AI features your gross margin can live with.

Every SaaS roadmap now has an AI line. Few have the architecture for it: multi-tenant isolation, per-customer cost attribution, eval pipelines, and pricing that survives token bills. We build AI-native features that demo well and scale better.

Discuss your use case →What we build

quantpi · industry/telemetry

$ industry.constraints()
✓ design point: COGS per tenant · isolation: hard multi-tenant
✓ deployment: cloud · hybrid · air-gapped
✓ proof standard: measured on your data
# domain constraints are design inputs, not blockers

IND/01What's at stake

The AI feature that delights in the demo can drown your margins at scale.

Token costs are a new COGS line that most SaaS pricing wasn't built for. Ship AI features without per-tenant cost attribution and usage governance, and your best customers become your least profitable. The architecture decisions — model routing, caching, context discipline, tenancy isolation — determine whether AI features compound value or erode it.

IND/02What we build here

Use cases we ship

AI feature engineering

Copilots, summarization, generation, and intelligence features built into your product — eval-gated, latency-budgeted, and shipped behind flags.

rollout: flag + canary

Multi-tenant LLM architecture

Hard isolation of tenant data through prompts, retrieval, caches, and logs — provable to your enterprise customers' security teams.

isolation: provable

Cost & model routing layer

Request-level routing across models by complexity, caching and context-trimming by design — the difference between 80% and 8% feature margins.

routing: complexity-based

Embedded RAG for customer data

Per-tenant retrieval over customer content with strict scoping — the ‘ask your data’ feature, done without cross-tenant nightmares.

scoping: per-tenant strict

Eval & release infrastructure

Golden sets per feature, regression gates in CI, and quality dashboards — so model upgrades are routine instead of terrifying.

upgrades: routine

AI pricing & packaging support

Usage modeling and margin analysis that informs how you price AI features — credits, tiers, or included — with data, not guesses.

basis: measured usage

IND/03Domain constraints we design for

Built for your constraints

Per-tenant cost attribution from the first request
Hard tenancy isolation across all AI data paths
SOC 2-aligned logging and data handling
Model-agnostic abstraction — switch providers without rewrites
Latency budgets per feature, enforced in CI
Graceful degradation when providers fail or throttle

IND/04Questions, answered straight

FAQ

Should we build on GPT-4-class APIs or run open-weight models?

Start with frontier APIs for speed; design the abstraction so high-volume, lower-complexity calls migrate to cheaper or self-hosted models as usage data accumulates. Most mature SaaS AI stacks end up hybrid — the architecture decision that matters is making routing changeable, which we build in from day one.

How do we stop AI features from destroying gross margin?

Instrument first: per-feature, per-tenant token costs from launch. Then engineer the cost curve — caching, context trimming, model routing, batch processing — and feed real usage data into pricing. We've seen feature COGS drop 60–80% post-launch through routing and caching alone.

Our enterprise customers ask hard AI security questions. Can you help?

Yes — we build the answers: tenant isolation architecture, data-flow documentation, retention controls, and the security-review pack your sales engineers need. ‘Is our data training your model?’ should have a one-word answer backed by architecture.

Can you work alongside our existing product team?

That's the default mode — we embed with your engineers, ship the first features together, and leave behind the eval infrastructure and patterns your team uses for everything after. Capability transfer is part of the deliverable.