We build the data foundation first, then put AI to work on it.
We work with your team so leaders decide from one set of numbers everyone trusts. Then AI takes routine work off people's desks and speeds up daily operations, with a person approving what matters.
The AI works in a demo and is still outside daily operations.
Production workflows cross applications and depend on permissions, rules, approvals and human judgment. Getting there takes engineering around the model.
The first step can be small: start with a readiness session, or bring one workflow and we map how Data + AI could improve it.
Data + AI readiness session
We assess the current environment across data, architecture, governance, AI, security, integration and operational readiness. The session delivers a practical picture of what is ready today, what needs work, and where the highest-value opportunities are.
Bring one process that takes too much time, needs heavy manual coordination or depends on institutional knowledge. We map the systems, data, decisions, documents and people involved, then outline how the data foundation and Claude could support or automate it.
Installation scheduling from 17 hours a week to under two
Getting install crews scheduled ran across five disconnected systems and cost 13 to 17 hours a week. A custom AI agent does the gathering through a connector into the system of record, and a full day of installs is signed off in about a minute.
From hand-sorted documents to 95% accurate claims classification in seconds
Sorting documents from many insurance providers by hand caused delays, errors, and lost files. Using Snowflake Cortex AI functions like AI_EXTRACT, Viewnear automated classification and extraction, lifting accuracy from 60% to 95% and cutting per-document handling to four seconds.
Advisory and agentic engineering for teams that already have part of the work in hand, and a full transformation when the data foundation and the agents both need to be built.
01Data + AI advisory
We find where data and AI pay off first, and plan the build with your team.
Workshops and an operations diagnosis
Readiness assessment, with the KPIs to track
Use cases ranked by value, and an architecture blueprint
Each engagement can run as a fixed-cost build, time and materials or a dedicated team, and the build can continue as a managed service.How pricing works→How an engagement runs→
Our team is already in place, so the work starts right after discovery.
A nearshore team of data and AI architects and engineers in Monterrey, Mexico and Austin, Texas, working on Central Time during your business day. The budget goes straight into use-case sprints.