Inference cost · HR SaaS
Prompt optimization + caching + per-feature model choice. Cost per call fell 55% without quality loss.
Applied AI for b2b, technology · saas.
For tech companies that want to embed AI in their products without burning runway or compromising customer data.
Overview
What we deliver in this vertical.
B2B · Platforms · Devtools
Technology · SaaS
For tech companies that want to embed AI in their products without burning runway or compromising customer data.
−55%cost per inference
3 wksfeature to production
99.9%per-tenant isolation
Applications
- 01In-product AI
- 02Vertical copilots
- 03Multi-tenant RAG with isolation
- 04SLA-driven model evaluation
- 05Token cost optimization
Positioning
AI in SaaS is margin or differentiation — pick one.
In a B2B SaaS scale-up, AI isn't a feature — it's product strategy. Either it composes real differentiation (with defensible unit economics) or it becomes operational cost dressed as innovation. There's no middle ground, and the product team that treats it as one loses the 18-month competitive cycle to whoever decided sooner.
By the numbers
What changes when an AI feature is competitive differentiation.
−0%inference cost with prompt + cache optimization
0 wksfrom diagnostic to first feature in production
0%multi-tenant isolation in enterprise RAG
+0%ARR expansion with AI as consultative upsell
Under the hood
A stack for AI features that scale with the user base.
In SaaS the challenge isn't just quality — it's per-call margin and per-tenant isolation. The stack below shows up in scale-ups that added AI as consultative feature without inflating OPEX.
Model routing
- Per-feature routerCheap model for trivial, large model for critical.
- Semantic cacheReused responses on similar queries (Redis + embeddings).
- Prompt compressionAggressive tokenization without context loss.
Multi-tenant RAG
- Vector store with per-tenant namespacePinecone, Weaviate or pgvector with ACL.
- Cross-contamination auditRuns in CI on every deploy.
- Tenant-filtered rerankerCohere or bge-reranker.
Observability
- Langfuse / PhoenixEnd-to-end tracing with cost per trace.
- Custom eval suiteGold-set + drift per feature.
- Unit-economics dashboardCost per call × attributed revenue.
Frontend
- Vercel AI SDKStreaming UX with graceful fallback.
- React Server ComponentsSSR for pre-computed responses.
- Optimistic UIImmediate response, later validation.
In production
Results in B2B SaaS.
Feature to production · legal platform
From initial conversation to production AI feature (with observability and SLAs) in 3 weeks. Customer-facing on day 1.
Multi-tenant isolation · marketing platform
Enterprise RAG with per-tenant isolation guaranteeing one customer's data never appears in another's response. Automatic cross-contamination audit.
04 · LA AI Method
From hypothesis to operation.
Five steps that prevent eternal pilots. Each stage has deliverables, metrics and decision gates.
M1
Diagnostic
Opportunity map, estimated value, data readiness.
2–3 wksM2
Strategy
Prioritized roadmap, reference architecture, governance.
2 wksM3
Proof of Value
POC with production criteria and business metric defined.
4–6 wksM4
Deployment
Model, integrations, security, observability and UX.
6–12 wksM5
Continuous Operations
Evolution, fine-tuning, eval suite, cost per use, new features.
RecurringFrequently asked
About AI in SaaS.
Do you help my internal team or do it for me?
Both. In SaaS it's common for the internal team to lead and LA AI to act as co-architects + review. For companies without a team, we do end to end.
How to keep model cost from killing margin?
Unit economics modeling from diagnostic. Each AI feature has declared cost per call tied to measurable revenue increase or churn reduction.
Multi-tenant RAG — how do you ensure isolation?
Per-tenant ACL across all layers: vector store, index, cache, logs. Automatic cross-contamination audit runs in CI.
Can I use proprietary models instead of AI SaaS?
Yes. For cases where the AI feature is competitive differentiation, fine-tuning a proprietary model is worth it. LA AI has expertise on both sides.