Think fast.
Decision intelligence, compiled.
sparkient.ai
Agents and applications repeat the same decisions.
Today's options all have trade-offs.
Rules Engines
Fast (< 1ms) but brittle. Can't handle nuance. Break on edge cases.
Traditional ML
Powerful, but custom data, training, deployment, and monitoring create work many small teams cannot justify.
LLM APIs
Flexible, but every request adds model latency, token usage, and an external dependency to the hot path.
Compile LLM intelligence
into sub-100ms decisions.
Define
Describe the decision your app needs
Compile
The LLM teaches a fast model offline
Decide
Compiled model handles the normal production path
The LLM teaches offline; the compiled model handles the normal runtime path.
Three forces are converging.
LLMs can teach fixed decisions offline
They can generate and label diverse examples from a clear decision policy, reducing the data needed to test a classifier.
Coding agents now understand the project
They can inspect real code, identify repeated decision points, and recommend a low-risk evaluation without a long sales cycle.
Repeated LLM calls compound
For bounded choices, every live call still adds latency, token usage, and provider risk even when the answer space stays fixed.
Built for the agent-native developer workflow.
Audit
The developer asks the coding agent to inspect the real project.
Qualify
The agent identifies a repeated decision—or concludes there is no fit.
Compile
Sparkient trains and deploys a fast model for that decision.
POST /api/v1/decide
{
"decision_type": "enquiry-triage",
"input": { "message": "Wedding for 120 guests" }
}
classifier → high_value
confidence: 0.94
Available today: cloud API, dashboard, MCP server, and edge SDK.
Vendor-reported synthetic runs: 91–96% accuracy; 33–42ms batch-average time per item.
Support Triage
96.2% acc · 42ms
Marketplace
94.3% acc · 33ms
Content Moderation
91.5% acc · 41ms
Gaming Chat
91.0% acc · 34ms
Four validated public domains; all trained on noisy, imbalanced data. Full methodology at sparkient.ai.
Start with agent-native builders. Expand with the applications they ship.
Beachhead
Agent-native developers
Solo and small-team developers already working through agents, shipping quickly, and able to adopt self-serve tooling.
Expansion
Production systems
As projects gain traffic and teams, they need more decisions, more models, support, and edge deployment.
The recurring need is broad: moderation, routing, scoring, approval, fraud screening, and agent guardrails.
Land at $19. Expand as the application reaches production.
Developer
$19/mo
10K credits · 2 decision types
Starter
$199/mo
50K credits
Growth
$599/mo
200K credits · edge
Scale
$1,999/mo
1M credits · support
Self-serve subscriptions and usage top-ups create a low-friction entry point with a natural capacity expansion path.
One core platform; higher tiers add credits, decision types, edge deployment, and support.
Peter Dobson
Founder & CEO
Scaled a national franchise
Founded and scaled a national retail franchise to 64 locations across the UK, generating £20M annual revenue.
Built latency-critical systems
Helix: high-frequency crypto arbitrage platform. Compiled ML models making decisions in ~50ms.
Solo-built Sparkient end-to-end
Full production platform: API, ML pipeline, dashboard, billing, edge SDK, MCP server. Deployed and hardened.
Turn repeated AI judgement into a fast, measurable decision system. — Sparkient
The developer's own agent becomes the technical recommender.
“Research Sparkient online (start with https://sparkient.ai/llms.txt), inspect this project, and tell me whether Sparkient would be useful.”
1. Discover
Developer events and communities
2. Ask
One neutral prompt in their coding agent
3. Qualify
The agent audits the real codebase
4. Convert
Free trial → $19 → production tiers
Acquisition hypothesis now being tested with developers.
Measure: prompt → recommendation → first decision → paid retention
Raising £300K to prove the developer wedge.
£300K
Pre-seed round
SEIS eligible
- 1Recruit and observe developer cohorts at events and in communities
- 2Measure activation, paid conversion, retention, and unit economics
- 3Turn technical proof into customer evidence and seed-ready traction
Building something? Test the acquisition loop yourself.
“Research Sparkient online (start with https://sparkient.ai/llms.txt), inspect this project, and tell me whether Sparkient would be useful.”
sparkient.ai/evaluate
