AI SaaS products
Multi-tenant AI products from MVP through enterprise readiness.
Services
AI-Native Product Services
Turn AI capability into products and platforms your customers and employees use every day.
- 12–16 wksconcept to revenue-ready MVP
- 70%+gross margins engineered in
- 100%IP ownership with you
Capabilities
Product engineering for the AI era
AI SaaS, internal platforms, workflow automation, business apps and CX AI.
Internal platforms
Company-wide AI platforms with governance and golden paths.
Workflow automation
Agentic process automation packaged as operable products.
Customer experience AI
Support and engagement systems that resolve, not deflect.
How we deliver
Products where AI is the point
AI-native products win on data loops, unit economics and trust — not model access. We engineer all three from the first sprint.
Product core
Architected for the AI product lifecycle
Streaming UX, feedback capture in every interaction, eval-gated releases and a data flywheel — the architecture patterns that make AI products improve with use.
- UX patterns users expect from modern AI products
- Feedback loops that become training signal
- Release gates on quality, not just tests
Copilot
Draft renewal summary for Acme Corp
3 accounts show churn risk — view analysis
Ask anything…
Economics
Margins designed before launch
Cost-per-action modeled against pricing tiers, then engineered down with routing, caching and right-sizing — so growth improves your P&L instead of your cloud bill.
- Unit-economics models tied to pricing
- Usage metering ready for billing
- Continuous cost optimization behind quality gates
−62%p95 latency
Before
3.4s
After
1.3s
Cost / 1k tasks
$41
Optimized
$14
Enterprise readiness
The checklist that closes big deals
SSO, RBAC, audit logs, tenant isolation, data residency and SOC 2-aligned controls — built before your first enterprise buyer sends the security questionnaire.
- Hard multi-tenant isolation across data planes
- SSO/SAML, RBAC and admin consoles
- Compliance-aligned logging and residency options
✓PII detected & redactedenforced
✓Answer grounded in sourcespassed
✓Role-based data accessenforced
✕Out-of-scope tool callblocked
SOC 2GDPRAudit logHITL
Use cases
Product engagements we run
New AI product builds
Vertical SaaS taken from concept to paying customers.
AI features in existing SaaS
Copilots and automation added without a rewrite.
Product rescues
Stalled AI products re-architected for quality and cost.
Internal platform builds
Governed AI foundations for every team.
CX automation products
Resolution-grade support AI with measurable CSAT.
Agent-as-a-product
Autonomous services with SLAs and metering.
Delivery
How we engage
Flexible models — from strategy sprints to dedicated pods — with clear phases, quality gates, and ownership through production.
- Clear delivery process
- Senior engineering talent
- Security built in
- Long-term capability transfer
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1
Align
Goals, constraints, success metrics and stakeholders.
-
2
Design
Architecture, delivery plan and risk controls.
-
3
Deliver
Build, integrate, harden and launch.
-
4
Operate
Monitor, optimize cost and continuously improve.
Our prototype wowed investors and melted at ten concurrent users. Fourteen weeks later we had paying customers on an architecture our CTO could defend — and margins our CFO could.
Works with your stack
Modern product stack
Cloud-native, typed, CI/CD-driven — engineered for iteration speed and operational calm.
- Next.js
- React
- TypeScript
- Python
- Postgres
- Pinecone
- Stripe
- Anthropic Claude
- OpenAI
- AWS
- Vercel
- Kubernetes
FAQ
Common questions
We have designs and a deck. What's the first step?
A feasibility sprint: prove the riskiest AI assumption on real data, model unit economics and cut the MVP honestly. Two to three weeks, and you know whether — and what — to build.
How do you keep AI costs from eating SaaS margins?
Cost is a launch requirement: routing to right-sized models, aggressive caching and metering tied to pricing. We target software margins, and we've hit them repeatedly.
Can you work alongside our existing product team?
That's the default — we embed with your team, ship together and transfer the AI-specific patterns so you own velocity after we leave.
What happens after launch?
Your choice: full handover with runbooks, or a managed-operations arrangement while you hire. Either way the code, infra and evals are yours.
Next step
Ship the product, keep the margin
Bring the concept — we'll pressure-test it on real data before you bet the roadmap.