End-to-end full-stack builds, with AI in the foundation, not bolted on.
Web apps, internal tools, vertical SaaS, customer portals.
Modern stack (Vue, React, Next.js + Python, Node, Postgres) with AI agents architected in from Day 1, not retrofitted after. You own the code, the deployment, and the AI.
What “AI in the foundation” actually means.
Most “AI features” added to existing apps look like this: a developer wraps a function in an OpenAI API call, slots it into a sidebar, ships it. It works but it's brittle. The AI layer doesn't share state with the rest of the app, there's no retry logic, no observability, no audit log. When the API rate-limits or has an outage, the feature breaks.
Building AI into the foundation means the architecture has AI primitives from Day 1:
- An agent runtime (we use LangGraph) that holds conversation state, calls your APIs, retries, degrades gracefully
- A queue layer so AI calls don't block user requests
- Confidence scoring + reject paths on every AI decision
- An audit log for every AI action (input, output, confidence, what happened)
- Provider abstraction so you can swap Claude for Gemini for OpenAI without rewriting
The difference shows up in production. AI features built this way handle 100x the load, fail gracefully under provider outages, and give you the audit trail regulated industries demand. We've built this pattern into Logan, ScriptShop, and the ClaimDeck-class work. The upfront cost is small. The retrofit cost when you skip it is large.
The stack we ship on.
| Layer | Default | When we swap |
|---|---|---|
| Frontend framework | Next.js 15+ (React) | Vue 3 if you have a Vue team. Astro for content-heavy sites. SvelteKit on request. |
| UI library | shadcn/ui + Tailwind | MUI if you need denser data tables. Custom design system for productized SaaS. |
| Backend framework | Python (FastAPI) for AI-heavy work, Node.js (Hono/Express) for thin APIs | Frappe if it's a vertical CRM |
| Database | PostgreSQL with Drizzle ORM | Postgres only. We don't ship NoSQL for systems of record |
| AI orchestration | LangGraph (Python) | LangChain for simpler workflows. Architecture sprint decides current best fit. |
| AI providers | Claude (Anthropic), Gemini, OpenAI, routed per task | Self-hosted Llama / Mistral for fully on-prem deployments |
| Auth | NextAuth / Auth.js | Clerk if you need enterprise SSO fast. Custom for regulated industries. |
| Resend for transactional | Postmark or AWS SES at scale | |
| File storage | AWS S3 | Cloudflare R2 for cheaper egress. Self-hosted MinIO for on-prem. |
| Background jobs | BullMQ + Redis | Inngest if event-driven. Temporal if long-running workflows. |
| Deployment | Vercel for frontend, Render/Fly.io for backend, AWS for enterprise | Whatever your cloud is. We don't lock you in. |
| Monitoring | Sentry + Plausible + custom audit logs | Datadog at enterprise scale |
Four project shapes.
Web apps + customer portals
Customer-facing apps with auth, dashboards, custom workflows, AI features. Example: vertical SaaS with multi-tenant support, embedded analytics, agent-driven insights.
Internal tools + admin dashboards
Operations teams need software that fits their workflow. Generic tools (Retool, Internal.io) often hit a wall. Custom internal tools, built once, owned forever, solve it. Example: our own project tracker (Next.js + Drizzle + Postgres) replaced Jira+Linear for our agency ops.
Vertical SaaS products
Productized software for a specific industry. When you want to sell software to your customers (not just internal use), this is the shape. Example: Logan started as a custom CRM for one client and became a vertical SaaS product.
Legacy modernization
Replace an aging app (jQuery + PHP, .NET Framework, old Rails) with a modern stack, without losing the years of business logic. We do incremental migrations, not big-bang rewrites.
Three buckets. Most “build it custom” requests fall into one.
Bucket 1: You should extend, not build
If your need is 80% covered by an existing platform you're already on (Salesforce, HubSpot, Webflow, Wix), extending is faster and cheaper. We've talked plenty of buyers out of full-stack builds when their need was a Salesforce Lightning component + an API integration.
See Salesforce + AIBucket 2: You should productize on Frappe
If you're building a vertical CRM or operational system that needs structured business objects + permissions + workflows + reporting, Frappe is a better foundation than starting from scratch.
See Frappe CRMsBucket 3: You actually need full-stack custom
Greenfield products. Tools where the data model doesn't fit any platform. Customer-facing apps with unique UX requirements. Internal tools where you've outgrown low-code. This is when we build full-stack from scratch.
Full-stack builds we shipped.
Dear Brightly
Custom NestJS platform with embedded React frontend. Modular monolith, isolated external integrations, 60k+ users migrated.
Read the case studyGetLogan
Frappe back end + Vue frontend + multi-LLM agent layer + custom UI components. Full-stack vertical CRM in production.
Read the case studySirenAI
Self-hosted Supabase + Express 5 + BullMQ + React 19 + Mastra agents. End-to-end rebuild from a stalled SaaS.
Read the case studySame 4-step process.
Common questions.
Building something custom? Let's scope it.
Free 30-min architecture call. Tell us what you're building. We'll tell you honestly whether you need a full-stack build, an extension of what you already have, or a different approach entirely.