AI-native SaaS for US, UK & EU founders
We build AI SaaS apps that don't burn cash on inference
AI-native SaaS products built right — multi-tenant architecture, usage-based billing for LLM costs, eval suites, prompt versioning, model routing (Haiku/Sonnet/Opus), and proper observability. Built on Claude, GPT, and Gemini with a provider-agnostic core. Typical engagements run 10-20 weeks.
- ✓ Multi-tenant SaaS shell + LLM-powered features in one engagement
- ✓ Usage-based billing (Stripe metered) tied to real token spend
- ✓ Eval suites that gate every prompt change before deploy
- ✓ Model routing — cheap models for easy tasks, premium for hard ones
- ✓ Prompt versioning, drift detection, and cost dashboards
- ✓ Provider-agnostic — swap Claude/GPT/Gemini without a rewrite
What you get
Multi-tenant + AI baked in
Workspace isolation, role-based access, AND tenant-scoped LLM features in one architecture. Not bolted on later.
Usage-based billing for AI costs
Stripe metered billing tracks real token spend per workspace. Customers see exactly what they used; you never lose money on heavy users.
Eval-first prompt engineering
Every prompt has a test suite. CI runs evals on every change. We deploy with quality scores, not vibes.
Model routing + cost guardrails
Haiku for classification and routing, Sonnet for most user tasks, Opus only when needed. Per-user budgets, prompt caching, batch APIs.
RAG done right
Vector DBs (pgvector, Pinecone, Qdrant), reranking, hybrid search, citations on every output. Not a single-vector-search demo.
Observability + drift detection
OpenTelemetry traces, Langfuse/Helicone for prompt history, alerting on quality regressions. You know when the AI breaks.
How we work
Discovery + eval design (2 weeks)
Define the product, draft eval criteria, scope cost/latency budgets, plan the multi-tenant + LLM architecture.
Build (8-16 weeks)
Iterative builds with eval-driven development. Weekly demos with eval scores, cost telemetry, and real users testing in TestFlight/staging.
Launch + monitor
Production deploy with prompt versioning, cost dashboards, drift detection, and incident runbook. Optional retainer for AI tuning.
Frequently asked
How is AI SaaS different from regular SaaS?+
Three big differences: (1) inference cost is variable per user, so billing has to be metered, not flat; (2) prompts need eval suites or you can't tell when quality regresses; (3) you need cost guardrails or one heavy user can blow your margin. We design for all three from day 1.
Which models do you build on?+
Claude (Anthropic), GPT (OpenAI), Gemini (Google), and open-source via Together/Replicate/Groq. We default to provider-agnostic architecture so you can swap when prices change or one provider has an outage.
How much does an AI SaaS cost to build?+
Pricing depends on scope — features, RAG complexity, eval requirements, billing complexity, integrations. We give you a fixed proposal after a 2-week discovery sprint. Book a discovery call for a precise quote.
How long does it take?+
AI SaaS MVP: 10-12 weeks. Full-featured AI SaaS: 14-20 weeks. Longer than a non-AI SaaS because of eval/observability work.
Do you handle the cost economics?+
Yes. We model your unit economics before we start building — token spend per active user, gross margin per pricing tier, model routing strategy. If the math doesn't work, we tell you before you spend.
Can you take over an existing AI SaaS?+
Yes. Most AI SaaS takeovers we do are because the original team shipped the LLM features without evals, observability, or cost controls. We audit, then stabilize, then extend.
What about hallucinations?+
RAG with citations, structured output (JSON schema or function calling), confidence scoring, output validation, human-in-the-loop checkpoints for high-stakes flows. We design the architecture to fail safely.
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