Atomic Build/Government contracting

Ship internal AI across your portfolio without compliance sprawl

Atomic Build embeds a product and engineering team inside your portfolio to build secure internal AI systems with NRC IRAP controls baked in. One compliance framework. Multiple portfolio companies. Zero overhead per holding.

Atomic Build·Programmatic SEO

Portfolio companies are building AI in isolation, duplicating compliance work and risking certification gaps.

Each holding invests in its own internal tooling, its own compliance review, its own data governance. NRC IRAP requirements live in spreadsheets. Data residency rules get enforced inconsistently. And when audit season hits, nobody has a clear answer about what's running where.

Duplicate compliance overhead per holding
Each portfolio company re-invents controls for data handling, model governance, and audit trails instead of sharing a vetted framework.
NRC IRAP ambiguity on AI systems
Requirements were written for traditional software, not LLMs and agents. Portfolio companies disagree on what actually complies.
Data residency and segregation friction
Shared AI infrastructure looks risky if you can't prove data from Company A never touches Company B's models.
No portfolio-wide AI playbook
Each holding experiments separately. No best practices flow. No economies of scale on tooling.
Audit readiness is reactive
Controls and observability get added after the fact when a compliance officer asks 'Where is the audit trail for that model?'
Hiring constraints limit internal build velocity
Portfolio companies can't staff internal AI teams fast enough to move on internal tools. Investment gets stuck.

Portfolio companies don't need 10 separate AI stacks. They need one compliant substrate and a playbook that works across holdings.

We build a shared internal AI infrastructure for your portfolio with NRC IRAP controls embedded from day one — not bolted on later. Each holding gets data isolation and compliance logging. You get a repeatable playbook. And your audit story becomes 'here's how every holding uses it' instead of 'we're still figuring it out.'

Compliance by architecture, not by process
NRC IRAP requirements get built into the system design: data residency, audit trails, model versioning, access control. Not a separate compliance layer.
Data segregation with shared infrastructure
One AI backbone across the portfolio. Each holding's data stays isolated. Compliance enforcement is automatic, not manual.
Forward-deployed governance team
We embed with your portfolio GRC and tech leads to translate NRC IRAP into AI-specific controls that actually work in production.
Portfolio-wide playbook, per-holding deployment
We build once, document thoroughly, and help each holding deploy the same system against their own use cases.
Observability from day one
Every model, every prompt, every inference gets logged. When an auditor asks 'what happened here,' you have a clear answer.
Scale compliance without scaling headcount
First holding takes 8 weeks to production. Second holding ships in 3 weeks using the same substrate. Cost per holding drops 60%.

From charter to compliant AI in production

We start with a single portfolio company to prove the model. Then we scale the playbook across your other holdings. NRC IRAP controls and data segregation stay consistent.

Portfolio compliance deep dive · Weeks 1–2
We map your NRC IRAP obligations as they apply to AI systems, understand your data residency rules, and document the compliance requirements that'll shape every build in the portfolio.
Infrastructure and governance design · Weeks 3–4
We design the shared AI backbone: authentication, audit logging, model versioning, data isolation, and observability. This substrate works for every holding.
First holding deployment · Weeks 5–8
We ship the first internal AI system into production with one portfolio company, proving the substrate works and compliance flows are real.
Portfolio rollout and scaling · Weeks 9+
We help your other holdings deploy the same substrate against their own use cases. Each new holding moves faster because the compliance and infrastructure work is done. You can build a portfolio-wide playbook.

Relevant services

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

OperationsCompliance & GRCFinance & ProcurementHR & OnboardingRisk & AuditSupply chainCompliance by architecture, not by processData segregation with shared infrastructureForward-deployed governance teamPortfolio-wide playbook, per-holding deploymentObservability from day oneScale compliance without scaling headcount

What portfolio companies are building with compliant AI infrastructure

These are the internal workflows we see deliver fastest ROI when shared across a portfolio. Each holding deploys against its own business, compliance rules stay consistent.

  • Internal process copilot

    Employees access a single interface for common tasks: contract review, procurement request drafting, compliance checklist generation. Access control ensures data from different holdings never leaks.

    −45% time on routine tasks

  • Policy adherence assistant

    Internal teams get real-time guidance on NRC IRAP compliance as they work: 'This document needs a data residency label,' 'This workflow requires audit logging.' Guidance is consistent across the portfolio.

    95% first-pass compliance

  • Contract intelligence system

    Finance teams upload contracts. AI extracts terms, flags unusual clauses, surfaces compliance obligations. Each holding sees only its own contracts, but all use the same model.

    −2.5 weeks per contract review

  • Internal knowledge assistant

    New hires and employees query a private AI trained on portfolio policies, procedures, and compliance rules. Data stays in each holding's silo. One interface, portfolio-wide.

    −3 weeks time-to-productivity

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.

  • Each portfolio company re-invents controls for data handling, model governance, and audit trails instead of sharing a vetted framework.
  • Requirements were written for traditional software, not LLMs and agents. Portfolio companies disagree on what actually complies.
  • Shared AI infrastructure looks risky if you can't prove data from Company A never touches Company B's models.
  • Each holding experiments separately. No best practices flow. No economies of scale on tooling.
  • Controls and observability get added after the fact when a compliance officer asks 'Where is the audit trail for that model?'
  • Portfolio companies can't staff internal AI teams fast enough to move on internal tools. Investment gets stuck.

Let's Connect

Decide what is worth building first.

We start with a single portfolio company to prove the model. Then we scale the playbook across your other holdings. NRC IRAP controls and data segregation stay consistent.

Let's Connect →See internal AI systems we've built

Questions portfolio and compliance leaders ask

How do we actually enforce data segregation if we're sharing AI infrastructure?
Data isolation is enforced at the system level, not by policy. Each holding's data is encrypted separately and lives in isolated storage. Models are either single-tenant or trained on pooled, anonymized data. When an auditor asks 'Can Company A access Company B's data?' the answer is 'The system doesn't allow it' — not 'Our process prevents it.'
Does this shared approach satisfy NRC IRAP for AI systems?
NRC IRAP doesn't specifically address LLMs and AI agents yet, but the control framework translates directly: data residency, access logging, change management, incident response. We map your obligations into technical controls and build them into the infrastructure. Your audit story becomes 'here's how every holding uses this compliant system,' which is stronger than 'each holding manages their own controls.'
What if one holding needs different compliance rules than another?
The substrate is flexible. Core controls (audit logging, data segregation, access management) are non-negotiable across the portfolio. But configuration — which models each holding can access, specific data residency zones, audit thresholds — is customizable per holding. Think of it like a multi-tenant SaaS with portfolio-wide guardrails.
How long does it take to get the first holding into production?
The infrastructure and compliance design runs 4 weeks. The first production system ships by week 8. Once that's live, every subsequent holding moves much faster — typically 3–4 weeks — because the hard compliance and infrastructure work is complete.
Can we start with one holding and expand to others later?
Yes. We recommend starting with one holding to validate the model and compliance approach. Once you're live and passing internal audit, rolling out to other holdings becomes a configuration and user-training exercise, not a new compliance build.
What happens to our portfolio's AI systems if we eventually sell a holding?
Data and models for the departing holding are cleanly isolated from day one. Transition is straightforward: export the holding's data, hand off the runbook, they keep using the same system or migrate to their own. No entanglement with the rest of your portfolio.
How does observability work for audit and compliance reporting?
Every inference, every model update, every data access is logged and queryable. You get a unified compliance dashboard where you can see, by holding, what models are running, who's using them, what they're processing, and whether any anomalies have triggered. Auditors can pull reports directly instead of asking five different teams.
Do we need to hire internal AI expertise to manage this?
You'll need someone to understand the system architecture and oversee deployment across holdings — but that's one person, not a full AI team per company. We document heavily and can stay on as a retainer to help each holding customize and troubleshoot.