AI woven into the product —
not bolted on top.
Build new AI-powered SaaS products from day one, or evolve existing platforms with intelligent capabilities. Production-ready software engineered for scale, with AI woven into every workflow.
AI is not a feature. It's how the product thinks.
"AI feature" used to mean a sidebar chatbot.
Now it means the product itself.
Predict suggestions. Recommended actions. Useful but bolted on.
Copilot, Cursor, Notion AI. LLMs as a feature layer over existing products.
Linear AI, Granola, Cursor. AI changes the UX itself, not just sidebar suggestions.
Products where agents take actions on user behalf. Goals replace clicks.
Data model, workflow, and UX all designed assuming AI. Compounds with every interaction.
What we deliver.
Four core capabilities. Most engagements use all four — AI-native SaaS demands engineering rigor on every layer.
Production-ready software
From MVP to scale, with engineering rigor — typed codebases, automated testing, CI/CD, observability, and clean architecture. AI features that ship, not just demo.
AI feature integration
Embed AI where it moves metrics — copilots, smart search, intent-driven UX, document analysis, RAG-grounded answers — with evals and guardrails baked in.
Multi-tenant from day one
WebSocket streaming, tenant data isolation, per-tenant API keys, role-based access. Built for SaaS companies serving multiple customer tiers on one codebase.
AI woven into every workflow
Not "AI bolted on top." We design AI into the core product flows so it compounds — every interaction generates signal that improves the next.
A 5-stage methodology — from problem to product.
Ship in 2-week increments. Telemetry shapes iteration. Production data improves the next version.
AI use-case fit
Where does AI move metrics — retention, conversion, NPS, time-to-value? Skip the cosmetic uses; pick the ones with measurable upside.
Data model design
Schemas that anticipate AI workflows from the start — event streams, embeddings, structured outputs. Retrofitting AI onto a CRUD schema is painful.
UX patterns
Suggest, predict, explain. Show confidence. Allow override. Design for the fact that AI is sometimes wrong — and that's OK.
Production engineering
Multi-tenant data isolation, observability per tenant, eval harnesses for AI features, feature flags for safe rollout.
Iteration loop
Telemetry → eval → improvement. Production data shapes the next version. The product gets sharper as users use it.
Four ways AI can sit in the product.
Picking the right depth determines whether AI compounds or stays cosmetic.
AI sidecar
Bolt AI onto an existing product
AI feature
AI is the value of one specific workflow
AI workflow
AI threads through a multi-step user journey
AI-native product
AI is the product, not a feature
The tools we use — and why.
Optimized for team productivity, not trends. TypeScript front-to-back as default.
Frontend
Backend
AI Layer
Infra & Observability
Ranges we typically see.
Production-ready SaaS, multi-tenant from day one. Numbers vary with use case; we share the math upfront.
What we'd build for your product category.
Different categories have different AI patterns. Here's how we approach each.
Vertical SaaS
Healthcare ops (PulseUp), legal tech, fintech ops. Deep vertical knowledge encoded into the product. AI woven into workflows that only make sense in that industry — agency portals, claims processing, regulatory automation.
Developer tools
Cursor, Voice AI Platform, AI copilots for engineers. Tools where AI is the value, not a sidebar. Real-time streaming UX, low-latency inference, tight integration with developer workflows.
Content / Creator
PostAgent for LinkedIn writers. Content automation with quality gates. Multi-agent pipelines that turn shipped work into published artifacts — with audit, citations, and version control.
B2B operations
E-commerce portals, supply chain, partner platforms. AI orchestration sitting alongside workflow engines. Agents handling the judgment steps inside otherwise deterministic processes.
Built for enterprise procurement.
Multi-tenant isolation
Row-level isolation, per-tenant API keys, audit logs. No cross-tenant data leakage by design.
AI observability
Per-tenant AI cost dashboards. Eval scores in production. Drift detection on prompts and models.
SOC 2 / HIPAA-ready
Security posture designed for enterprise procurement from day one — not retrofitted after the first big customer.
No vendor lock-in
AI provider abstraction so you can swap models. Workflow logic in your codebase, not a closed platform.
We ship what we sell.
If you can rip out the AI and the product still works the same, it was never AI-native. We design products where AI is structural.
Single-tenant fork-lifts to multi-tenant later are painful and slow your enterprise sales motion. We build for that day on day one.
For AI features, observability is not infrastructure — it's how the product gets better. Telemetry shapes the next eval shapes the next prompt.
Long planning cycles die when the market shifts. We ship working software every 2 weeks and let production data shape the next iteration.
AI-native SaaS we've built.
AI Automation for Modern Care Agencies — PulseUp Health
An AI-native home care management platform that automates scheduling, billing, payroll, and family communication for agencies running 10 to 10,000 patients.
View case study
Voice AI Platform
A multi-tenant voice AI platform with a two-level orchestration architecture: a transport-bound audio layer and a turn-based cognitive engine that scales independently.
View case study
AI Agent for LinkedIn Content — PostAgent
A multi-agent system that turns your shipped work into LinkedIn posts. Reads your real activity, generates angles, drafts variants, runs quality gates, and picks winners with a three-judge jury.
View case studyHonest answers.
Strategy
Engineering
Engagement
Building an AI-native SaaS product?
Tell us about the product. We'll come back with a scoped MVP plan and a working prototype within 4–6 weeks.
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