Anthropic and Viewnear
A Claude Certified Partner in the Claude Partner NetworkAgentic solutions. Governed data. One team that ships Claude to production.
Viewnear is a Claude Certified Partner in the Claude Partner Network, one of the teams that takes Claude from a prototype to an agent in production, with a person approving what matters. Claude Certified Architects, Claude Code in the build, Model Context Protocol (MCP) connectors into the systems a business already runs, and governed data underneath, so the agent starts with the right data.
Two ways we put Claude to work
On the work still done by hand, and inside the data it depends on
Put Claude on the work still done by hand
Every business has processes that still run on someone reading, checking and typing. We put Claude on them: the agent reads the request, checks the system of record through an MCP connector scoped to the objects the process needs, works out the next step and scores its confidence. Above the threshold it acts; below it, the case goes to a person with the reasoning shown. Fixed business rules handle what has to be exact; Claude handles the judgment rules cannot.
Run Claude inside the data
Where the data lives in Snowflake, Claude runs there too: as a Cortex model next to governed data, inside Cortex Agents, and under Cortex AI Gateway, which governs which agents reach which data, models and tools. The same model on both planes, so the behavior a team trusts in a demo is the behavior that ships, and the agent starts with data the business already trusts.
Autonomy, calibrated
Autonomous where it is confident, reviewed where it is not
The threshold is the design. Every proposed step carries a confidence signal, and a number tuned per process decides whether it executes on its own or goes to a person. Human-in-the-loop is what happens below the line, not a queue every action waits in.
Read the request
The agent takes the request as it arrives, in the words the business already uses.
Check the record
It reads the system of record through an MCP connector scoped to the objects that process needs, and nothing else.
Score the step
It works out the next action and scores its own confidence against the evidence it found.
Clear the threshold
Above the line the step runs on its own. Below it, the case goes to the person who owns the process, with the reasoning shown.
Write it back
The step is written as a named identity, autonomous or approved, with every call logged either way.
The threshold starts conservative and moves on evidence: as the evals hold, more of the volume runs unattended and the exceptions are what people spend their time on.
What this looks like in practice
One named engagement, and the service built from it
Installation scheduling from 17 hours a week to under two
Magnolia Doors builds custom metalwork for high-end homes around San Antonio and installs it with its own crews, and getting those crews scheduled was costing 13 to 17 hours a week across five disconnected systems. Viewnear put Claude on the job through a custom Model Context Protocol connector into their system of record. Scheduling an installation now takes about three minutes instead of 23 to 35, a full day is approved in about a minute, and 15 to 19 administrative hours come back every week without adding a person.
Built with ClaudeRead case study→Data + AI, agents inside and around Snowflake
Process discovery, the connector into the system of record, the agent, the approval design and evaluation before scale, in 8 to 16 weeks. Built with Claude, Claude Code and MCP.
See the offerThe partnership at work
Claude brings the reasoning. Viewnear engineers the system around it.
A model on its own is a demo. This is what turns it into an agent a business can run.
Governed data underneath
An agent is only as trustworthy as the data it reads. The data practice comes first, usually on Snowflake, so the agent starts from numbers the business already stands behind.
Connectors into the system of record
Model Context Protocol (MCP) connectors built with Claude Code, granted the specific objects a process needs and nothing else, running as a named identity the client can revoke, with every call logged.
A threshold, not a rubber stamp
Each process gets a confidence line tuned to what it can tolerate. Above it the agent acts; below it a person decides, seeing exactly what would change, field by field. Planning stages cannot write at all; the step that can is confined to one object.
Evaluated before it scales
Evals measure accuracy against the production system before a use case widens, so scaling rests on evidence rather than a demo that went well once.
Claude Certified Architects
Anthropic's own certification on the build, alongside the SnowPro-certified engineers who govern the data, working US hours from Austin and Monterrey.
Documented, then kept improving
Documentation and runbooks ship with every agent. From there the team runs it, or Viewnear keeps running, evaluating and improving it as a managed service.
Third-party proof
“Viewnear has been building enterprise-grade production AI systems with governed Snowflake data anchored on Anthropic Claude.”
The people
Meet the team behind the Claude practice
Claude Certified Architects and the engineers who build with Claude Code, led by people who have put agents on real processes and answered for the result.
Led by our leadership team Meet the leadership team →
The agent starts with the right data. The other half of the story is the Snowflake Partner Network.
About the Snowflake partnershipTell us the process your team still does by hand.
We'll come back with the first agent, the data it needs, and a date.