risk management.

Snowflake's security framework includes:

• Fine-grained access control with row-level and column-level masking
• Data lineage and classification for understanding data provenance
• Policy-based access control with external tokenisation partner support
• Automatic encryption at rest and in transit
• Dynamic data masking to protect sensitive information
• Audit logging and monitoring for compliance reporting

These are essential for organisations operating under SOC 2, HIPAA, GDPR, PCI DSS.

5. Native AI & Python ecosystem

Snowflake has built-in support for Python and machine learning, so your team can build and run models where the data already lives instead of exporting them elsewhere. With solid AI and data governance in place, it becomes easier to try new ideas safely and move them into production. The key building blocks are:

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Business impact:

This means that teams can train, deploy & serve ML models securely inside Snowflake. Data scientists spend less time on data engineering and infrastructure management and more time building models that drive business value.

6. Marketplace & data sharing economy

The Snowflake Marketplace reshapes how enterprises access 3rd-party data (functioning as the "App Store for data"). We are looking at:

• Thousands of data providers covering financial data, geospatial information, retail insights, weather patterns, ESG metrics, and logistics intelligence
• Live data feeds without pipelines (No ETL required)
• Private data exchange across subsidiaries, partners, and customers

Business impact:

You can now achieve faster analytics, better forecasting, and smarter decisions by instantly accessing external data sources that would traditionally require weeks of negotiation, integration work, and ongoing pipeline maintenance.

7. Extensibility: unistore & native apps

Snowflake is no longer just a data warehouse. In 2025, it can also handle simple day-to-day transactions and apps that run directly on your data.

Next-generation capabilities:

• Unistore for OLTP-lite workloads, enabling hybrid transactional/analytical processing
• Snowflake Native Apps for custom application development
• Streamlit integration for building interactive data applications
• Real-time data pipelines via Kafka connectors and Snowpipe Streaming

Business impact:

Snowflake serves hybrid workloads that legacy databases struggle to handle without significant operational complexity. Organisations consolidate their data infrastructure rather than maintaining separate systems for transactional and analytical workloads.

Real-world example: Snowflake consulting & migration results

Here’s what the shift looks like in practice. In a recent Snowflake project with a global FMCG company, we rebuilt the analytics backbone by establishing a governed core data model, automating ingestion and orchestration with native services and partner connectors, and reconnecting BI directly to a single, auditable source of truth. As seen in the table below, the result was a step-change in reliability and speed.

Documented results from migration to Snowflake:

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Beyond the database

Snowflake’s strengths include a unique design, flexible scaling, strong access and security controls, built-in AI features, and safe sharing across regions, which make it more than a database. It is a modern cloud data platform that powers predictive analytics, self-service reporting so product teams can trust the data and use it with ease. In business, the faster you get answers, the stronger your advantage, and Snowflake is setting the standard for company data platforms.

If you are choosing a data platform in 2025, plan for what you will need next year as well as today. Snowflake’s design is built for an AI-ready cloud-based future. We help you make that future real by setting up Snowflake, connecting your data, putting clear access rules in place, and keeping costs under control with a simple 90-day plan that we build with your team.

Ready to turn Snowflake into results?

Book a 30 minute call with our Snowflake consultant →

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