AI-native product architecture
Products designed around AI capabilities from day one — not features bolted onto a legacy core.
Platforms & Products
AI-Native SaaS Platforms
We partner with product teams to design, build, and ship AI-native SaaS with scalable architecture and strong UX.
- 12–16 wksidea to revenue-ready MVP
- 70%+gross margin protected by cost engineering
- 1platform from MVP to enterprise scale
Capabilities
Product engineering for the AI era
Product platforms built with AI at the core — for new markets and differentiation.
Multi-tenant AI infrastructure
Tenant isolation, per-customer knowledge and usage metering built into the foundation.
Unit economics engineering
Token costs designed into pricing and margins before launch, not discovered after.
Enterprise readiness
SSO, audit logs, data residency and compliance — the checklist that closes enterprise deals.
How it works
From concept to compounding product
AI-native products win on data loops and unit economics, not model access. We engineer both from the first sprint.
Architecture
Built AI-first, not AI-bolted
When AI is the product, architecture decisions change: streaming UX, eval-gated releases, feedback capture in every interaction, and a data flywheel that makes the product better with every user.
- Streaming, multi-modal UX patterns users expect
- Feedback loops that turn usage into training signal
- Eval-gated releases so quality never ships backwards
Copilot
Draft renewal summary for Acme Corp
3 accounts show churn risk — view analysis
Ask anything…
Multi-tenancy
Every customer isolated, every customer smarter
Per-tenant knowledge bases, isolated data planes and configurable model routing — so customer A's data never leaks to customer B, and enterprise buyers can bring their own cloud or model.
- Hard tenant isolation across data and vector stores
- Per-tenant model routing and feature flags
- Bring-your-own-cloud and BYO-model options for enterprise
Private VPC
GPU poolAutoscaleMulti-AZ
AWSAzureGCPOn-prem
Unit economics
Margins designed in, not hoped for
We model cost per user action before launch, then engineer it down: caching, routing, right-sized models. Your pricing page and your inference bill stay friends as you scale.
- Cost-per-action modeling tied to pricing tiers
- Usage metering ready for billing integration
- Continuous cost optimization behind eval gates
−62%p95 latency
Before
3.4s
After
1.3s
Cost / 1k tasks
$41
Optimized
$14
Use cases
Where ai-native saas delivers value
Vertical AI SaaS
Domain-deep products for legal, health, finance or logistics buyers.
AI features in existing SaaS
Copilots and automation added to your product without an architecture rewrite.
Internal product spin-outs
Internal tools hardened into sellable, multi-tenant products.
AI product rescues
Stalled AI products re-architected for quality, cost and enterprise sales.
Agent-as-a-product
Autonomous service offerings with SLAs, metering and human oversight.
Platform plays
APIs and extensibility that let customers build on your AI.
Delivery
How we build it
We start from the business outcome, then design agents, models, tools and guardrails that can survive production — not just a demo.
- Production-ready architecture
- Secure tool integrations
- Measurable business KPIs
- Operate & improve playbooks
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1
Discover
Map workflows, data, constraints and ROI.
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2
Architect
Define models, tools, memory and trust boundaries.
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3
Build
Ship a production-ready system with evals and observability.
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4
Scale
Optimize cost, quality and adoption across teams.
Two engineering quarters had produced a demo that melted at ten concurrent users. The rebuild shipped to paying customers in fourteen weeks — with margins we can actually defend to the board.
Works with your stack
Built on the tools you already run
We integrate with your models, clouds, data platforms and enterprise systems — no rip-and-replace.
- Anthropic Claude
- OpenAI
- Next.js
- React
- Node.js
- Python
- Postgres
- Pinecone
- Stripe
- AWS
- Vercel
- Kubernetes
FAQ
Common questions
We have a product idea. Where do we start?
A short discovery sprint: validate the workflow, test AI feasibility on real data, model unit economics and define the MVP cut. You leave with evidence and an architecture, not slides.
How do you keep inference costs from killing margins?
Cost is a design constraint from day one: routing simple requests to cheap models, caching aggressively, and metering usage so pricing tracks cost. We target gross margins comparable to traditional SaaS.
Can you add AI to our existing SaaS product?
Yes — that's half our product work. We integrate AI capabilities into your existing architecture and release process, usually starting with one high-value workflow in your product.
Who owns the IP?
You do — code, models, prompts, evals, everything. We're an engineering partner, not a platform you rent.
What does enterprise-ready mean concretely?
SSO/SAML, RBAC, audit logs, data residency options, SOC 2-aligned controls, admin consoles and usage reporting — the checklist your first enterprise buyer will send you, built before they send it.
Next step
Build the product your market is waiting for
Bring the idea — we'll bring the architecture, the build and the margin math.