Production e-commerce search with proven relevance
Fine-tuned ML pipeline that delivers 28% better results than vanilla TypeSense/OpenSearch—now deployed and ready.
Benchmarked on 130K real Amazon queries (ESCI dataset):
| Kwiree (fine-tuned) | 0.332 NDCG@10 |
| OpenSearch Semantic + Rerank | 0.319 NDCG@10 |
| OpenSearch BM25 + Rerank | 0.307 NDCG@10 |
| OpenSearch Hybrid | 0.302 NDCG@10 |
| OpenSearch Semantic | 0.296 NDCG@10 |
| OpenSearch BM25 | 0.258 NDCG@10 |
Production Ready, Not Vapor
This is not a design partner program. The API is live.
What's ready:
- Documented REST API (ingest, search, analytics)
- Deployed on production infrastructure
- Monitoring and alerting configured
- 99% uptime SLA with prorated credits
- P99 latency <150ms on production traffic
What's NOT ready yet:
- Synonym management
- A/B testing framework (use your own tools)
- Advanced merchandising rules
- 24/7 support (48hr email response, monitoring is 24/7)
Why Not Build This Yourself?
"Can't I just hire a contractor to build hybrid search?"
You could. But here's what that actually looks like:
Build it yourself:
Cost: $15K-30K upfront + $3K-5K/month maintenance
Risk: You need ML expertise in-house to maintain. If contractor leaves, you're stuck. Model degrades over time without retraining.
Use Kwiree:
Cost: $949/month (early adopter, 12 months)
Risk: Solo founder, 99% SLA, early product.
But: proven tech, no build cost, exit anytime
The question: Is saving $40-80K/year worth it?
For most teams with limited ML expertise, managed service makes more sense.
Technical Architecture
Hybrid pipeline: BM25 + fine-tuned embeddings + fine-tuned reranking, optimized for e-commerce product search.
Query:
"wireless headphones for running"
BM25 Retrieval
(100 candidates)Lexical matching on titles/descriptions
p50 latency: 14ms
Embedding Search
(100 candidates)Fine-tuned on e-commerce queries
p50 latency: 30ms
Fusion
(50 candidates)Reciprocal rank fusion
p50 latency: 14ms
Reranking
(Final 10)Fine-tuned on e-commerce queries
p50 latency: 40ms
Total latency: 100ms (120-150ms p99)
Addressing the Elephant in the Room
Solo Founder Risk: Let's talk about it.
The concerns are valid:
- •Single point of failure (what if I get hit by a bus?)
- •Limited bandwidth (support response times)
- •No redundancy (if I'm sick, things slow down)
- •Uncertain future (will this become full-time?)
Here's how I'm mitigating these risks:
1. Technical safeguards
- Infrastructure is automated
- Monitoring runs 24/7 (I get paged)
- Data backups are automated and tested
- Code is documented for potential future team
2. Exit guarantees
- Data export API available anytime
- First 14 days is free, evaluate, no strings attached
- No long-term contracts (month-to-month)
- If I shut down: 90-day notice + model handoff
- Fine-tuned models would be open-sourced if I quit
3. Commitment path
- Currently: Part-time (employed elsewhere)
- At 10 customers: Full-time commitment
- At 25 customers: Hire support engineer
- Being transparent about growth path
4. Track record
- Built production ML search systems at 2 companies
- Know how to run infrastructure at scale
- Have deployed customer-facing APIs before
- Not my first production system
The honest assessment:
- •If you need enterprise stability → use Algolia
- •If you're okay with early-stage risk for up to 70% savings and better relevance → this might work
- •If you're risk-averse → wait 6 months, come back
I'm not hiding the risks. You're betting on:
- The technical approach (proven in benchmarks)
- My ability to operate infrastructure (proven at prev jobs)
- Early-stage execution risk (real, but mitigated)
FAQ
Ready to validate the claims?
Run the benchmarks yourself, or apply for a free benchmark on your catalog