Reliable LLM outputs
start with clean context
Less redundant data. Lower costs. Faster responses.
More efficient & deterministic results.
Redundant context hurts reliability, cost, and speed.
Same information from docs, code, memory, and tools competing for attention. The model gets confused. Outputs become non-deterministic. Costs spiral. Latency increases.
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.
How Distill works
Math, not magic. No LLM calls. Fully deterministic.
Deduplicate
Remove redundant information across sources.
→ More reliable outputs
Compress
Keep what matters, remove the noise.
→ Lower token costs
Summarize
Condense older context intelligently.
→ Longer sessions
Cache
Instant retrieval for repeated patterns.
→ Faster responses
Result: Clean, efficient context•<50ms overhead•Deterministic & auditable
Where Distill fits
RAG, tools, memory, docs
DISTILL++Clean context, reliable outputs
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
Integrates with your AI stack
Reliable outputs start with clean context.
Deterministic. Auditable. Zero-latency feel.
The reliability layer your LLM needs.