VegradeAI engineering
Capability

Full-Stack AI Applications

We design and build complete product surfaces—APIs, web apps, and AI features—with clear boundaries between UX, application logic, and model services so your team ships fast without painting into an architectural corner.

Full-stack AI applications your users can rely on.

B2B SaaS copilots, customer portals, internal platforms

Phases

4-phase program

Timeline

8–14 weeks depending on surfaces and integrations

Outcomes

3 target deliverables

Problem framing

Where teams lose leverage

AI demos bolted onto brittle monoliths collapse under real traffic, security review, and evolving model requirements. You need full-stack delivery with architecture that grows with the business.

  • 1

    Unclear boundaries between UI, APIs, and model services slow every release.

  • 2

    Monoliths make it hard to scale retrieval, agents, and transactional workloads independently.

  • 3

    Security and observability are retrofitted instead of designed in from day one.

Target outcomes

What this engagement delivers

  • Layered architecture with documented APIs and ownership boundaries

  • Microservices or modular services sized to your stage—not premature complexity

  • Production deployment paths with CI/CD, monitoring, and KT-ready documentation

Scope

Deliverables we commit in writing

Exact backlog is tailored in discovery; below is representative of what enterprise buyers typically require for acceptance.

01

System design and planning aligned to current needs and future growth

02

Microservices, event-driven, and API gateway patterns where they earn their cost

03

Presentation, business logic, and data layers with clear contracts

04

Deployment targets your team already uses—containers, serverless, or managed runtimes

05

High availability, performance tuning, and security architecture by design

Program structure

Phased delivery model

Milestones map to artifacts you can review with engineering, security, and finance stakeholders.

1

Week 1–2

Requirements & architecture

Business goals, technical constraints, and reference architecture.

2

Weeks 2–8

Core build

APIs, frontends, AI feature wiring, and integration with your data plane.

3

Weeks 8–10

Hardening

Load testing, security review pack, and observability.

4

Week 10+

Launch & handoff

Production cutover, runbooks, and engineering KT to your team.

Reference view

Logical architecture

Your production topology will reflect your cloud, identity, and data residency choices — this diagram communicates control points and trust boundaries we design around.

Technology

Typical stack (vendor-neutral)

We standardize on primitives your team can operate — and avoid stack-lock where it hurts maintainability after handoff.

TypeScript / PythonNext.js · ReactFastAPI · NodePostgreSQLAWS · GCPKubernetes

Indicative timeline

Typical product programs: 8–14 weeks depending on surfaces and integrations

Final scope depends on your data maturity, integration count, and compliance requirements — all defined in the written SOW.

Get a scoped estimate

Governance

Security and compliance posture

We implement technical controls and documentation suitable for enterprise procurement — not checkbox theater.

Defense-in-depth: authN/Z, encryption, and audit-friendly logging

Environment separation (dev / staging / prod) with promotion workflows

Architecture decision records your team can extend after handoff

Procurement

Statements of work, change control, and optional penetration-test windows are scoped explicitly. Legal sign-off remains with your counsel.

FAQ

Technical and commercial questions

Full-Stack AI Applications

Ready to scope this engagement?

Thirty-minute discovery call. Fixed written scope within a week. No open-ended hourly burn.