Questions, answered.

You are ready when your operational data lives somewhere you trust, someone owns it, and you can name one specific decision better data would change this quarter. If your data is spread across five systems and nobody reconciles them, AI will produce confident answers built on numbers nobody can defend. We publish a free five-question assessment that scores exactly this, with the full rubric shown, and no email required to see your result.

Most pilots die because the people who built them left before production. A consultancy runs discovery, delivers a proof of concept, and hands off; your team inherits a prototype that was never designed to run. We work the opposite way: we join the team, ship working code weekly on your own infrastructure, and stay to operate it. If the honest answer is that a use case should be killed, we say so and document why.

Then we tell you, in writing, and you keep the analysis. We have formally advised a client not to build AI on their data until the underlying data-trust work was finished, and we have built three machine-learning models, tested them, found the accuracy was an artifact of leaked labels, and refused to ship any of them. Not building is a real outcome we are paid to reach.

This is the right question to ask a small firm. Two things protect you. First, everything runs on your infrastructure in your accounts, so you always hold the systems, the code, and the documentation regardless of what happens to us. Second, every engagement keeps a written decision log, so the reasoning behind each choice is recoverable by whoever picks it up next. You are never holding a system only one person understands.

PurviewX gets stalled AI and data work into production, or tells you honestly why it should be killed. We join a client's team, build production data and AI systems on the client's own cloud, and stay to operate them. We work across energy, insurance, distribution, legal, enterprise security, and workplace safety. We also measure whether those systems actually work, and we publish the cases where they did not: three models we refused to ship, a cost estimate we revised down 5x, and a productivity finding we corrected from 31% to 43% after our own audit found the first number was wrong.

A traditional AI consultant runs a discovery phase, delivers a proof of concept and a recommendations deck, and moves on. A fractional chief AI officer typically advises at the strategy level without building. PurviewX does the hands-on version of both: one embedded operator who learns the business, writes the code, ships it to production on your infrastructure, and stays to run it. The deliverable is a working system, not a strategy document.

PurviewX publishes its prices rather than quoting on request. A two-week Production Readiness Diagnostic is $12,500, applied in full as a credit against the first invoice of a build engagement begun within 90 days. Embedded build engagements run $18,000 to $24,000 per month on a six-month initial term and then month to month on thirty days' notice. Enterprise starts at $35,000 per month. AI and token spend is billed at cost with no markup, under a not-to-exceed cap agreed in advance.

The client owns everything. PurviewX builds on your cloud, in your accounts, using your infrastructure. When an engagement ends, all code, pipelines, and documentation belong to you. There are no proprietary platforms to license and no vendor lock-in. A build engagement runs a six-month initial term and then continues month to month on thirty days' notice. Whenever it ends, you keep every working system.

Embedded AI leadership is a model where an AI leader joins your team directly (attending standups, learning the business, mapping the data) for 2 to 4 weeks before writing any code, then builds and operates production AI systems on your own infrastructure. Unlike traditional consulting that delivers a deck and leaves, embedded AI leadership stays to ship systems into production and continues to operate them.

According to MIT's Project NANDA report, The GenAI Divide: State of AI in Business (2025), 95% of enterprise AI pilots deliver zero measurable return. The pilots usually don't fail because the technology is bad. They fail because the incentive structure rewards impressive demos over production systems. The demo becomes the deliverable, and what's possible never becomes what's running. PurviewX defines success as a system processing real data in production.

Yes, and we write them for the people who have to follow them rather than for a compliance binder. We built one for a 200-person organization in about three weeks, with different guidance for field operations, customer service, administrative, and management roles, plus scenario-based training. Every policy ships with a built-in review cadence, because any AI policy needs a version two within a year. Governance that does not include its own maintenance becomes shelfware.

An engagement starts with a one-hour conversation, then a 2 to 4 week embed where we learn the business and map the data before writing code, then a build phase of roughly 8 to 16 weeks with working code shipped every week. You see something running in the first weeks of the build, not at the end. After launch we stay to operate, optimize, and expand it.

Companies sitting on large operational datasets, most often where the data is spread across several systems that disagree with each other. Work to date spans energy, insurance, distribution, legal, enterprise security, and workplace safety. Examples include a water-quality product measuring roughly 300 Connecticut ZIP codes against federal legal limits, an insurance data unification across five platforms, a distribution data-enrichment project covering 101,862 properties, and a South Florida court intelligence pipeline rebuilt onto a county bulk feed.

PurviewX was founded by Alexander Snyder, an embedded AI leader who has shipped production systems across six industries and three clouds. PurviewX is based in Portland, Oregon, and works with clients nationwide.