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95% of enterprise AI pilots deliver zero measurable return.

We find out whether your AI
actually works. Including ours.

PurviewX is embedded AI leadership for companies sitting on real operational data. We join your team, ship production systems on your own cloud, and measure whether they actually work. Then we publish the ones that didn't, including our own.

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01 // THE REALITY

The AI landscape has a production problem.

95%

of enterprise AI pilots deliver zero measurable return

MIT Project NANDA, The GenAI Divide, 2025

74%

of CIOs regret a major AI vendor decision

Gartner / CIO Dive, 2025

40%+

of agentic AI projects will be canceled by 2027

Gartner, 2025

$52B

projected agentic AI market by 2030 (46% CAGR)

MarketsandMarkets, 2025

The market is flooded with AI consultancies selling pilots. The companies winning are the ones who ship production systems and stay to operate them.

02 // THE RECEIPTS

The receipts.

Most vendors show you the demo that worked. Here is the fuller record, corrections and refusals and all, with the real numbers. Including our own.

Corrected

31% → 43%

We restated our own productivity finding, upward.

Our first pass said field technicians were 31% productive. Our own follow-up audit found a measurement error: a proximity-matching fix showed most of the 'unproductive' hours were within 100 meters of a real customer. We corrected the number in the client's favor.

Corrected

1,290 → 906

We caught our own product serving unverified data as real.

In a product we built, much of the listing data had been inferred by a language model rather than observed, and our own 'verified' flag didn't mean what it said. We built a confidence spine that grades every listing by how it was obtained, and corrected the public inventory from 1,290 to 906 genuinely bookable.

Corrected

$1,500 → $234

We revised a bill down roughly 5x, unprompted.

We estimated a client's monthly AI token cost at $1,100 to $1,500. Actual spend came in at $234. We told them.

Corrected

19% waste

We quantified our own preventable waste and told the client.

A broken dedup query and backwards name parsing wasted credits on an enrichment job. We documented every dollar, reported 19% preventable waste, and fixed each cause. The client expanded the engagement.

Refused

3 models

We built three ML models and refused to ship any of them.

Under adversarial review, promising accuracy collapsed once we removed leaked labels and circular targets. We flagged all three as untrusted and kept them shadow-only, rather than put a confident-but-wrong model in front of a real decision.

Shipped

5 pivots

Four approaches failed in public before one worked.

A water-quality product went through leak detection, bacteria screening, and lab testing before the fifth iteration (real-time quality by ZIP with risk context) landed. It now processes 50,000+ calls a month.

Published

2 disproved

We published the experiments that disproved us.

Our AI research system, Edwin, filed a provisional patent with 10 claims validated against 35 experiments. Two of those experiments disproved what we expected. The filing says so.

The strongest thing we can show you is not a case study that worked. It is the record of what we caught, corrected, and refused, because that is the part almost nobody else will put in writing.

See the full production record

02 // THE STORY

How we got here.

2024

Started asking the wrong question.

I thought the challenge was technical: pick the right model, build the right pipeline, ship the dashboard. It wasn't. The technology was the easy part. The hard part was understanding the business well enough to know what to build.

The pattern

It's always the same problem.

Every company I touched had the same gap: operational data scattered across five tools, a leadership team making decisions on gut instinct, and a stack of AI vendor pitches that all sounded the same. Nobody was translating between the data and the business.

The pivot

From consultant to embedded partner.

I stopped showing up with slide decks. I started joining the 7am calls, learning every employee's name, sitting in on operations meetings. The work got better immediately. You can't build intelligence for a business you observe from the outside.

Now

Six clients. Zero pilots.

Systems running in production for six clients across six industries and three clouds, plus two products of our own. And a written record of the ones that didn't work: the models we killed, the numbers we got wrong the first time, the bill we told a client was too high. We stay long enough to find out which is which.

PurviewX exists because I kept seeing the same gap: companies that need AI leadership, not AI vendors. The difference is staying.

03 // THE WORK

Real work. Real numbers.

We don't show you polished case studies. We show you what we actually built, what broke, and what the numbers look like.

EnergyProduction

50,000+ calls/month. After five pivots.

A water quality product that nobody wanted, until the fifth iteration.

50,000+

Calls analyzed monthly

90%

Reduction in manual review

5

Product iterations

Lessons

  • First four approaches failed. We shipped all five.
  • Emotional hooks outperformed technical accuracy.
  • Client patience through five pivots is why this works.
InsuranceOngoing

Five platforms. One operational picture. Twelve weeks.

Data scattered across five systems. Zero unified customer view.

5

Data sources unified

12 weeks

Time to operational picture

Ongoing

Status

Lessons

  • The real work started after the first dashboard shipped.
  • Commission structures required raw data, not assumptions.
  • We're still there. That's the point.
DistributionExpanded

100K+ properties enriched. $0.05 per contact.

Broken dedup queries and backwards name parsing. We documented everything.

100K+

Properties enriched

$0.05

Cost per contact

Expanded

Engagement result

Lessons

  • 19% of spend was preventable waste. We quantified every dollar.
  • The client expanded after seeing the failure report.
  • Transparency is a better sales tool than a pitch deck.

05 // THE PROCESS

How it works.

No six-month discovery phases. No 50-page SOWs.

011 hour

The Conversation

An honest assessment. We'll tell you if embedded AI leadership is the right fit, or if you need something else entirely. No pitch deck.

  • Fit / no-fit assessment
  • Scope outline
  • Honest expectations
022 to 4 weeks

The Embed

We join your team before writing a single line of code. Learn the business, map the data, understand the people. This is where most consultancies skip ahead. We don't.

  • Data landscape audit
  • Stakeholder map
  • Priority backlog
038 to 16 weeks

The Build

Iterative development with working code every week. Documented failures alongside wins. You see everything.

  • Weekly working deployments
  • Documented decision log
  • Production-ready systems
04Ongoing

The Stay

We don't hand off and disappear. We operate, optimize, and expand. The best work happens after launch.

  • Operational monitoring
  • Continuous optimization
  • Expansion roadmap

We price on outcomes, not hours.

06 // THE METHOD

The Embedded Intelligence method.

Not a framework from a meeting. A pattern from doing the work.

Embed before you build.

Join the team before writing code. Learn every name, attend every standup, understand the business before touching the data. You can't build intelligence for a company you observe from the outside.

Fail in public.

Document every mistake. Quantify every dollar of waste. The client who saw our 19% waste report expanded the engagement. Transparency beats polish every time.

Ship to production or it didn't happen.

No pilots. No proofs of concept as final deliverables. No dashboards that nobody opens after the first week. If it's not running in production, processing real data, it doesn't count.

You own everything.

Your cloud. Your infrastructure. Your code. When an engagement ends, everything is yours. No recurring platform fees. No lock-in. No vendor dependency.

07 // THE LAB

The Lab

Edwin

Most AI consultancies bolt ChatGPT onto your problems and call it innovation. We built our own.

Edwin is 43,000 lines of Python, tested by another 36,000 lines of tests. It's an AI memory system that verifies what it stores, challenges what it believes, and catches 95% of false information before it enters memory. It uses multi-model adversarial verification (where AI models argue with each other to surface the truth), agentic pipelines, and cryptographic proof of knowledge state. A provisional patent was filed in February 2026 (USPTO 63/991,739), with 10 claims validated against 35 experiments. Two of those experiments disproved what we expected. We published them anyway.

Everything we learn building Edwin flows directly into client work. The same verification discipline that powers our R&D powers your data warehouse. Edwin is the reason we assume our own output is wrong until it survives a challenge, which is why we catch things that other firms miss, including in our own work.

We don't build on top of other people's AI. We build our own, and then we bring what we learn to you.

43,000

lines of Python

2,827

passing tests

95%

false info caught

1

patent pending

// FAQ

Questions, answered.

The honest version. No sales spin.

What is embedded AI leadership?

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.

What does PurviewX do?

PurviewX provides embedded AI leadership for industries with large operational datasets, including energy, insurance, distribution, legal, enterprise security, and workplace safety. We join a client's team, build production data and AI systems on the client's own cloud, and stay to operate them. We also measure whether those systems actually work, and we publish the cases where they did not: three machine-learning models we built, tested, and refused to ship, a cost estimate we revised down by 5x, and a productivity finding we corrected from 31% to 43% after our own audit found the first number was wrong.

Why do most enterprise AI pilots fail?

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.

How is PurviewX different from a traditional AI consultancy?

A traditional consultancy runs a 6-month discovery, rotates 4 to 6 consultants, delivers a proof of concept plus a recommendations deck, and moves on after a knowledge-transfer session. PurviewX embeds for 2 to 4 weeks, deploys one embedded leader plus purpose-built AI agents, delivers a production system processing real data, and stays to operate, optimize, and expand it. With PurviewX, the client owns all code and infrastructure. There is no proprietary platform or lock-in.

Who founded PurviewX?

PurviewX was founded by Alexander Snyder, an embedded AI leader who has shipped production systems across six industries and three clouds: energy, insurance, distribution, legal, enterprise security, and workplace safety. PurviewX is based in Portland, Oregon, and works with clients nationwide.

What is Edwin?

Edwin is PurviewX's in-house AI research system: an AI memory architecture with 43,000 lines of Python, tested by another 36,000 lines of tests, of which 2,827 pass. A provisional patent was filed in February 2026 (USPTO 63/991,739), with 10 claims validated against 35 experiments, 2 of which disproved what we expected. It uses multi-model adversarial verification (where AI models argue with each other to surface the truth) and catches 95% of false information before it enters memory. The engineering discipline behind Edwin flows directly into PurviewX client work.

How much does PurviewX cost?

PurviewX prices on outcomes, not hours. Engagements begin with a one-hour conversation to assess fit, followed by a 2 to 4 week embed, an 8 to 16 week build phase, and an ongoing operate-and-expand relationship. Clients build on their own infrastructure and own everything, so there are no recurring platform or license fees.

Who owns the code and systems PurviewX builds?

The client owns everything. PurviewX builds on the client's cloud, in the client's accounts, using the client's infrastructure. When an engagement ends, all code, pipelines, and documentation belong to the client. There are no proprietary platforms to license and no vendor lock-in.

What industries does PurviewX work with?

PurviewX works with companies that sit on large operational datasets, across six industries: energy, insurance, distribution, legal, enterprise security, and workplace safety. Examples include a water-quality product processing 50,000+ calls per month, an insurance data unification across five platforms delivered in 12 weeks, a distribution data-enrichment project covering 100,000+ properties at $0.05 per contact, a South Florida court intelligence pipeline delivering a 75% cost reduction, and a country-risk intelligence framework built on official government advisory sources.

How long does a PurviewX engagement take?

An engagement starts with a one-hour conversation, a 2 to 4 week embed where PurviewX learns the business and maps the data, an 8 to 16 week build phase with working code shipped every week, and then an ongoing operate-and-expand phase. PurviewX does not hand off and disappear. The best work happens after launch.

08 // START HERE

Let's talk.

Three ways to start.

Read

Browse our build logs and see how we think.

Read the blog

Quick Assessment

Five honest questions. See whether your data foundation is ready for production AI, no email required.

Take the assessment

Talk

One hour. No pitch deck. Just an honest conversation about your data.

Schedule a conversation

or email alexander@purviewx.ai