Compiled Decision Intelligence

Think fast.

LLM intelligence, compiled.

Sub-100ms decisions. Smarter than rules. Cheaper than LLMs.

Request Early AccessSee how it works

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

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
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
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

Our Story

Why we built Sparkient

We spent years building systems where speed and intelligence both mattered. High-frequency trading systems that needed to make smart decisions in milliseconds. AI platforms that used LLMs for remarkable reasoning — but at 1–3 seconds per call.

We kept running into the same gap. Rules engines are fast but fragile. LLMs are intelligent but slow. The space between 10ms and 100ms — fast enough for any hot path, intelligent enough for real judgment — was completely empty.

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.

“The best decisions shouldn't take the longest.”

— Peter Dobson, Founder

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.