FAQ for real searches.

V1 is the business you run today. V2 is that business rebuilt AI-native alongside it. You keep V1 running while Faction builds V2 module by module, then you move over when it is proven.

AIOS Discovery is the V2 readiness path: a V2 Readiness Checklist plus working sessions (about a week). It scopes a platform build. It is distinct from the $200 Production Discovery, which audits an existing app across 13 layers.

Production Discovery is the $200 app audit (13 layers). AIOS Discovery is for businesses: readiness checklist and sessions that scope a V2 / AIOS platform build. The 13-layer language applies to apps, not to the business itself.

AI-Directed Engineering (AIDE): audit in, modular finishing on the Conveyor Belt, audit out. Senior engineers direct AI through fixed modules. See Methodology and the Conveyor Belt.

AI-directed product engineering means engineers work with AI tools across architecture, implementation, testing, and delivery, while humans own tradeoffs, reviews, and acceptance. The goal is faster cycles without lowering production standards. Teams like Faction organize work in fixed modules so progress is visible (often with demo checkpoints) rather than open-ended.

A modular MVP scopes work as end-to-end slices (commonly UI + API + data) instead of a sprawling backlog. You ship in stages, approve working software regularly, and reduce the risk of burning budget on the wrong features. If you want a structured build with predictable pricing, labs that specialize in modular delivery are worth a conversation.

It means core platform basics, auth, baseline admin, logging patterns, encryption posture, and related foundations, are treated as included platform work rather than billed as feature line items on every project. Exact inclusions vary by product, but the intent is to concentrate spend on differentiated product work.

Yes, that is a common entry point. Many teams get to a working demo fast, then stall on production concerns: security hardening, deployments, reliability, and maintainability. Offerings such as vibe-code finishing are designed to bridge that gap rather than rewriting from scratch.

A Prototype is one automation module delivered in a single 40-hour sprint using Faction's R.A.P. method (Rapid Automation Prototyping). You bring the use case; we deliver a production-ready machine with demos and a fixed quote before you commit.

Shadow AI is employee use of AI tools outside an approved governance program, often browser tools that bypass legacy controls. Regulated firms care because undocumented usage creates gaps in supervisory records, vendor management, privacy practices, and training evidence. Platforms such as Olakai focus on detection and reporting; deployment partners typically handle installation and onboarding.

It can be, after normal engineering controls: dependency review, secret hygiene, threat modeling proportionate to risk, QA, observability, and deployment discipline. Teams that skip those steps blame the model; teams that enforce them ship safely.

A demo proves a concept for a narrow path; production software handles failure modes, authentication edges, data durability, operational monitoring, upgrade paths, and maintainability. Confusing the two is the most expensive mistake in fast AI builds.

It is Faction’s $200, 24-hour, 13-layer production engineering scan of an AI-built or vibe-coded application. The deliverable is a branded PDF with severity-rated findings, a scorecard, a modular build plan, and a Statement of Work ready for signature, not a DIY fix-it guide. The fix-it audit product stays on mattmurphy.ai. Start Production Discovery.

The Conveyor Belt is Faction’s managed finishing pipeline for teams that ship continuously. Builds enter through Production Discovery, move through modular finishing stages, and exit with a verification discovery so the findings stay the build plan. See the Conveyor Belt.

AIOS is Faction’s greenfield AI operating platform build: domain services, AI agents, evaluation loops, and guardrails, delivered in $8K modules and typically scoped through Production Discovery. Typical programs land in the 25–50 module range. See AIOS.

Hosted AI Infrastructure (formerly known as the Matt Stack) is AI infrastructure you own: open-weight models on hardware you control. Faction scopes and can manage the stack; you pay your VPS provider directly for hardware. Managed engagement is quoted fixed at scoping. See Hosted AI Infrastructure.

Timelines depend on scope, integrations, and review cadence, but modular delivery is intentionally designed to shorten cycle time versus traditional waterfall engagements. Expect scoping honesty first: unclear requirements and missing data slow every team, AI-assisted or not.

Rapid MVPs are built in fixed 40-hour modules (typically three to five). Module count, timeline, and a fixed quote come from discovery before any build work begins. The only published Faction price on the site is the $200 Production Discovery.

A fixed unit of professional time aimed at one coherent product slice: planning, implementation, review checkpoints (often three demos), and delivery artifacts for that slice. Scope expansion means another module or a revision, not unlimited work inside the same unit.

Often yes, especially when you want acceleration on a constrained timeline, architectural leadership for a risky area, or a partner that can integrate with your toolchain. Alignment matters: modular partners work best when your team can approve work quickly and owns product decisions.

Assessments focus on readiness, prioritization, and risk before major build spend, useful when stakeholders disagree on where AI should land or when governance is unclear. Build engagements produce production software once the organization is positioned to absorb it.

The format is designed to generate prioritized ideas quickly with facilitation, cross-functional participation, and an executive-friendly readout. It is not a substitute for full product delivery, but it can align leadership and create a believable shortlist for pilots.

It targets the gap between “works on my machine” and production: security review themes, deployment patterns, monitoring hooks, hardening, and polish appropriate to the product’s risk profile. The point is to finish what AI-assisted prototyping started, without pretending security and operations are optional.

Choose platform work when you need multi-tenant architecture, serious compliance boundaries, performance budgets, or long-lived systems that will evolve for years. MVPs validate; platforms scale. If you are unsure, a short discovery pass should clarify which track reduces regret.

Faction positions as a deployment and implementation partner for Olakai in wealth-management contexts, focused on getting monitoring in place and producing operational outcomes, not just selling licenses. Technical platform questions often route to Olakai; delivery planning routes through the implementation partner.

No. Faction functions as a specialized build and deployment partner, not executive leadership substitution. Governance, approvals, budgets, and vendor decisions remain client-owned.

Teams routinely protect sensitive materials. Bring your standard mutual NDA; if you don’t have one, ask during scheduling what format is acceptable. Don’t upload regulated data to uncontrolled channels.

Generally yes, cloud-native deployment is common. What matters is access, networking constraints, identity systems, and environment parity between staging and production.

With explicit module boundaries and change paths. If new work does not fit the current module, it is scoped as the next slice, this protects both sides from silent budget blowouts.

Most client value is delivered with integrated systems, workflow automation, and product engineering, not bespoke foundation-model training. If fine-tuning is truly required, it should be justified by data rights, evaluation metrics, and ongoing maintenance costs.

By agreed acceptance criteria: performance targets, reliability checks, security review outcomes, and user-visible outcomes tied to the business case, not “lines of code” or story points for their own sake.

Yes. Critical findings become priority modules; warnings become the next tier. The modular scope and Statement of Work are the work order for finishing via Vibe Code Finisher, Conveyor Belt entry, or an AIOS rebuild when the architecture itself is the problem.

Complete the intake on the Production Discovery page (embedded Tally form), then pay $200 via Stripe when redirected. Private repos use the read-only Faction Audit Reader GitHub App on that repo only. Faction does not ask for your GitHub password, 2FA, or a personal access token.

Use the public scheduling link on the site to book an intro consult. Bring a concise problem statement, constraints, timelines, stakeholders, and any existing repo or prototype context. Expect direct questions, you want a partner who disqualifies poor fits early.

Depends on intake quality and stakeholder availability; many engagements target a written scope orientation within days, not weeks, once information is sufficient. Silence usually means missing access or unclear ownership, solve that first.

Faction’s positioning emphasizes modular buys versus long-term retainers for core build work. Support and evolution can still be structured as follow-on modules when needed. If a vendor insists on a 12-month commitment for everything, compare that to modular alternatives.

A one-page brief: user, pain, desired outcome, systems involved, compliance constraints, budget band, and decision timeline. Links to the prototype and a high-level architecture diagram beat a slide deck of promises.

Both can fit if the problem is real and the team can decide quickly. Startups often need speed and focus; enterprises often need integration and governance. The common requirement is executive alignment and access to decision-makers.

Assessment tiers and hackathons are quoted at scoping. The $200 Production Discovery is the one published Faction price. Start with the free CEO roundtable on AI Consulting & Professional Services, or Production Discovery for an app audit.

Ask whether a small module or a focused audit fits. Not every problem needs a long program, clarity on deliverables matters more than calendar length.

Open Production Discovery, complete the intake form, then pay $200 via Stripe when Tally redirects you. Public repos can start immediately after payment; private repos need the Faction Audit Reader installed on that single repository first. You receive a branded PDF with modular scope and Statement of Work within about 24 hours and can revoke GitHub access when the report lands.

Ask during sales. Reference calls are normal for serious budgets; expect mutual fit checks and respect for past clients’ time.