The V2 of your business. AI-native from the foundation up.
AIOS is the flagship engagement: your operation rebuilt as one AI-native platform instead of a patchwork of rented tools. Your current business is V1, and it is the prototype. We build V2 alongside it, module by module, and you move over when it is proven.
Book a callYou already know what this costs you
A request arrives carrying twelve specific criteria. Staff search seven divisions by hand, and can still miss the right answer, because nothing in the current setup can hold all twelve at once. An hour of work that should take ten minutes.
Thousands a month going to a decades-old tool nobody would choose today, because leaving it was never made real. V2 makes the exit path real, with your data reconciled and owned before anything is switched off.
Four layers. One platform. AI does the work.
An AIOS build produces a production platform where structured data, agent intelligence, guardrails, and evaluation loops are baked into the architecture from day one.
Domain services
Bounded modules that own your business objects. Leads, profiles, contracts, invoicing, notifications. Each publishes events to one stream. Every object has one source of truth.
AI agents
Agents with defined triggers, tool sets, JSON output contracts, and human-in-the-loop policies. Email triage, lead assignment, matching, contract drafting, finance reconciliation, copilot.
Guardrails
One control plane across all agents. PII redaction on outbound LLM calls. Confidence thresholds. Cost caps per workflow. Provider fallback with automatic failover. Prompt versioning with rollback.
Evaluation loop
For every agent, the evaluation harness is built before the agent, from your historical data, during discovery. Release gates enforce acceptance criteria in CI. No agent ships without passing its own test suite.
Modular monolith. One store. One stream.
Bounded domain modules with explicit event contracts, deployed as one service over consolidated PostgreSQL and one event stream. Module boundaries enforced in code. Any module can split later without redesign.
| Domain service | Owns | Phase |
|---|---|---|
| Lead & Assignment | Lead, assignment routing | 1 |
| Entity Profile & Media | Profiles, media assets | 1 |
| Search & Recommendation | Indexes, scoring, ranked results | 2 |
| Package & Booking | Projects, packages, bookings | 2 |
| Contracts & eSign | Contract lifecycle, signatures | 3 |
| Finance & Invoicing | Invoices, payments, payouts | 3 |
| Notifications | Templates, delivery, threads | 1+ |
| Admin & Configuration | Users, roles, config | 0/1 |
| Analytics & Reporting | Metrics, dashboards | 5 |
Domain services are illustrative, drawn from a reference engagement. Every AIOS build is scoped to the client's actual business objects through AIOS Discovery.
Zero deviations
Every AIOS build follows these architectural commitments. They are not negotiable, because they are what make the AI capabilities achievable and maintainable.
One source of truth
Per business object. No duplicate masters, no reconciliation jobs, no conflicting records.
Structured data by default
Confidence-flagged extraction. Free-text becomes structured on ingestion, not after the fact.
AI-native, not AI-bolted-on
Users encounter AI in their existing screens and workflows. Not a separate chatbot tab.
Human-in-the-loop
For anything with commercial consequence, a human gives the approval. Platform-enforced, not policy-suggested.
Evaluation-first
Harnesses built before agents. Release gates in CI. No agent reaches production without passing its own test suite.
Universal audit logging
Every state change, every AI action, platform-level. Append-only, object-locked, 7-year retention.
Production Discovery to steady-state. V1 runs the whole time.
The AIOS build follows the same methodology as every Faction engagement. The scope is larger. The method is identical: three demos per module, working software over slide reviews, and your current systems untouched until V2 is proven.
AIOS Discovery
A snapshot of the whole business, not an app audit. The V2 Readiness Checklist captures your workflows, systems, SaaS spend, data, and what must never leave your walls. Your team fills it in across roles, we talk it through in a working session or two with the right people in the room, and V1 becomes the blueprint. The output is a phased, modular scope ready to contract.
Foundations
Identity, data model, event stream, audit log, CI/CD, evaluation harness scaffolding, migrate-or-freeze triage. The substrate everything runs on.
Phased build
Domain services and agents built in parallel tracks, three demo checkpoints per module. Each phase closes on written exit criteria, and you can stop at any gate with working, documented software.
The move to V2
When the platform passes its exit audit and your acceptance criteria, you move over. Not a day before. Then steady-state: agent quality monitoring, prompt tuning, security patches, iteration.
Four to eight months, depending on the business
AIOS is not one size. The discovery scopes the platform to the operation in front of it, and the module count follows the business, not the other way around.
Founders spinning up a new operation with no legacy to carry. The platform is the business, built AI-native from the first day.
The owner-led company ready to turn on its V2: core workflows rebuilt, agents in the daily work, and the legacy tools retired on your schedule.
Family businesses, blue collar and white collar, franchise groups and multi-unit operations. Departments get their own services, with compliance-grade controls where partners require them.
How it's priced
Module pricing and typical platform ranges are itemized in the written scope that follows discovery. No numbers lead here by design: the scope comes from your business, and the quote comes from the scope.
AIOS plugs into everything Faction builds
Production Discovery
The 13-layer audit for software. Every app your team builds on the platform passes through it.
MethodologyThe Conveyor Belt
The delivery pipeline: audit in, modular finishing, audit out, production on a cadence.
InfrastructureHosted AI Infrastructure
Sovereign inference, database, and monitoring on hardware you control. You own the infrastructure.
FOE
Production monitoring across every deployed agent and service.
Patching V1 vs. building V2
The modernization path
Merge legacy repositories, upgrade frameworks, layer AI in "later phases." Preserves existing technical debt, treats AI as an add-on, and takes 12 to 18 months before any intelligence reaches production. A new engine in a rusted car.
The V2 build
Greenfield AI-native, with V1 as the blueprint and V1 still running. Agents embedded in every workflow from day one, structured data by default, evaluation loops gating every release. Intelligence ships in Phase 1, and you move over when it is proven.
Your V1 taught you how the business runs. Let's build V2.
Book a call and walk us through the operation. The first step from there is the V2 Readiness Checklist: a snapshot of the business your team completes in about 90 minutes, so the scope we design comes from facts, not assumptions.