FAQ for real searches.

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.

R.A.P. is a disciplined way to ship a single automation slice quickly—think one workflow automation or integration track—priced as a fixed module rather than an open-ended build. If you want one automation validated and deployed using a repeatable engineering rhythm, it is a pragmatic format.

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.

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.

Public-facing pages cite common starting bands for MVP-style programs, but credible pricing depends on module count and integration complexity. A serious answer requires a scoped story map and constraints review—not a generic estimate from a landing page.

It is a fixed module of professional time aimed at one coherent product slice. Practically, you should expect planning, implementation, review checkpoints, and delivery artifacts aligned to that slice—not unlimited scope changes within the same module. If requirements expand, the next step is another module or a scope revision.

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.

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.

Use the dedicated pages for AI assessments and AI hackathons on this site; they describe tiers and what each tier is meant to solve. If your procurement team needs a formal quote, say so during scheduling.

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.

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