For private equity operating teams
One trusted AI bench for the whole portfolio
Scope the right AI work, match each portfolio company with specialists who have shipped it before, and keep one accountable standard from first pilot to production.
Every introduction is backed by verified production work, reference checks, and a defined specialty.
- First step
- 15 min scope
- Minimum proof
- 2+ deployments
- Best fit
- $25k+ projects
- Typical pace
- Days not weeks
The portfolio problem
AI opportunity is distributed. The operating standard cannot be.
Portfolio teams need a repeatable way to find the right work, source the right expertise, and keep delivery accountable across companies with very different maturity.
Prioritization
Different companies, different AI starting lines
Separate real operating leverage from executive pressure, vendor noise, and disconnected pilots.
Execution
The right specialist changes by use case
A data-platform rebuild, a support agent, and an underwriting workflow should not go to the same generalist team.
Governance
Speed still needs an accountable standard
Move quickly without leaving each portfolio company to invent its own bar for security, quality, and production readiness.
Capacity
Internal teams need judgment, not another queue
Give operators access to senior AI leadership and delivery capacity without forcing every company to hire a full team first.
One operating model
From scattered AI requests to a portfolio-wide delivery lane.
A lightweight front door for operators, with specialist matching behind it.
01
Scope the portfolio
Align on value-creation priorities, company readiness, budget bands, and the decisions the operating team needs to make.
02
Match the work
Neuronify selects two to three vetted specialists or boutique teams for each scoped need — never a directory or bidding pool.
03
Stay accountable
Keep one standard for introductions and engagement health while each portfolio company works directly with the team it chooses.
Where the network fits
Match expertise to the actual constraint.
Use Neuronify for one urgent portfolio-company build or as a consistent specialist bench across the fund.
Specialists only. No generalists presented as AI experts.
01
Portfolio diagnostics
Identify the companies and workflows where AI can create measurable value now.
02
First production workflow
Take one high-value process from scope to a working production system.
03
Data, RAG & agent builds
Match specialized engineers to systems that must work with real data, controls, and users.
04
Fractional AI leadership
Add senior judgment for roadmaps, vendor decisions, hiring, and delivery oversight.
Across the hold period
The AI question changes at every stage of the hold.
Diligence, the first hundred days, mid-hold standardization, and exit preparation each ask something different of the operating team. Treating them as one undifferentiated “AI initiative” is how portfolio programs stall.
Diligence
Judge the AI story before you underwrite it
Targets increasingly arrive with an AI narrative attached. The question is whether it is a product, a pilot, or a slide. A short technical read — what is actually deployed, what it depends on, who built it, and what happens if that person leaves — separates a defensible capability from a demo. This is the cheapest AI work in the whole hold period and the most frequently skipped.
First 100 days
Pick one workflow, not a program
The pattern that works post-close is narrow: one process with measurable hours or margin attached, one accountable owner, and a working system in production inside a quarter. The pattern that fails is a portfolio-wide AI mandate with no named workflow behind it. Sequence matters more than ambition — a single shipped workflow earns the credibility and the data access that the next four need.
Mid-hold
Turn one win into a repeatable standard
Once a company has a production system running, the leverage shifts from building to standardizing: the same security review, the same evaluation approach, the same vendor questions, the same definition of done. Companies at different maturity levels do not need the same roadmap, but they should be held to the same bar. That standard is what makes AI work portable across the portfolio instead of trapped in one company.
Pre-exit
Make the capability legible to a buyer
A buyer discounts what it cannot verify. Documented systems, owned data pipelines, named internal owners, and evidence of measured outcomes read very differently in a data room than a set of vendor subscriptions and an enthusiastic CTO. The work of making AI capability legible is best done well before the process starts, not during it.
What goes wrong
Five ways portfolio AI programs quietly fail.
None of these look like failure at the time. They look like progress, right up until the quarter where the number was supposed to move.
Buying a generalist for a specialist problem
The firm that built a marketing dashboard is not the firm that should build a retrieval system over regulated documents. AI delivery has genuinely distinct specialties — data infrastructure, retrieval, agent orchestration, evaluation, applied ML — and the pitch meeting flattens all of them into "we do AI." Matching by demonstrated use case is the single highest-return control an operating team has.
Mandating AI without naming the workflow
A portfolio-wide directive to "adopt AI" produces pilots that satisfy the directive and change nothing. Value comes from a named process, a named owner, and a number that is supposed to move. Everything else is activity.
Letting every company invent its own bar
Without a shared standard, each portfolio company negotiates its own data terms, its own security review, and its own definition of production-ready. The variance shows up later as risk the operating team did not know it was carrying.
Confusing a demo with a system
A convincing prototype and a durable production system look identical in a steering committee. The differences — evaluation, error handling, permissions, monitoring, cost per transaction under real load — only surface in month three, which is exactly when they are most expensive to fix.
Hiring a full team before the first result
Standing up an internal AI function before a single workflow has shipped commits payroll to a roadmap nobody has validated. Fractional leadership plus matched delivery capacity gets to the first result faster, and makes the eventual hiring decision an informed one.
What the operating team is actually buying
2+
Verified deployments
Every specialist has at least two production AI systems Neuronify can verify.
2–3
Curated matches
A short list selected for the scoped work — not a marketplace search result.
1
Accountable partner
Neuronify stays involved and fixes the match if an engagement is not working.
Start with the portfolio brief
Bring the AI priorities. Leave with the right operators.
Share the companies, use cases, budget bands, and timing. Neuronify will help scope the work and curate the specialists who have already shipped it.
Briefs and the initial scoping conversation are free.
Frequently asked questions
How does Neuronify work with private equity firms?+
Operating teams bring the portfolio picture — companies, use cases, budget bands, and timing — and Neuronify scopes the work and matches each need with two to three vetted specialists or boutique teams. Each portfolio company then contracts directly with the team it chooses, while the firm keeps one consistent standard for how introductions are made and how engagements are judged. The brief and the initial scoping conversation are free.
Can you support AI diligence on a target, not just portfolio companies?+
Yes. A technical read on a target’s AI claims is one of the most common first engagements: what is actually in production, what it depends on, who built it, how much of it is vendor-supplied, and what the realistic cost of maintaining or replacing it looks like. It is a short, focused piece of work, and it is considerably cheaper than underwriting a capability that turns out to be a pilot.
What does a typical engagement cost?+
The network is a fit for projects starting around $25k. Portfolio diagnostics and diligence reads sit at the lower end; a first production workflow typically runs into the mid five figures to low six figures depending on data complexity and integration surface; fractional AI leadership is usually a monthly retainer. Neuronify scopes the band before making introductions so budget conversations happen before anyone’s time is spent.
How are specialists vetted?+
Every specialist in the network has at least two production AI deployments Neuronify can verify, plus reference checks and a defined specialty rather than a general "we do AI" claim. Verification comes first and introductions come second — the point of the network is that the operating team does not have to run technical vetting itself for every portfolio company.
Do you work with one portfolio company or across the whole fund?+
Both. Some firms start with a single urgent build at one company and expand once the model proves out; others use the network as a standing specialist bench across the fund from the beginning. The operating model is the same either way — the difference is whether the standard is applied once or repeatedly.
How fast can a portfolio company start?+
Scoping takes about fifteen minutes of an operator’s time, and curated introductions typically follow in days rather than weeks. Actual start dates depend on the specialists’ availability and the company’s data access, but the matching step is deliberately not the bottleneck.
What if the match does not work out?+
Neuronify stays involved through delivery rather than disappearing after the introduction, and fixes the match if an engagement is not working. Accountability after the handoff is the part most staffing firms and marketplaces do not offer, and it is the part that matters most when the work is technical enough that the operating team cannot easily judge it alone.