No. Your agency stays client-facing and owns the relationship. We work as async white-label delivery support behind the scenes, with artifacts you can review before anything reaches the client.
Your agency won the client. The scope is messy. Send the brief, PDF, Figma, or call notes. We turn it into a delivery path, repo, SDLC artifacts, QA handoff, and implementation plan.
Who It Is For
Your team keeps client ownership. New Normal AI Lab provides the implementation capacity, SDLC artifacts, QA handoff, and governance-ready documentation that make delivery easier to review, estimate, execute, and hand off.
Take on more client work with async white-label implementation capacity, full SDLC control, and clean delivery handoff.
Convert briefs, call notes, Figma files, and product assumptions into requirements, architecture, backlog, implementation, and release docs.
Turn AI use cases into prototype modules, internal tools, integration flows, and reviewed systems with traceability built in.
Move beyond standard websites without drowning in backend scope, API decisions, QA expectations, and documentation gaps.
SDLC System
We do not just write documentation. We deliver software with the SDLC discipline agencies need to scope, build, test, release, and hand off confidently.
What goes into a delivery package?
Each engagement is scoped to the project. The output can be a prototype module, integration, internal tool, SaaS platform, AI-enabled workflow, backend/API system, or full project repo.
Starting from $1,500
For agencies that need to clarify scope before build: SRS, SAD, architecture notes, API/data model spec, backlog, acceptance criteria, test strategy, estimate, and handoff docs.
Request sample discovery packStarting from $3,500
For teams that need to validate the hardest part first: review-ready prototype repo, core workflow implementation, API scaffold, data model notes, fixtures, README, and assumptions.
Request prototype sampleStarting from $7,500+
For first-release builds: scoped MVP implementation, frontend/backend/API as needed, integrations as scoped, SDLC docs, test strategy, QA handoff, release notes, and delivery documentation.
Discuss MVP deliveryScoped per project
The core offer: white-label delivery capacity for complete software projects, including implementation, SDLC package, QA support, release handoff, deployment notes, and optional governance layer.
Send a scope for delivery reviewAvailable as add-on
For AI-enabled systems where traceability matters: AI risk register, human review gates, audit trail structure, model/tool assumptions, data handling notes, and ISO 42001-style readiness checklist.
Ask about governance layerHow It Works
Intake
Send a PDF, Figma link, RFP, repo, call notes, rough requirements, or client brief. We read it like a delivery team, not a strategy workshop.
Structure
We turn the input into requirements, architecture decisions, acceptance criteria, test strategy, backlog, risks, and handoff expectations.
Build
We build the agreed slice or full project with frontend, backend, APIs, integrations, data model, and repo documentation matched to the scope.
Review
We review edge cases, test paths, acceptance criteria, risk notes, release gaps, and what your client team needs to validate before sign-off.
Package
You get the repo and the SDLC artifacts together, so your agency can inspect the work, continue the build, or present it cleanly to the client.
Deliver
We stay behind the scenes as delivery capacity. You remain the client-facing owner, with clearer scope, cleaner execution, and less delivery drag.
Sample Delivery Package
The sample package is a mock example of how we combine implementation and SDLC control artifacts. It shows the code structure, delivery docs, review notes, and handoff materials an agency can inspect.
Request Sample Delivery Packagesample-b2b-ops-portal/
app/
dashboard/page.tsx
api/workflows/route.ts
api/reports/route.ts
components/
workflow-board.tsx
review-queue.tsx
audit-timeline.tsx
lib/
permissions.ts
supabase-admin.ts
validation.ts
docs/
SRS.md
SAD.md
TEST_STRATEGY.md
QA_HANDOFF.mdGovernance Layer
Governance is a trust layer around delivery. It helps your agency ship AI-enabled work with clearer assumptions, review gates, and traceable handoff notes, without overstating compliance claims.
Known AI assumptions, risk categories, reviewer notes, and mitigation ideas captured alongside the build.
Decision points where a person reviews generated outputs, client-facing copy, approvals, and operational actions.
A practical event model for prompts, tool usage, decisions, changes, and release notes where traceability matters.
Notes on data sources, retention expectations, access boundaries, sensitive fields, and external system dependencies.
A lightweight checklist that helps agencies discuss governance posture without claiming certification.
Documented use of models, third-party APIs, automation boundaries, fallback paths, and client review responsibilities.
Reference Points
Common questions from agencies evaluating async delivery capacity, SDLC artifacts, and client-ready handoff support.
No. Your agency stays client-facing and owns the relationship. We work as async white-label delivery support behind the scenes, with artifacts you can review before anything reaches the client.
Send whatever exists: PDF scope, RFP, Figma file, call notes, repo link, backlog, Loom, rough requirements, or a messy client email thread. The first job is turning that input into delivery structure.
No. Documentation is the control layer around the build. Depending on scope, we can deliver a discovery pack, technical slice, MVP, full project repo, integration work, or governed AI delivery add-on.
Governance is included where it matters and can be added for AI-enabled systems. We document assumptions, review gates, risk notes, audit trail structure, and ISO 42001-style readiness without claiming certification.
For a clear scope packet, the first response is usually enough to identify package fit, missing information, and the next delivery step. Larger builds need a scoped review before timeline and pricing are responsible.
Contact
Send the client scope, PDF, Figma file, repo link, call notes, rough requirements, or project outline. We will review fit, missing assumptions, likely delivery package, and the next practical step.
Good first inputs