Reliable LLM outputs
start with clean context
Same inputs should produce same outputs. We make that possible.
Deterministic deduplication. Zero-latency feel. No inference. Full traceability.
LLM outputs are unreliable because context is polluted.
30-40% of RAG context is redundant. Same information from docs, code, memory, and tools competing for attention. The model gets confused. Outputs become non-deterministic.
Non-Deterministic Outputs
Same workflow, different results. Redundant context makes outputs unpredictable run-to-run.
Confused Reasoning
Models see the same thing 5 ways. Signal gets diluted. Long-horizon reasoning breaks down.
Production Failures
"Vibe coding" works in demos. In production, unreliable outputs block enterprise adoption.
"Garbage in, garbage out"
- The fundamental problem with LLM reliability
You can't fix unreliable outputs with better prompts.
You need to fix the context that goes in.
Clean context → Reliable outputs.
Math, not magic.
No models were harmed in the making of this optimization.
CLUSTER
Agglomerative clustering groups similar chunks.
~6ms
SELECT
Pick the best representative from each cluster.
<1ms
RERANK
MMR balances relevance and diversity.
~3ms
Total: ~12ms•LLM calls: 0•Deterministic, auditable, reliable
Where Distill fits
Any vector database
DISTILLCluster, Select, Rerank
Any model
A reliability layer between your data sources and your LLM. No infrastructure changes required.
Why not just use an LLM?
LLMs are non-deterministic. Reliability requires deterministic preprocessing.
| LLM Compression | Distill | |
|---|---|---|
| Latency | ~500ms | ~12ms |
| Cost per call | $0.01+ | $0.0001 |
| Deterministic | No | Yes |
| Lossless | No | Yes |
| Auditable | No | Yes |
Use LLMs for reasoning. Use deterministic algorithms for reliability.
Built on research, not hype.
We read the papers. We wrote the code.
Agglomerative Clustering
Hierarchical clustering that adapts to your data. No need to specify K. No hyperparameter tuning.
Average linkage • O(n² log n)
Maximal Marginal Relevance
Balance relevance and diversity in one pass. λ = 1.0 → pure relevance. λ = 0.0 → pure diversity.
Carbonell & Goldstein (1998)
SIMD-Accelerated Similarity
Vectorized distance computation. Process 50 chunks in under 2ms.
Pure Go • No dependencies
Enterprise ready.
Determinism, traceability, and compliance for production AI.
Fine-grained Authorization
Secure RAG access controls
Audit Logs
Full traceability
PII Detection
Auto-redact sensitive data
On-Prem / VPC
Your infrastructure
Pricing
Open Source
Free
Forever
- ✓Core deduplication engine
- ✓Clustering + MMR algorithms
- ✓Local execution (your infra)
- ✓Pinecone & Qdrant support
- ✓Community support (GitHub)
Cloud
Popular$0.001/query
Pay as you go
- ✓Everything in Open Source
- ✓Hosted API (99.9% uptime)
- ✓Advanced re-ranking models
- ✓Real-time usage analytics
- ✓1K free queries/month
- ✓Email support
Enterprise
Custom
Volume discounts
- ✓Everything in Cloud
- ✓Fine-grained authorization
- ✓SSO (SAML, OIDC)
- ✓Audit logs & compliance
- ✓On-prem / VPC deployment
- ✓Dedicated support & SLA
Works with your stack
Reliable outputs start with clean context.
Deterministic. Auditable. Zero-latency feel.
The reliability layer your LLM needs.