Build vertical AI products with platform discipline.
SoloFrame powers the flagship shared-engine contrast: DWA and GTM-OS. BizFrameHub, Colombian Spanish Academy, and 60-Day Founder extend the SVTech proof into SMB AI education, localized language learning, and founder go-to-market execution.
The architecture is why this was possible. One founder. Multiple proof surfaces.
Most agentic AI implementations fail at the quality bar: coherence collapse, hallucination at scale, no ground-truth anchor. The manifest-driven architecture is what made the clinical, commercial, SMB, and localized learning outputs coherent enough to ship.
- ✗ Coherence collapse across long content chains
- ✗ Hallucination accumulates without a ground-truth anchor
- ✗ No promotion gate - plausible-looking output ships
- ✗ Quality degrades without enforced structural guardrails
- ✓ Manifest guardrails at engine level - constraints enforced architecturally, not by prompt
- ✓ Evidence grading as anchor - every lesson cites primary literature
- ✓ PMHNP validation loop - board-certified clinicians as ground-truth signal
- ✓ MAIA promotion gate - models rejected when they miss the bar, documented when they do
Phase 2 in DWA still requires zero engine changes. BizFrameHub is launching today as a 57-course AI Business Academy for SMBs and professionals from the websites workspace. Colombian Spanish Academy proves a localized education/community vertical can move in days. Competitors cannot close that gap by hiring alone. The operating discipline is the advantage.
See the DWA vertical deep-dive ->Flagship contrast plus expansion proofs. One discipline, many outputs.
DWA and GTM-OS remain the cleanest architecture proof: opposite compliance posture, same engine. BizFrameHub and Colombian Spanish Academy add the generalization proof: SVTech can package new domains into credible product surfaces without turning each project into a sprawling custom build.
classifier: "maia"
forum: "flarum"
guardrails: [never-diagnose, ...]
classifier: "noop"
forum: "nodebb"
guardrails: [no-financial-advice, ...]
57-course AI Business Academy for SMBs, professionals, consultants, and small teams, covering marketing, sales, client work, operations, automation, and internal tools.
20 courses across 6 tracks, Tutor Luz, and a Bogotá community guide module. Proof that the same product grammar can express localized language learning and place-based community content.
Now live on Mono-PaaS: an AI-native startup academy - 257 lessons, 6 tracks, an AI build coach, and a builder certification. Proof the same engine spins up new schools, audiences, and business lines through configuration, not rebuilds. See the vertical →
Build your new academy, operator program, or branded AI learning business on the same manifest-driven platform already powering multiple live schools.
Hard rules: no vertical may fork the engine; nothing lands in packages/ until the abstraction is earned by multiple products or a committed next use case.Full architecture →
Why the engine works
Three properties the flagship products and newer proof builds share, each a direct consequence of the design decisions made from day one - and each translating into how fast future verticals ship, how safely we operate, and how the portfolio compounds.
Launch a new vertical in weeks, not quarters
A vertical is defined as configuration - not code. New products compose from shared primitives instead of duplicating systems. The first flagship took months; later proof builds can move in days.
Compliance as a setting, not a rewrite
Healthcare verticals set phi: true and inherit HIPAA-aware behavior. Non-healthcare verticals get none of that overhead. The manifest wires up retention, guardrails, and classifier sidecars; the engine enforces it.
Every vertical sharpens the next
Shared classifiers, shared assessments engine, shared analytics pipeline. The safety model a healthcare vertical trains today improves every future clinical deployment. Longitudinal data compounds into a moat competitors can't buy.
Vertical AI portfolios fail in three predictable ways
Every studio we've watched try to build multiple AI products has hit the same three walls. SoloFrame was designed against each of them from the beginning, because we're the studio too.
Each new vertical becomes a new codebase
By the third product, you're maintaining three stacks - each with its own auth, tenancy, billing, and AI wiring. The platform vision collapses into backlog.
Manifest-driven verticals. Each product is configuration, not a forked codebase. One engine, many outputs. An admission rule keeps the core small: no package lands without ≥2 consumers.
Application-code isolation is a breach waiting to happen
When isolation lives in WHERE clauses and middleware instead of the database, one missed condition in one query can expose another tenant's data.
Database-enforced isolation as a v2 target. v1 ships audited application-level scoping; v2 moves the boundary into Postgres row-level security keyed on a per-request tenant GUC. The tenantLeakHarness ships with that rollout and blocks the build on any regression.
Off-the-shelf moderation misses domain-specific risk
The signals a healthcare deployment needs to catch are not the signals a forum moderator needs to catch. LLM-provider safety filters were trained to catch different things.
Pluggable safety layers. Verticals that need a purpose-trained classifier attach one (DWA's MAIA); verticals that don't get none of the overhead. Wired in by manifest flag, not app code.
A vertical is configuration, not a codebase
A typed JSON manifest describes everything the engine needs to know about a vertical: compliance posture, AI model choices, adapter wiring, assessments, branding, roles, billing plans. Twelve primitives. All validated. All version-controlled.
Changing a vertical means editing configuration. Adding a vertical means composing primitives. Neither requires changing the engine.
{
"id": "dwa",
"compliance": {
"phi": true,
"retentionDays": 2557,
"guardrails": [
"never-diagnose",
"always-988-on-crisis"
]
},
"ai": {
"classifier": "maia",
"coachingModel":
"claude-haiku-4-5"
},
"adapters": {
"forum": "flarum",
"storage": "r2",
"mail": "resend"
}
}Six proof surfaces, one company thesis.
DWA and GTM-OS are the flagship engine contrast: PHI vs non-PHI, MAIA vs noop classifier, same shared core. BizFrameHub and Colombian Spanish Academy prove SVTech can turn domain knowledge into new commercial and localized learning surfaces quickly.
A HIPAA-aware mental-health education platform that psychiatric practices license to extend patient care between sessions. 922 lessons live now across three schools, with a Social Intelligence School next. Clinical assessments, MAIA safety classifier, provider coordination, and a per-licensee population intelligence dashboard.
A founder go-to-market operating system with 49 courses, 974 localized English-Spanish lesson files, DISC-typed roleplay, pod matching, execution AI, and API integrations.
A practical AI Business Academy for SMBs, professionals, consultants, and small teams. 57 courses across 8 tracks, launching today with curriculum organized for immediate workplace use.
A Colombian Spanish learning vertical with 20 courses across 6 tracks, AI tutoring, cultural context, and a Bogotá Curada community guide module that ties places back into lessons.
An unclaimed middle between two well-served markets
Foundation-model providers ship models, not products. Vertical SaaS companies ship products, not platforms. AI-native point products ship one use case. The middle - a platform that builds and operates vertical SaaS with a shared AI-native core - is largely unclaimed.
The education market is enormous. The wedge is fast-deployable AI-native vertical academies.
SVTech is not trying to be another LMS. The sharper claim is school-in-days deployment: your branded academy, live in days, with an AI coach that knows your curriculum, and a safety layer you can dial up to clinical grade.
HolonIQ projects the global education market will reach almost $10T by 2030.
WEF estimates 59 of every 100 workers will need training by 2030.
WEF reports upskilling is the most common workforce strategy through 2030.
IMF analysis says nearly 40% of global jobs are exposed to AI-driven change.
OpenAI, Anthropic, Google, Mistral. The infrastructure every AI product runs on - but none of them ship vertical SaaS, and none have visibility into a vertical's data flywheel.
Veeva, Toast, Procore and the rest. Excellent at one industry vertical. Rarely AI-native at the core. No reusable spine for the next vertical; each new category is a fresh raise.
A vertical PaaS that builds and operates its own vertical SaaS. Each vertical's real-world usage feeds the shared data flywheel - sharpening the platform that powers every future vertical.
Modular monolith. Horizontally deployable. Redundant by design.
SoloFrame is being built as a horizontally deployable modular monolith - one codebase, stateless app replicas, shared state in Postgres / Redis / object storage, tenant routing resolved at request time. It runs comfortably on one node today and scales to a multi-node deployment under Dokploy + Traefik + Docker Swarm as workloads grow. Multi-node isn't just for scale - it's redundancy: stateless replicas behind Traefik mean a failed node doesn't take the platform down, and rolling deploys don't require maintenance windows.
One codebase, multi-tenant, stateless replicas behind Traefik. Today's beta footprint fits on a single node.
Dedicated app replicas, dedicated DB, dedicated storage - a deployment topology choice, not a code fork.
Inference runs CPU-only for serving-path isolation (50-200ms target latency, no GPU contention with application workload). Training and retraining are GPU-bound; the data-flywheel architecture unlocks once live-customer signal accumulates and dedicated GPU capacity is available.
Root-server deployment, not Vercel-style per-invocation pricing
We deploy to VPS and root-server infrastructure via Dokploy, not to managed-PaaS hosts that bill per request, per function invocation, or per bandwidth unit. That matters for AI-heavy workloads: a single thoughtful LLM session can fire dozens of inference calls and streaming tokens, and per-invocation pricing converts engagement into variable cost that fights margin.
Running on root servers gives us predictable capacity at a predictable price. The modular-monolith-on-Swarm pattern adds redundancy and horizontal scale by adding nodes - not by paying a hosting vendor more per user. That structural advantage compounds as usage grows.
Questions we hear a lot
Fast answers to the things clients, partners, and investors most often ask.
What is SVTech Consulting?+
What is SoloFrame?+
What is a vertical PaaS, and how is it different from a foundation-model provider or a vertical SaaS company?+
What vertical SaaS are running on SoloFrame today?+
How does SoloFrame ensure tenant isolation?+
BYPASSRLS) and a withTenant(ctx, fn) contract every DB-touching engine must obey. A tenantLeakHarness lands with the v2 rollout and blocks the build on regression. Dedicated-DB deployment is available as a paid SKU for licensees who require it.What is MAIA, the distress classifier?+
How does SVTech engage with clients?+
Why a monolith instead of microservices?+
Three reasons to reach out
A custom vertical built for your organization. A licensed deployment of an existing one. An investor conversation. Pick the one that fits.
