Embed AI into your SaaS before your competitors do.
We build AI features for B2B SaaS products that increase activation, retention, and conversion - multi-tenant by design, API-first, and deployed in weeks not quarters.
Talk to an AI SaaS engineer2.4×
avg conversion uplift from AI search
< 50ms
semantic search latency target
6 weeks
to first AI feature in production
AI features that drive retention
AI Search
Semantic search that understands intent, not just keywords. Vector embeddings over your product's data with sub-50ms latency. Instant upgrade to natural language queries for end users.
Product Copilot
In-app AI assistant that understands your product's data model and helps users complete tasks faster. Reduces support tickets, increases feature adoption, and drives time-to-value.
Recommendations
Personalised content, product, and action recommendations based on user behaviour, preferences, and similar user patterns. Increases session depth and drives secondary feature adoption.
Auto-tagging & Classification
Automatic categorisation of user-generated content, support tickets, documents, and records - reducing manual tagging overhead and enabling downstream automation workflows.
Summarisation
Automatic summaries of long documents, activity feeds, meeting transcripts, and datasets. Surfaces what matters without requiring users to dig.
The multi-tenant challenge
SaaS AI is meaningfully harder than single-tenant AI. These are the three problems we solve before writing feature code.
Data isolation
Each tenant's data must be isolated - in training, retrieval, and inference. Shared models must not leak tenant A's data into tenant B's outputs. We enforce isolation at the embedding store and retrieval layer.
Per-tenant vs shared models
Shared models reduce cost but require careful separation. Per-tenant fine-tuning delivers higher accuracy but increases operational complexity. We architect the right balance for your scale.
Cost attribution
AI feature costs vary dramatically by usage. We build per-tenant token and inference cost tracking from day one - enabling usage-based pricing and identifying unprofitable users.
Activation & retention impact
2.4×
Trial-to-paid conversion
AI search embedded in a B2B marketplace - users who used AI search were 2.4× more likely to convert.
+38%
Feature adoption
Copilot feature drove 38% higher adoption of adjacent product features in the first 90 days post-launch.
−22%
Churn reduction
Personalised recommendations reduced churn by 22% among users who engaged with the feature at least once.
Integration patterns
API Layer
Best for: Search, classification, copilot
When
Synchronous AI features (search, classification, real-time recommendations)
AI microservice behind your existing API gateway. Stateless, horizontally scalable, returns structured JSON. Easiest to integrate and monitor.
Background Job
Best for: Auto-tagging, report generation
When
Async AI features (batch tagging, summary generation, model retraining)
Job queue (BullMQ, Celery, SQS) with AI worker pool. Results stored and surfaced when ready. Lower cost, tolerates latency.
Streaming
Best for: Copilot, summarisation
When
Copilot and generative features where perceived speed matters
Server-Sent Events or WebSocket streaming from inference layer. First token in < 500ms. Significantly better UX for text generation.
Cost per feature use
SaaS AI unit economics can make or break a product line. We architect for cost from the start - not as an optimisation task at the end.
Cache embeddings - most queries hit the same vectors repeatedly. Cache aggressively at the embedding layer.
Model tiering - route simple classification tasks to smaller, cheaper models. Reserve frontier models for complex generation.
Prompt compression - user context summarised before each call. Reduces tokens without reducing output quality.
Usage budgets - per-tenant soft limits with graceful degradation. Unprofitable usage patterns flagged automatically.
Batch where possible - nightly batch for non-urgent AI tasks (tagging, indexing) at 10× lower cost than real-time inference.
"AI search increased trial-to-paid conversion 2.4× for a B2B marketplace."
Semantic vector search replacing keyword search across 200K+ product listings. 40ms average query latency. Multi-tenant embedding isolation across 800 supplier accounts. Shipped in 5 weeks from scoping to production.
From first call to AI feature in production - in six weeks.
We run a structured engagement designed for SaaS engineering teams - low drag, high output.
Week 1
Discovery
We map your data model, user journeys, and existing API surface. Define AI features and success metrics.
Week 2
Architecture
Design integration pattern, isolation strategy, cost model, and tech stack. Engineering spec signed off.
Week 3–4
Build
AI microservice or pipeline built, tested, and integrated into your staging environment.
Week 5
Validation
QA against your data, latency benchmarking, tenant isolation testing, and A/B test setup.
Week 6
Production
Feature flags, gradual rollout, monitoring dashboards live. First AI feature shipped.
Hiring an in-house AI team vs. shipping with StartxLabs.
The SaaS AI stack, organised by what it does.
“We went from keyword search that missed half our catalogue to semantic AI search with 40ms latency - trial-to-paid conversion went up 2.4× in 90 days.”
2.4×
Trial-to-paid conversion uplift
40ms
Average query latency
800+
Supplier accounts isolated
5
Weeks from scoping to production