Gravity: AI-Native MBSE + GRC
for Deep-Tech Founders

Provenance

The Analysis Behind These Findings

The findings above are not survey results — they are operating lessons. Signet Solis runs the Legacy Minerals Initiative: a 150-technology development program spanning mining, defense, and space, carried by a founding-scale team under four concurrent compliance frameworks. Gravity exists because we had to run that program before any tool existed to run it with. Every argument in this paper was paid for in program time.

We began where every deep-tech founder begins: evaluating the incumbent stacks. The MBSE suites assumed staffing we did not have — dedicated modelers, SysML fluency, tool administrators — and their outputs stopped at the engineering boundary. The GRC platforms understood cloud-service audit logs but had no concept of an interface control document, a verification cross-reference, or a configuration baseline. Nothing connected the two. The program's early state was exactly the sprawl Section 2 describes: a manifest, spreadsheets, documents, and a growing reconciliation tax paid by the same people doing the engineering.

Gravity's architecture is the distillate of specific decisions made under that pressure. Graph over documents: the program's technology manifest became a Life Cycle Modeling Language instantiation, because a plain-language entity model was the only form the whole team could maintain without specialist staffing. One model, tiered views: serving engineers, auditors, and investors from the same graph — rather than maintaining parallel artifacts — produced the PRISM disclosure framework. Compliance as extraction: the traceability we were already maintaining for engineering reasons turned out to be the audit evidence the frameworks wanted, so the GRC layer became views over the model rather than a second system. Each of these is now a platform feature; each began as a survival decision inside a live program.

The proof discipline runs the same direction. Gravity's first production tenant is the program's own flagship vehicle-platform effort, which carries its requirements, interfaces, and milestone-review packages in the platform. Gravity's own governance runs through Gravity — ISO/IEC 42001 management-system scoping, an active AI risk register, decision-trace capture on external-facing AI output. We hold the platform to the discipline it sells, and we publish posture data rather than adjectives.

These lessons did not stop at our own program. Signet also performs contracted systems-engineering work on safety-critical aerospace systems — requirements, traceability, and verification artifacts developed under formal range-safety and system-safety standards, inside a customer-prescribed toolchain. That engagement independently reproduced every failure pattern this paper describes. Three observations from it are worth stating plainly:

• A capable team with heavy automation still pays the reconciliation tax — it simply pays in script maintenance instead of clerical labor. At one point the same editorial-compliance rule had to be implemented at three separate layers of the document pipeline, because no single point of control existed.
• A model-based tool that cannot be read or written programmatically degenerates into another document silo. When API access is unavailable, exports become the working truth, and every engineering decision must be hand-carried back into the tool in hours-long bulk-import sessions. "Model-based" is not sufficient; the model must be the live, programmable substrate of daily work.
• Structure beats text. An attempt to confirm traceability links by text-similarity scoring matched 4% of candidates — because real-world requirement and test text is templated boilerplate. Verifying the same links as graph-edge intersections matched 100%. Traceability is a structural property; tools that treat it as a text problem will miss it.

Those observations are why this paper's claims are stated with confidence: we have watched the same failure modes appear in a program we run and a program we were hired into — under different tools, different standards, and different scale.

Where each finding is argued:

• Finding 01 (compliance is an engineering problem) — Section 1 quantifies the burden; Section 3 shows evidence originating in engineering activity
• Finding 02 (existing GRC tools solve the wrong layer) — Section 4, the Drata and ServiceNow analyses
• Finding 03 (AI-native workflows collapse evidence latency) — Section 3, the extraction workflows
• Finding 04 (the LMI program demonstrates this at 150-node scale) — Section 5, the proof case and posture data
• Finding 05 (graph-native MBSE removes the reconciliation tax) — Section 2, the workbench and the wedge

Section 1

The Compliance Burden for Deep-Tech Founders

Defense-adjacent and space-sector ventures face a compliance stack that is qualitatively different from commercial software startups. ITAR and EAR govern which technologies can be developed, who can participate in development, and which foreign nationals may access technical documentation. NIST SP 800-53 mandates information security controls at a depth appropriate for systems that may ultimately interface with government networks. CMMI-DEV Level 3 requires institutionalized engineering processes — documented, trained, measured, and enforced — before a program is considered mature enough for government prime contractor trust. AS9100D extends ISO 9001 quality requirements into aerospace-specific dimensions: configuration management, nonconformance tracking, first-article inspection.

Each of these frameworks operates on a different cadence, uses different vocabulary, and is audited by different bodies. ITAR compliance is continuous and self-enforcing. CMMI appraisals are periodic and conducted by lead appraisers. AS9100D certification requires a certified registrar. NIST assessments may be driven by a government customer's authorization schedule. A founding team has no reasonable path to satisfying all four simultaneously using manual processes alone — the documentation overhead would consume engineering capacity that must remain on the technical stack.

The scale of the documentation surface is concrete. NIST SP 800-53 Rev 5 defines a catalog of more than 1,000 controls and control enhancements across 20 families; a moderate-impact baseline alone selects 287 controls, each requiring an implementation statement and ongoing assessment evidence. CMMI-DEV appraisal at Level 3 examines twenty-plus process areas, each demanding documented practices, training records, and objective evidence of institutionalization. AS9100D layers aerospace-specific obligations — configuration management, first-article inspection, nonconformance control — onto the full ISO 9001:2015 clause structure. ITAR compliance requires registration and a technology control plan governing every technical artifact the team produces. Conventional practice treats each of these frameworks as a fractional-to-full dedicated role; carried concurrently, the four amount to a compliance department assembled before first product revenue. That is the structural drag Gravity is built to remove.

Section 2

Gravity as an MBSE Platform

Before Gravity is a compliance tool, it is a working systems engineering environment. The standard founding-stage configuration splits engineering truth across four or more tools: requirements in a requirements manager or a spreadsheet, architecture in a diagramming or SysML tool, analyses and budgets in spreadsheets, and decisions, test reports, and interface documents in a shared drive. None of these tools agree with each other by construction; engineers reconcile them by hand. On a small team that reconciliation tax is paid by the same people designing the system — and every milestone review restarts it.

Gravity replaces that sprawl with a single graph-native model built on the Life Cycle Modeling Language (LML) 2.0, an open standard whose plain-language entity model — requirements, assets, interfaces, actions, risks, decisions, verification activities — is designed to be read and maintained by the whole program team, not only dedicated modeling specialists. Requirements trace to the architecture elements that satisfy them and the verification activities that prove them. Interface control is first-class: interfaces are entities with owners, states, and change history, not documents to be diffed. Configuration baselines are snapshots of graph state, so "what did the design look like at review" is a query, not an archaeology project.

Every project begins with a Foundation — a seven-phase ordered readiness model: Concept of Operations, Requirements Baseline, Architecture & Decomposition, Risk & Hazard Baseline, Trade Studies, Verification & Validation, Milestones & Gate Plan. The Foundation engine tracks completion per phase, scores overall maturity, surfaces the single next step, and routes it to the discipline-appropriate agent. A new project knows exactly where it is and what to do next before any artifact is authored.

AI participation is native to the workbench and grounded in each project's specific context. Agents are injected with the project's domain, sector, applicable standards, current foundation maturity, and CONOPS summary — so the SE Orchestrator on a copper flotation project reasons about mineral processing, and on a lunar vehicle-platform it reasons about space systems engineering. Agents draft requirement candidates from source material, flag traceability gaps and orphaned verification activities, check interface definitions for inconsistency, and assemble milestone-review packages directly from graph state. The Foundation panel ties this together: each phase card surfaces the relevant agent and a pre-seeded prompt matched to that phase's authoring task, so projects advance from skeleton to high-maturity through agent-assisted authoring that the engineer accepts and edits. The engineer remains the authority — every AI contribution lands in the model as a traceable, reviewable change.

This is how the LMI program actually runs. The 150-node technology stack is modeled in Gravity, and the program's flagship vehicle-platform effort manages its requirements, interfaces, and supplier evidence in the platform — assembling its milestone-review packages, from system requirements review through preliminary design review, directly from the model. The compliance integration described in the next section builds on this engineering substrate; without a live model, there is nothing to extract evidence from.

Section 3

Gravity's Approach: MBSE as the GRC Substrate

Model-based systems engineering organizes system design around a formal model — a structured, machine-readable representation of system requirements, architecture, behavior, and verification relationships. Every design decision is represented in the model and every model element can be traced to a requirement, a test, or a decision record. This traceability is, incidentally, exactly what compliance frameworks require: documented requirements, evidence of verification, change records, configuration baselines.

Gravity's core integration is to treat the systems engineering model as the primary compliance evidence source. Rather than generating compliance artifacts separately — a CMMI-required process asset library, an AS9100D quality plan, a NIST control implementation statement — Gravity extracts these from the engineering model using AI-native workflows. When an engineer updates an interface control document in the model, Gravity identifies which NIST controls are affected, which AS9100D clauses require notification, and whether the change creates an ITAR-relevant modification to the technical scope.

In the current platform release (v0.8), this works as follows. Gravity's engineering model is graph-native and built on the Life Cycle Modeling Language (LML) 2.0 open standard, so requirements, architecture elements, interfaces, decisions, risks, and verification activities exist as connected entities rather than as documents that must be reconciled after the fact. The platform's first packaged workflow, the Supplier Evidence Package, assembles audit-ready supplier and component evidence directly from the model and is in production use today. Projects are now durably registered — the tenant registry persists across deployments, so engineering state is not ephemeral. Document ingestion allows external artifacts (specifications, interface documents, third-party analyses) to be uploaded, tagged by PRISM tier, and referenced from within the project graph. Access control follows the same architecture throughout: every tenant view is generated through PRISM, Signet's tier × sector × role disclosure framework, so the same engineering graph serves an internal engineer, an auditor, and an investor at different disclosure depths without duplicated artifacts. And because Gravity's own AI participates in evidence handling, the platform records AI decision traces to immutable storage under a seven-year retention commitment — the platform is governed by the same discipline it provides.

Section 4

Differentiation: Why Existing Tools Fall Short

Traditional MBSE Suites

Dedicated MBSE and requirements platforms — Cameo Systems Modeler, IBM DOORS and Rhapsody, Innoslate, Jama — are the incumbents on the engineering side of this problem. They are capable tools, calibrated for organizations with dedicated modeling staff: SysML fluency, per-seat enterprise licensing, and tool-specialist roles are assumed. They also stop at the engineering boundary, leaving governance evidence generation to a separate tool layer — so a program that adopts them still carries the GRC reconciliation burden this paper opened with. And when these tools are deployed without programmatic access — a common enterprise configuration — the model itself becomes one more silo: exports become the working truth, and engineers hand-carry every decision back into the tool. Gravity's positioning is the inverse: a plain-language LML 2.0 model the full founding team can work in directly, with the compliance layer extracted from the same graph the engineers already maintain.

Palantir

Palantir's AIP and Foundry platforms are enterprise data integration and analytics tools used by large defense primes and intelligence agencies. They solve a different problem: integrating heterogeneous data sources at scale for decision-support applications. The minimum viable engagement scale and the required integration infrastructure are calibrated for organizations with operational data systems already in place. A founding-stage deep-tech venture does not have the data estate, the integration team, or the budget to engage Palantir's platform as a compliance substrate. Palantir is not competing with Gravity at the founding stage; it is a potential future integration target when scale justifies it.

Drata

Drata is a cloud-native compliance automation platform that excels at SOC 2, ISO 27001, and HIPAA compliance for software companies. It integrates with common SaaS toolchains (GitHub, AWS, Okta) to collect compliance evidence automatically. The product is well-designed for its target market. The gap for deep-tech founders is that CMMI-DEV and AS9100D are not in Drata's framework library — and could not be supported by the same SaaS-integration approach, because the evidence for these frameworks lives in engineering artifacts (model files, test reports, nonconformance records) rather than cloud service audit logs.

ServiceNow

ServiceNow's IRM (Integrated Risk Management) module is an enterprise-grade GRC tool capable of supporting NIST frameworks and custom control libraries. The barrier is implementation complexity and cost: ServiceNow implementations require specialist consultants, months of configuration, and ongoing platform administration. For a founding-stage venture, the implementation cost and operational overhead of a ServiceNow GRC deployment would exceed the cost of a dedicated compliance officer — which defeats the purpose of automation at this stage.

Gravity entries reflect platform v0.8 capabilities in production use on the Legacy Minerals Initiative program. A checkmark indicates deployed platform capability at founding-team scale — not third-party certification or appraisal status of any customer program. Competitor entries reflect each product's documented framework coverage and target deployment scale as of Q2 2026.

Section 5

Proof Case: The Legacy Minerals Initiative

The Legacy Minerals Initiative is a 150-technology stack organized across eight development phases, spanning mining extraction, defense autonomy, and space systems domains. The program operates under four concurrent compliance frameworks: NIST SP 800-53 (information security), CMMI-DEV Level 3 (systems engineering maturity), AS9100D (aerospace quality management), and ITAR/EAR (export control). At founding-team scale, managing four frameworks against a 150-node technology development roadmap is a systems problem, not a documentation problem.

Gravity is deployed as the compliance substrate for the LMI program. The 150-node architecture is represented in the Gravity engineering model. Control mappings for NIST SP 800-53 are maintained against the model's information security boundaries. ITAR technical scope determinations are attached to individual nodes at their category classification (A: Signet-owned; B: Licensed; C: Open standard; D: To-develop). As nodes progress through development phases, Gravity identifies which framework obligations activate and generates evidence-collection tasks for the engineering team.

Compliance posture across the four frameworks, at the Q1 2026 baseline assessment:

• NIST SP 800-53: 62% implementation — control baseline fully mapped; continuous monitoring architecture in place
• CMMI-DEV Level 3: 45% implementation — process areas defined; institutionalization underway
• AS9100D: 38% implementation — quality management system structure established; internal audit cycle initiated
• ITAR/EAR: 88% implementation — technology control plan active; DTSA trade secret protections enforced

Detailed compliance posture data, open finding counts, and framework progress timelines are available to DT:2 portal users in the Governance dashboard. DT:3 due-diligence access under executed NDA provides full control implementation statements and audit-ready evidence packages.

What the program demonstrates is structural, and we state it carefully: the compliance posture above is carried without a dedicated compliance staff. Framework obligations are tracked against the same model the engineering team already maintains, so evidence accumulates as engineering work lands rather than in audit-driven reconstruction sprints. The program's first production tenant — a flagship mining-platform development effort — runs its requirements, interface management, and supplier evidence through Gravity as the system of record. Signet's own AI governance runs through the platform as well: ISO/IEC 42001 management-system scoping is complete, an AI risk register is active, and external-facing AI decisions are captured under a six-step decision-trace architecture with immutable retention. Quantified before/after productivity benchmarking is a deliverable of the commercial alpha program now being stood up; this paper deliberately reports only what the program demonstrates today.

Conclusion

MBSE + GRC Integration as Structural Advantage

The compliance frameworks that govern defense, space, and resource development ventures are not optional — they are the entry ticket to government contracts, prime contractor partnerships, and regulated export activity. For a founding team, the question is not whether to satisfy these frameworks but how to do so without consuming the engineering capacity that the technical stack demands.

Gravity's answer is architectural: build compliance artifact generation into the systems engineering workflow so that the evidence is a byproduct of engineering activity, not a separate work stream. The LMI program demonstrates this is feasible at a 150-node scale across four frameworks simultaneously.

Qualified investors seeking detailed technical documentation, compliance posture data, and full program architecture access should request DT:2 or DT:3 portal access through the investor relations channel.