Atomic Build/PE/VC

One playbook. Multiple holdings. No rebuild.

Atomic Build embeds a forward-deployed team inside your portfolio to build a repeatable AI modernization framework. Deploy standardized workflows, governance and product features across disparate companies while each maintains independent innovation velocity.

Atomic Build·Programmatic SEO

You're running parallel AI initiatives across unconnected companies.

Each portfolio company is solving the same AI problems independently. Duplicate engineering effort. Fragmented tooling. No shared governance. No cost leverage. By the time one holding ships a workflow, another is starting from scratch.

Replicated engineering across holdings
Multiple portfolio companies hire AI teams to solve the same problems independently. Same spend, same timeline, no learning transfer.
No shared AI infrastructure
Each holding builds on different LLM providers, vector databases, and evaluation frameworks. You're paying for redundancy instead of compounding.
Governance sprawl
Each company defines its own data access, model validation, and operational controls. No portfolio-level risk management or cost visibility.
Slow time-to-value
Without a repeatable playbook, each holding takes 12+ months to get from 'AI strategy' to a production workflow.
No cost lever across the portfolio
You're negotiating LLM and infrastructure contracts company-by-company instead of leveraging portfolio scale.
Innovation gets bottlenecked
Companies that move faster than the playbook get blocked. Companies that lag can't catch up. The portfolio stalls.

A repeatable AI modernization playbook compounds faster than isolated innovation.

We don't believe in writing strategy decks and handing them to portfolio companies to execute. Instead, we embed with your portfolio office and the fastest-moving holding to build a concrete, proven playbook—then help other companies adopt and adapt it. The result is standardized governance and infrastructure with independent innovation velocity.

Start with one holding, compound across the portfolio
We pick the holding with the clearest AI opportunity and ship a production system in weeks. That playbook becomes the template: same data architecture, same governance model, same evaluation framework. Other holdings don't start from zero.
Shared infrastructure, independent products
Build once for orchestration, monitoring, and compliance. Let each holding innovate on top. Think of it like shared cloud infrastructure with company-specific applications.
Governance without friction
Portfolio-level controls (data access, model validation, cost limits) are baked into the playbook so compliance doesn't slow individual companies.
Measure and adapt
Each deployment feeds back into the playbook. The second holding is faster than the first. The third faster than the second. Playbooks improve with scale, not complexity.
Retain operational autonomy
The playbook is a framework, not a cage. Each company keeps its own roadmap, its own risk appetite, its own product decisions. The playbook just removes the friction.

Build the playbook with one holding, scale to many

We start with your fastest-moving company or clearest opportunity. Ship a production AI system in 6 weeks. Document the playbook. Then help other holdings adopt, adapt, and compound.

Portfolio opportunity audit · Week 1
We interview leadership and teams across 3–4 portfolio companies to map AI opportunities, identify the best first holding, and score where AI creates portfolio-wide value.
Playbook design · Weeks 2–3
Working with your portfolio office and the first holding, we design the AI infrastructure, data architecture, governance model, and evaluation framework that will scale to other companies.
First holding deployment · Weeks 4–8
Our forward-deployed team ships the first production AI workflow into the chosen holding. This deployment becomes the reference implementation for all others.
Adopt and compound across portfolio · Weeks 9+
We work with additional portfolio companies to adopt the playbook, adapt it to their specifics, and ship. Each deployment gets faster. The playbook improves with each iteration.

Relevant services

Most engagements combine three or four of these. Start with what hurts most.

OperationsFinance & OpsProduct & EngineeringSales & GTMHR & OpsFinance & StrategyStart with one holding, compound across the portfolioShared infrastructure, independent productsGovernance without frictionMeasure and adaptRetain operational autonomy

Where AI modernization compounds across holdings

These are the workflows we see get adopted fastest across PE/VC portfolios. The first holding ships in 6 weeks. The second in 3. The third in 2.

  • Customer support triage

    AI agent classifies incoming tickets by urgency, intent, and resolution path. Escalates only edge cases. Works across SaaS holdings with different ticketing systems.

    −45% avg support labor, 1.2x CSAT

  • Invoice and expense audit

    Automated line-item audit across accounts payable. Flags overage, duplicate, and contract mismatches. Scales to multiple holdings with shared audit rules.

    −3.2% AP spend, 99.8% audit coverage

  • Code review and documentation copilot

    AI assistant reviews pull requests, suggests tests, and auto-generates API docs. Shared across engineering teams with company-specific style guides.

    −28% review cycle time, +67% docs coverage

  • Sales collateral generation

    AI system generates pitch decks, case study content, and product comparison docs from a shared knowledge base. Each holding curates its own narrative.

    +40% sales content velocity, −60% time-to-collateral

When to talk to us

Some patterns we hear on the first call. If two or more of these are true, the conversation is worth having.

  • Multiple portfolio companies hire AI teams to solve the same problems independently. Same spend, same timeline, no learning transfer.
  • Each holding builds on different LLM providers, vector databases, and evaluation frameworks. You're paying for redundancy instead of compounding.
  • Each company defines its own data access, model validation, and operational controls. No portfolio-level risk management or cost visibility.
  • Without a repeatable playbook, each holding takes 12+ months to get from 'AI strategy' to a production workflow.
  • You're negotiating LLM and infrastructure contracts company-by-company instead of leveraging portfolio scale.
  • Companies that move faster than the playbook get blocked. Companies that lag can't catch up. The portfolio stalls.

Let's Connect

Decide what is worth building first.

We start with your fastest-moving company or clearest opportunity. Ship a production AI system in 6 weeks. Document the playbook. Then help other holdings adopt, adapt, and compound.

Let's Connect →See the engagement model

Portfolio leaders ask these before committing

How do we identify high-value AI automation opportunities across the portfolio?
We run a Portfolio Opportunity Audit in week 1: interviews with ops, product, and finance leads across 3–4 holdings to map workflows, quantify pain, and score where AI moves EBITDA. We prioritize by deployment effort and portfolio leverage—looking for workflows that repeat across multiple companies or unlock scale in one holding that other companies can adopt.
Does the playbook lock us into specific technology choices?
No. The playbook defines the framework (data architecture, governance model, evaluation standards), not specific vendors. We typically recommend LLM providers and infrastructure based on your risk tolerance and cost targets, but you stay flexible. If a holding wants to experiment with a different approach, they can—as long as it meets the governance baseline.
How do we ship an AI product across multiple holdings without hiring separate teams at each company?
The playbook is the force multiplier. The first holding ships with our forward-deployed team. We document every decision, integration point, and operational control. The second holding doesn't need to rehire or rediscover—they inherit the playbook and adapt it. By holding 3–4, other companies have internal capacity to lead their own deployments with our team providing oversight.
How do we modernize internal workflows with AI without disrupting day-to-day operations?
Every workflow in the playbook includes a human-in-the-loop design. The AI handles triage, draft decisions, and low-risk tasks. Edge cases and high-stakes decisions route back to your team. You gain leverage without losing control. We also stage deployments on historical data and shadow runs before going live.
What is an Opportunity Sprint and how does it fit into our engagement?
The Opportunity Sprint is week 1 of our engagement: we embed with your portfolio office and 3–4 portfolio companies for intensive interviews, workflow mapping, and opportunity scoring. We quantify the cost of each pain point and recommend the best first deployment target. The output is a prioritized, scored backlog with dollar impact—so you're never guessing where to start.
How do we manage costs and avoid vendor lock-in across holdings?
The playbook defines a shared cost model: shared LLM contracts, shared infrastructure (vector DB, monitoring), and company-specific applications on top. You negotiate once for the portfolio and allocate usage-based costs to each holding. You also retain flexibility to swap vendors—the framework isn't dependent on proprietary tooling.