Compiled Decision Intelligence

Think fast.

LLM intelligence, compiled.

Sub-100ms decisions that are smarter than rules and cheaper than LLMs. We compile LLM reasoning into fast, portable models — shifting your intelligence from a per-call cost to near-zero inference.

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The latency gap

<1ms
Rules EnginesDrools, OPA, Rego
1–10ms
Custom MLIf you have an ML team
10–100ms
SparkientNear-LLM intelligence
150–300ms
Fast LLMsGroq, Cerebras
1–3s+
Standard LLMsGPT, Gemini, Claude

How It Works

From definition to decision in hours

No training data. No ML expertise. Define what you need to decide and Sparkient handles the rest.

1

Define

Describe your decision in plain English. What are the options? What rules should always apply?

2

Teach

Our LLM teacher generates thousands of labelled examples from your definition. No historical data needed.

3

Compile

We train a fast classifier that replicates the LLM’s judgment. Hyperparameter-tuned and ONNX-exported.

4

Deploy

Call the API for sub-100ms decisions. Or export an edge bundle with zero cloud dependencies.

Request
curl -X POST https://api.sparkient.ai/decide \
  -H "Authorization: Bearer sk_live_..." \
  -H "Content-Type: application/json" \
  -d '{
    "type": "content_moderation",
    "input": {
      "text": "Check out this amazing product!",
      "user_trust_score": 0.82
    }
  }'
Response · 3.2ms
{
  "decision": "approve",
  "confidence": 0.94,
  "latency_ms": 3.2,
  "reasons": ["CONTENT_SAFE", "TRUSTED_USER"],
  "escalated": false
}

Ready to integrate in minutes, not months.

<100ms
Decision latency (p95)
~$0
Marginal inference cost
0
Training data required
10x
Faster than fast LLMs

The Economics

Intelligence shouldn't cost per-request

LLM APIs

Variable cost. Pay per decision.

500K decisions/day × $0.001

$182K/year

Sparkient

Fixed cost. Compile once, run free.

500K decisions/day × ~$0

~$0/year

Sparkient shifts intelligence from a variable cost to a fixed cost. The LLM teaches during training. The compiled model decides in production — at effectively zero marginal cost.

Benchmarks

Proven on real decision domains

Every number below comes from an end-to-end compilation benchmark — Gemini teacher → compiled model → evaluation on held-out data. No cherry-picking.

Benchmarked

Content Moderation

4-class moderation (allow / flag / restrict / remove) compiled from Gemini judgments. Beats the best ML baseline by 13 pp macro-F1.

0.73Macro-F1
29msp95 latency
+13ppvs best ML baseline
99.9%Cost reduction vs LLM
Benchmarked

Gaming Chat

4-class gaming chat enforcement (allow / mute / restrict / ban). Compiled policy exceeds both teacher and ML baselines on the same data.

0.80Macro-F1
30msp95 latency
+3ppvs best ML baseline
99.9%Cost reduction vs LLM
Benchmarked

Marketplace Listings

4-class listing review (approve / flag / restrict / reject). Compiled model achieves 95.1% F1 — surpassing every baseline.

0.95Macro-F1
33msp95 latency
+3ppvs best ML baseline
99.9%Cost reduction vs LLM

Our Story

Why we built Sparkient

We spent years building systems where speed and intelligence both mattered, systems that needed to make smart decisions in milliseconds. AI platforms that used LLMs for remarkable reasoning — but at 1–3 seconds per call and costs that scaled linearly with every request.

We kept running into the same two gaps. Rules engines are fast but fragile. LLMs are intelligent but slow and prohibitively expensive at scale. The space between 10ms and 100ms — fast enough for any hot path, intelligent enough for real judgment — was completely empty. And nobody had solved the economics: how do you get LLM-quality decisions without paying per-request?

The insight was simple: use the LLM as a teacher. Let it make thousands of decisions offline, carefully, with all its reasoning power. Then compile that intelligence into a fast model. Ship the compiled model. Get LLM-quality judgment in under 100 milliseconds — at effectively zero marginal cost.

“We took slow intelligence and made it fast.”

— Peter Dobson, Founder

FAQ

Common questions

Compiled Decision Intelligence is a new approach that uses a large language model (LLM) as a teacher to generate training data offline, then compiles that intelligence into a small, fast model that runs in production. The result is LLM-quality decisions in under 100 milliseconds, at effectively zero marginal cost per inference. The LLM teaches. The compiled model decides.

Traditional LLM APIs charge per request — at scale, this means hundreds of thousands of dollars per year for high-volume use cases. Sparkient shifts this from a variable cost to a fixed cost. The LLM is only used during training to generate synthetic data and label examples. Once the model is compiled, it runs in production with near-zero inference cost. You pay to compile the intelligence once, then run it effectively free.

Sparkient decisions typically complete in under 100 milliseconds (p95), with many tabular-dominant decisions completing in under 10ms. This is 10–30× faster than the fastest LLM inference providers like Groq (150–300ms) and over 100× faster than standard LLM APIs (1–3 seconds). Fast enough to sit in any latency-sensitive hot path.

No. Sparkient generates its own training data using an LLM teacher. You define your decision type in plain English — what the options are, what rules should always apply — and Sparkient handles synthetic data generation, labelling, model training, hyperparameter tuning, and deployment. No ML team required.

Get early access

Sparkient is in private beta. Join the waitlist to be among the first to compile your decisions.

No credit card required. We'll reach out when your spot is ready.