Custom AI integration built right into your product.
Semantic search instead of keyword search, personalized recommendations, content generation for users, an AI assistant as a feature of your SaaS. Including fine-tuning when the data justifies it.
Embeddings + vector DB → 10× relevance vs keyword search. Multilingual built-in, typo-tolerant, understands intent. Reranking with Cohere on top-100 candidates.
Embeddings text-embedding-3-large
Vector DB: Pinecone / Qdrant / pgvector
Cohere reranking on top-100
Multilingual with no extra config
Latency <80ms p95
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Recommendation Engine
Real-time per user/session. Cold-start handled (content-based on metadata + collaborative after the first interactions). A/B framework + segment analysis.
Hybrid: content + collaborative
Cold-start handled
A/B framework included
Real-time scoring <50ms
Segment-level optimization
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In-Product AI Assistant
A new feature for your SaaS. Context-aware (knows what screen/state the user is in, what data it has). Full usage analytics + cost tracking per user/feature.
Context-aware on app state
Function calling on your API
Usage analytics + cost tracking
Multi-modal (text + screen capture)
Streaming response UI included
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Fine-tuned Models
Fine-tuning on your own corpus for specific terminology/style. Eval framework + continuous benchmark vs base model. LoRA for optimal cost, full fine-tune when justified.
LoRA / QLoRA / full fine-tune
Custom eval framework
Benchmark vs base + GPT-4o
Hosting on Modal/Replicate/in-house
Re-training on fresh data
0× rel.
semantic search
vs keyword
MRR@10 measured
0%
engagement uplift
post-recommendation
vs control group
0 wks.
average time to
feature live
discovery → A/B
0ms
p95 latency
recommendation API
real-time inference
Case Study · Marketplace
Marketplace 200K SKU: +34% conversion after semantic search
A marketplace with 200,000 products, running an existing keyword search (Elasticsearch). 47% of searches returned zero results when users used natural language ('red evening dress for Christmas' vs 'red dress'). We built a hybrid: keyword + embeddings + reranking.
Search → purchase CR1.8%2.4%
Zero-result rate47%8%
Click-through rate22%41%
Stack & Decisions
Embeddingstext-embedding-3-large
Vector DBQdrant self-hosted
RerankingCohere rerank-v3
Hybrid retrievalBM25 + dense (RRF fusion)
A/B test8 wks., +34% lift validated
Result in 10 wks.
Search→purchase conversion +34%, zero-result rate down from 47% to 8%. Revenue attributed to search +1.4M EUR/year. Monthly inference + Qdrant cost: 480 EUR.
FAQ
Questions about Custom AI Integration
RAG (default): use it when you have specific knowledge (docs, knowledge base) and want citable answers. 90% of cases are RAG. Fine-tuning: when you need a very specific style/format/terminology (legal, medical, highly specialized technical) or a structured task with a repetitive pattern. We can combine both.
Pinecone: managed, scales without hassle, pricier (from $70/month). Qdrant: open-source, self-hosted on a VPS (from 15 EUR/month), full control, excellent performance. pgvector: integrates into your existing Postgres, fine up to 1-5M vectors. Weaviate: when you need complex query filtering.
New user (zero history): we use content-based on product/content metadata + popularity priors per segment (geo, device, hour-of-day). After 3-5 interactions, a progressive switch to collaborative filtering. For new products (zero traffic): embedding on product metadata + artificial injection into the top-N with automatic detection to avoid polluting metrics.
It depends heavily on volume. Semantic search at 100K queries/month: ~30-80 EUR embeddings + 15-50 EUR vector DB + 80-200 EUR reranking. AI assistant chat at 10K conversations/month: 200-800 EUR in tokens. Self-hosted recommendation engine: 60-180 EUR/month. We always calculate cost per user/feature beforehand.
Yes, mandatory. Before any 100% rollout, we run A/B tests for 4-8 weeks with 10-20% traffic split, measuring conversion / engagement / retention. We roll out only if the lift is statistically significant and has positive business value. Framework: GrowthBook / Statsig / custom.
Modal Labs (serverless GPU, pay-per-second, ideal for variable workloads), Replicate (similar but API-first), Together AI / Fireworks (managed inference with optimal rates), in-house on Lambda Labs / RunPod when we have large constant volume. We decide based on cost/latency profile per use case.
Before: baseline metric (search CR, engagement, retention). During build: A/B framework set up with a target metric. Post-launch: weekly reports on lift % vs control + attributed revenue/cost. If lift stays below threshold after 8 weeks, we revert. Refund on build cost if the pilot fails, confirmed by A/B.
60-minute discovery · free
Which AI feature would move the needle most in your product?
A 60-minute discovery session with our tech/product team. We assess AI fit vs non-AI alternatives, prioritize by effort/impact, and deliver a 6-12-week pilot plan with an A/B framework included.
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Discovery with your product team
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AI vs alternatives evaluation
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Effort/impact priority matrix
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Pilot plan with A/B framework
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