Strategy
AI Consulting
Strategy, audits, and architecture for teams deciding what to build, what to buy, and what to delete. We come in technical and leave you compounding.
// 12 engagements · avg 4 weeks
AI consulting · SaaS · Automation
AI-native systems
that compound.
Ripple helps operators and founders ship AI products and internal systems that move real numbers. Technical depth, no theater.
ripple · bash
Services
What we do
Most engagements braid all three. Strategy informs the build, the build exposes the automation surface, automation feeds back into strategy.
Product
SaaS Development
End-to-end product builds for AI-native software. Zero to first revenue, or prototype to production-grade infrastructure.
// zero-to-one · production hardening
Operations
AI Automation
Workflows, agents, and ops tooling that take repetitive work off your team. RAG over your data, evals on your processes, integrations everywhere.
// agents · RAG · evals
Architecture · request loop
Stack
The working set
Boring on purpose. The interesting part is what we build with these, not what they are.
tool
version
role
frontend
next.js
15+
app router, server components
react
19
ui
tailwindcss
3.x
utility-first, design tokens
typescript
5.x
strict everywhere
backend
node
20+
runtime
postgres
16+
primary store + pgvector
inngest
-
workflow + retries
redis
7
cache + queues
ai / models
claude
4.5+
primary model · opus / sonnet
openai
gpt-4o
task-specific fallback
ripple/evals
-
structured output verification
infra
vercel
-
web + edge functions
railway
-
long-running services
cloudflare
-
cdn + r2 storage
How we work
A few opinions, held loosely
- 01
Build for the system, not the demo.
Most AI prototypes never reach production because they were never designed to. We start where it ends: integrations, evals, ownership, cost.
→ 0 demos in production
- 02
Right-sized models, right-sized scope.
A two-hundred-line script with a small fast model often beats a six-month platform. We pick the smallest thing that solves the actual problem.
→ 200 LOC > 6mo platform
- 03
Your data is the moat.
Foundation models commoditize. Proprietary workflows, evals, and labeled feedback do not. We design for that asymmetry.
→ proprietary > foundation
- 04
Compounds beat features.
Systems that get smarter with use are worth more than features that ship once. Every engagement plants substrate for the next one.
→ every engagement plants substrate
Contact
Tell us what you're building
Send a one-paragraph note about what you're trying to ship, or book a thirty-minute intro. We'll come back with whether we're a fit, what we'd do first, and what it would cost.
Book a call
30-minute intro