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Singular sits in front of your model calls as a secure, fully managed LLM gateway. It selects the evidence your model needs, drops redundant context, and forwards a leaner request to the provider you already use — without rewriting your application.
~81% median context reduction across a 15-case representative demo suite — not a guarantee for every workload.
How it works
A gateway that optimizes context. Not a summarizer.
Singular sits between your application and your model provider. It keeps the original evidence, drops only what the model did not need, and proves the answer stayed the same — so you send fewer, leaner tokens without changing your application.
- 01
Receive
Your app sends its request to Singular exactly as it would to your provider. Nothing to rewrite — Singular speaks the OpenAI-compatible API your code already calls.
- 02
Optimize
Before the model is called, Singular selects the chunks of context that actually carry the evidence for the answer and drops the redundant noise. Repeated patterns are indexed locally and the original text is preserved — nothing is rewritten or summarized.
- 03
Forward
A smaller, evidence-dense request reaches the provider and model you choose. If anything ever looks wrong, traffic passes straight through untouched. Every decision is logged with the reason.
See Singular select evidence
Singular keeps the evidence required for the request and removes irrelevant context — without rewriting it. Illustrative, scaled example — not the benchmark figures.
Original evidence stays intact. Singular only selects what reaches the model.
Run it beside your traffic, measure reduction and quality on your own requests, then activate. If the numbers do not hold up, you change one URL back.
Architecture
Sits in your request path. Never in your way.
Singular is an inline, OpenAI-compatible layer between your app and your provider. It removes redundant context deterministically before the request reaches the model, keeps the evidence, and gets out of the way the moment anything looks wrong.
- Gateway
- Singular sits in front of your model calls as a fully managed, OpenAI-compatible API gateway. Swap one base URL and keep the SDK, request shape, and provider you already use.
- Deterministic
- The same request always yields the same optimization. Every token we drop is logged with the reason, so results are reproducible and auditable.
- Fail-open
- If Singular ever degrades, your traffic passes straight through to the provider you selected, untouched. Your app never goes down because of us.
- Privacy-first
- We never train on your prompts, retention is configurable, and every request is encrypted in transit. Singular runs as a fully managed API, so there is nothing for your team to operate or secure.
Benchmarks
Less context. Equivalent answers.
Across a 15-case representative demo suite, Singular cut the context sent to the model by a wide margin while answers stayed equivalent to the un-optimized baseline. These are not customer results; they show what is possible on redundant, long-context workloads.
- 81%
- Median context reductioninput tokens removed
- 99.1%
- Answer quality retainedLLM-judge vs baseline
- 69%
- Blended cost reductionat a long-context mix
- +82 ms
- Added latencyp50 overhead
Even the weakest cases hold up: our lowest reduction still cut context 56%, and our lowest quality case stayed at 95.4% answer equivalence.
| Case | Domain | Context (in → out) | Reduction | Quality |
|---|---|---|---|---|
| Support ticket triage | Support | 12,400 → 1,922 | 85% | 99.6% |
| Long-doc RAG QA | Knowledge | 31,800 → 5,247 | 84% | 99.1% |
| Codebase Q&A | Engineering | 48,200 → 11,086 | 77% | 98.4% |
| Contract review | Legal | 22,500 → 3,825 | 83% | 99.8% |
| Multi-turn chat memory | Agents | 18,900 → 3,686 | 80% | 99.2% |
| Meeting-notes summary | Productivity | 14,200 → 2,556 | 82% | 99.5% |
| SQL over schema | Data | 9,800 → 1,372 | 86% | 100.0% |
| Research synthesis | Knowledge | 56,300 → 11,260 | 80% | 98.0% |
| Customer email draft | Sales | 7,400 → 2,146 | 71% | 99.4% |
| Log analysis | Engineering | 41,000 → 6,150 | 85% | 98.7% |
| Product FAQ bot | Support | 11,200 → 1,792 | 84% | 99.9% |
| Legal clause extraction | Legal | 27,600 → 5,244 | 81% | 99.0% |
| Knowledge-base agent | Agents | 33,400 → 7,014 | 79% | 98.3% |
| Compliance check | Legal | 19,500 → 8,600 | 56% | 97.2% |
| Spec-to-test gen | Engineering | 15,800 → 4,029 | 75% | 95.4% |
Demo suite · 15 cases · GPT-4o-class, default settings · quality = LLM-as-judge equivalence vs the un-optimized baseline answer · blended cost assumes input tokens are ~85% of spend · measured 2026-06. Representative figures, not customer data.
Early access
Join the waiting list!
Get notified when Singular opens the next pilot slots for teams cutting redundant context without losing evidence.
FAQ
The questions your team will ask.
What is Singular?
Singular is a secure LLM gateway that sits in front of your model calls. It optimizes context before the request reaches your provider, so you send fewer, leaner tokens without changing your application.
How does Singular integrate with our stack?
Singular exposes an OpenAI-compatible API gateway. In most cases you change one base URL and keep the SDK, the request shape, and the provider you already call. There is nothing to rewrite.
Which providers and models are supported?
Singular optimizes the context and forwards the request to the provider and model you choose. It works alongside the major providers rather than replacing them, so you stay in control of the model behind it.
Will it change the answers our model returns?
Optimization is tuned for equivalence: across our demo suite, answers stayed at or above 95% LLM-judge equivalence to the un-optimized baseline. You can run Singular side by side with your current setup and compare on your own traffic before committing.
What happens if Singular is unavailable?
Singular is fail-open. If it ever degrades or times out, your request passes straight through to the provider, untouched. Your application does not go down because of us.
What does Singular do with our data?
We never train on your prompts. Requests are encrypted in transit, retention is configurable, and Singular runs as a fully managed API, so there is nothing for your team to operate or secure on their side.
Will we really see ~81% reduction?
That is the median of a 15-case representative demo suite, not a promise for every workload. Highly redundant, long-context requests save the most; short prompts save less. Real savings depend on your traffic mix, so the honest next step is a short pilot on your own requests.
How does Singular reduce LLM costs?
Singular selects the evidence your model actually needs and drops redundant context before the request reaches your provider. With fewer input tokens billed, long-context workloads cost less while answer quality stays at or above 95% equivalence to the un-optimized baseline.
Can Singular optimize RAG token usage?
Yes. RAG pipelines often retrieve more context than the model needs. Singular keeps the chunks that carry the answer and drops the rest, which is where most RAG token savings come from.
Is Singular a summarizer?
No. Summarizers rewrite your context and can paraphrase or drop facts. Singular preserves the original evidence chunks and removes only what the model did not need, so the answer stays the same.
How do we get started?
Start with a low-risk pilot. Point a slice of your traffic at Singular, keep your baseline running in parallel, and measure reduction, quality, and latency on your real requests. If the numbers do not hold up, you change one URL back.
Still deciding? Join the waitlist to start a pilot →