Enterprise AI Deployment
We put AI into production inside your business.
Whether you have a failed AI pilot to rescue or a mandate to adopt AI and no clear place to start, we help you map what your business actually needs and stand up production-grade AI inside your own infrastructure. You own it, and its IP value compounds over time. That matters most in regulated industries, where your data never has to leave your network, every answer traces back to a source someone can audit, and the system escalates to a human rather than guessing. We only call it success when your P&L moves.
The 95% problem
The reason 95% of enterprise AI pilots never move the P&L
Track record
We are operators, not consultants.
Neutron Enterprise is led by Ryan Junee, a serial founder who has built and sold three companies, with exits to YouTube, Veeva Systems, and CAI Software. Through those companies, his software has been deployed into dozens of Fortune 500 operations, the large, complex, and often regulated environments this work is built for. You get that experience leading your engagement directly, backed by a team we scale to the work.
Through our prior companies (Omnisio, Parsable, Ostro) our work has shipped into:
What we build
We supply all seven pieces of a production AI system, inside your own network.
Independent research (MIT, 2025) put roughly 95% of enterprise AI pilots at no measurable impact on the bottom line, and the reason is almost never the model being too weak. A model is about 20% of a working system. The other 80% is the knowledge layer, agent orchestration, integrations, a measurement and evaluation harness, code-enforced governance, model independence, and a compounding flywheel, the seven pieces that were never built. We supply all seven, foundation first, before any agent, deployed inside your own infrastructure and tailored to your systems and workflows. It is built for you, rather than a product you bend your operation around. Done right the result behaves like a strong new hire, modest on day one and close to indispensable by month twelve, except its knowledge stays when people leave and every action it takes is logged.
- 1. Knowledge Layer ("brain") The brain indexes your CRM, ERP, ticketing, and documents, and every answer carries citations back to the source. Truth stays in your systems of record: the brain reads and cites them and never becomes a stale shadow copy. When the evidence is thin it says so and routes to a person, rather than inventing a confident answer. No more guessing
- 2. Agent Orchestration Durable agents that carry work from end to end and coordinate across steps and systems, not one-shot chatbots. They take real actions through the governance layer, so one system does the work that per-seat assistants never will. Beyond per-seat
- 3. Integrations Deep read and write connections into the systems you already run: CRM, ERP, ticketing, the data warehouse, and documents. The system works inside your stack instead of becoming one more portal nobody opens. Inside your stack
- 4. Measurement & Evaluation Harness Accuracy is tracked against a test set your own experts sign off on. Any change that would make answers worse is blocked before it ships. You watch the score instead of hoping. So it can't silently decay
- 5. Code-Enforced Governance Agents act through a policy engine rather than by convention, with human approval wherever it matters. Every action is attributable, auditable, and replayable, so the system can be handed real work. Auditable
- 6. Structural Model Independence Every model call routes through one gateway you control. Swapping Claude for GPT, Gemini, or an open-weight model on your own GPUs is a config change we prove with an eval run, so you are never locked to a single AI vendor. No lock-in
- 7. Compounding Flywheel Every correction your team makes becomes a reviewed improvement to the knowledge, the retrieval, and the policy. The system is worth more at month twelve than on day one. Compounds
All seven run as one system inside your own network, your cloud VPC or your data center, built on open-source components and your own code, which you own. Any modules we reuse to get you live faster come with it under a perpetual license at no extra cost, with full source, so you can run or rebuild them yourself. No SaaS in the critical path, no per-seat license.
High-value workflows
Engineered for P&L impact.
Once the foundation is in place, the same system reaches into the platforms your business already runs on, your ERP, CRM, data warehouse, and documents, and takes governed action inside them. These are examples of where it moves real money. The actual set comes out of your Audit, and it can be any custom workflow your business needs:
- Customer support Deflection
Agents answer from your own docs, past tickets, and product data, each reply carrying a citation, draft or send within policy, and hand off to a person the moment the evidence runs thin.
- Sales & account management Pipeline
Agents pull account history from your CRM, draft the next touch, keep records current, brief the rep before every call, and flag accounts going quiet before they slip.
- Finance & procurement Margin
Agents read purchase orders, contracts, and invoices from your ERP, match three ways, catch off-contract and tail spend, watch budget variance, and route the exceptions while they are still actionable.
- Operations & coordination Throughput
Agents carry the cross-system busywork between handoffs, chase the exceptions, and keep work moving so nothing stalls waiting on someone to pull a report.
- Predictive maintenance & uptime Downtime
Agents watch equipment and asset data against maintenance history, flag a failure signature before it causes a stoppage, check parts and availability, and open the work order. Unplanned downtime is often the single biggest controllable cost.
- Inventory & demand planning Working capital
Agents align stock, replenishment, and forecasts to real demand across your systems, so you stop parking cash in excess inventory and stop losing margin to stockouts and expedites.
- Compliance & document review Audit-ready
Agents review documents against your policies and specs, assemble and gap-check the filings and reports that eat expert time, and keep a complete, inspection-ready audit trail, with a person signing anything that needs judgment.
- Real-time dashboards & reporting On demand
Agents stand up the live dashboards and one-off pulls that usually wait on an analyst, drawn from your warehouse with the query shown, so anyone can ask a question of the business and get a current answer.
These are examples, not a menu. We build whatever custom workflow moves your P&L: the Audit surfaces the handful that matter most, and we build those on the same governed foundation inside your own infrastructure, every action logged and attributable.
What changes
What it looks like when the system is working.
Speed
Coordination stops being the bottleneck
Work that used to sit in a queue for days, waiting on someone to reconcile three systems by hand, moves in minutes. Small problems get caught while they are still small.
Leverage
More output from the same team
You get more done without a headcount to match it. Your people spend their hours on the calls that need a human while the system carries the repetitive coordination.
Compounds
It gets better the longer you run it
Every decision and correction becomes signal the system learns from on your specific work. A competitor who starts later cannot buy back the months of your data it has already absorbed.
The asset you own
This becomes one of the most valuable things your company owns.
Most AI spend rents someone else's product by the seat, and it shows up as an expense that never accrues to you. What we build is different on the balance sheet. The system is custom, built for you on open-source components and your own code, and you own it: the data, the configuration, the integrations, the audit history, the improvement history, and the eval sets, the compounding brain of your business. The modules we reuse to ship faster come with it under a perpetual license at no extra cost, with full source, so you can run or rebuild them yourself. It runs in your own network. There is no subscription anyone can switch off, and no vendor who can take it away.
Why it compounds
Real usage trains the knowledge, the retrieval, and the policy, so the system gets sharper every week it runs. The improvement history it builds, the golden set, the correction corpus, and the eval sets tuned to your business, is an asset you own and cannot buy anywhere. A rented seat is worth the same at day 365 as at day 1. This is worth more, because the asset compounds. At year three, one path has receipts and an asset. The other has receipts.
Regulated industries
Built for environments where AI has to be provably safe.
Pharma, financial services, energy, healthcare, and government cannot put AI into production on someone else's cloud with no record of what it did. The architecture is built for exactly those constraints, because the deployments behind it were too.
- No data egress Your boundary
Your data and the models run inside your own network. Nothing is shipped to a third-party API and there is no vendor cloud in the critical path. What is yours never leaves.
- Immutable audit trails Provable
Every action is written to an append-only, tamper-evident log: who or what acted, when, and the evidence it acted on. Attributable and replayable for any auditor or inspector, months later.
- Sovereign by design You own it
It runs on your own infrastructure, and where you need it, your own GPUs and open weights, fully air-gappable. No single AI vendor to depend on and no kill switch anyone else holds.
- Human oversight built in In the loop
A policy engine decides what agents are allowed to do, with a person approving anything that carries real risk. Authority is scoped and granted, never assumed.
- Designed for strict regulation By design
Governance, immutable audit trails, data residency, and human oversight are designed in from day one, so the system stands up to the controls that regulated industries demand instead of being retrofitted later to pass an audit.
- Proven where it counts Track record
Our software has already run in pharmaceutical GxP environments and for state-owned critical national infrastructure. We build where the margin for error is zero.
How the work takes shape
Four steps, each one earning the next.
You are never asked to commit to the whole thing up front. Each stage produces something you keep, and each one gives you a clean place to stop.
01
Audit
Where to start
2-4 weeksIn two to four weeks we interview your team and find the five to ten places where AI moves a KPI you already report on. You get a sequenced roadmap, a KPI map with baselines and dollar impact, and a board-ready one-pager. Every opportunity names the number it moves and who owns it, or it does not get built.
02
Prototype sprint
Proof on your data
1-2 weeksOne use case, built on your own data, working in one to two weeks rather than quarters. If the demo does not convince you, we stop. If it does, the fee credits fully toward the build.
03
Build
Foundation first
PhasedWe redesign the workflow first, then build the knowledge foundation before any agent. Agents act through a policy engine with human approval where it matters, models and retrieval are tuned on your own data, and every change is gated against a test set your experts sign. You see working software each week. No slides.
04
Operate
It compounds
OngoingWe run the improvement flywheel, re-evaluate on every major model release and swap when the numbers say so, and send a monthly report that shows the KPI moving. Month to month after the first quarter.
The team stays small and flexible: only the people your engagement needs, led directly by Ryan, with specialists brought in as the work calls for them. You get an operator's full attention on your P&L, not a junior analyst working from a playbook.
A low-risk first step
The first step is small, fixed, and proven before anything goes live.
Nobody should have to commit to a full build to find out whether it pays off. The first engagement is deliberately small: one workflow, a fixed scope, tested against your real data before a single thing reaches production, with a clean place to stop at every step.
- One workflow, fixed scope
A single, clearly bounded first use case with a defined deliverable and timeline. Nothing open-ended.
- You see it on your data first
It runs against your real data where you can judge it before it touches production. If it does not convince you we stop, and if it does the sprint fee credits toward the build.
- Backed by numbers, not claims
Nothing ships without accuracy benchmarks and failure-mode coverage that your own experts have signed off on.
- You keep everything
Whatever gets built runs in your own infrastructure with full source, version-controlled and reversible, owned by you from day one.
Let's find where AI actually pays off in your business.
It starts with one 20-minute call. You leave knowing the two or three places AI would move a real number in your business, whether or not we ever work together.