Separate Venture · Seed Pitch
Cloudflare for LLM Tokens
TokeniMax sits between any application and any LLM provider. Prompt compression, intelligent routing, semantic caching, and cost governance — in one unified API layer.
“Every company using Ai is overpaying by 40-70%. TokeniMax automatically reduces that bill — invisibly, instantly, with zero code changes.”
The Platform
Four Pillars of Token Optimization
Each pillar delivers standalone value. Combined, they create a compound pipeline effect.
TF-IDF Extractive Compression
40-70% token reduction with 100% semantic meaning preserved. Removes redundant context before tokens reach the provider — fewer tokens in, same quality out.
Cheap-First Model Selection
30-80% cost reduction at 95% quality parity. Routes queries to the cheapest capable model with automatic fallback, health monitoring, and circuit breaking.
pgvector Cosine 0.85 Threshold
Up to 73% of LLM calls eliminated. When User B asks a semantically similar question to User A, the cached answer returns instantly — no LLM call, no cost.
Multi-Level Budget Enforcement
100% visibility into token spend. Per-tenant budget controls, real-time cost tracking, rate limiting with graceful degradation, and subscription-tier entitlements.
Compound Pipeline Effect
70-90%
Total cost reduction when all four pillars operate in sequence. Compression reduces tokens → routing selects cheapest model → caching eliminates repeat calls → governance enforces budgets.
Competitive Landscape
The Only Unified Platform
Every competitor solves one or two pillars. None combine all four into a compound pipeline.
| Capability | Helicone | Portkey | Bifrost | Kong Ai | TensorZero | TokeniMax |
|---|---|---|---|---|---|---|
| Cost Monitoring | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| LLM Routing | ✗ | ✓ | ✓ | ✓ | ✓ | ✓ |
| Semantic Caching | ✓ | ✓ | ✓ | ✗ | ✗ | ✓ |
| Prompt Compression | ✗ | ✗ | ✗ | ~ | ✗ | ✓ |
| Budget Governance | ✗ | ✗ | ~ | ✓ | ✗ | ✓ |
| Per-Sub Tiering | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ |
| Compound Pipeline | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ |
Built & Battle-Tested
Production Implementation: 27 LLM Packages in RA
TokeniMax is not a concept — it is extracted from production code inside the RealRiches platform.
| Pillar | Packages | LoC | Status |
|---|---|---|---|
| Routing | 7 packages | 2,600+ | LIVE |
| Caching | 1 package | 521 | LIVE |
| Governance | 3 packages | 1,100+ | LIVE |
| Compression | 1 package | ~400 | BUILDING |
Proof of Value
RA Margin Impact
TokeniMax transforms RA from good margins to exceptional margins across every subscription tier.
| Tier | Price | Before | After | Improvement |
|---|---|---|---|---|
| Pro | $89 | 84.0% | 88.2% | +4.2% |
| Elite | $149 | 78.3% | 84.9% | +6.6% |
| Team | $499 | 82.0% | 87.4% | +5.4% |
| Enterprise | $1,299 | 79.7% | 85.8% | +6.1% |
| Blended | — | 80.7% | 86.5% | +5.8% |
At Scale
Compound Savings
The savings multiply with every subscriber added to the platform.
| Subscribers | Monthly COGS Without | With TokeniMax | Monthly Savings | Annual Savings |
|---|---|---|---|---|
| 1,000 | $32,260 | $4,850 | $27,410 | $328K |
| 10,000 | $322,600 | $48,500 | $274,100 | $3.29M |
| 100,000 | $3,226,000 | $485,000 | $2,741,000 | $32.9M |
Growth Trajectory
5-Year Projections
| Year | Milestone | Customers | ARR | Valuation |
|---|---|---|---|---|
| 2026 | Seed. RA = Customer #1 | 1-5 | $0-$200K | $15-$25M |
| 2027 | Series A. 25 customers | 15-30 | $1-$2M | $80-$150M |
| 2028 | Series B. 200+ customers | 100-300 | $8-$15M | $300-$600M |
| 2029 | Growth. 1,000+ customers | 500-1,500 | $30-$50M | $700M-$1.2B |
| 2030 | Exit window | 2,000+ | $60-$100M | $1-$2B |
Seed Round
$3-5M raise at $15-25M pre-money valuation. 10-15% dilution. 18-24 month runway to Series A milestones.
Market Context
Market Comparables
| Company | Stage | Raised | Valuation | Key Metric |
|---|---|---|---|---|
| Helicone | Seed | $5M | $25M | Acquired by Mintlify Mar 2026 |
| TensorZero | Seed | $7.3M | ~$30-40M | Bessemer-backed |
| LangChain | Seed→B | $10M→$125M | $1.25B | 62x growth |
| Fireworks Ai | Series C | $327M | $4B | 10T tokens/day |
| Weights & Biases | Acquired | — | $1.7B | CoreWeave acquisition |
Defensibility
The Moat: Compound Data Flywheel
TF-IDF patterns learned from each query improve extraction quality for all future queries.
Model performance data across providers refines routing decisions for every subsequent request.
Every query warms the semantic cache. Hit rates compound: 30% → 50% → 70%. Each customer benefits all others.
Usage patterns across tenants inform budget recommendations and anomaly detection at the platform level.
Each pillar feeds data back into every other pillar. The compound effect creates a moat that deepens with every API call. 12-24 months to defensibility.
Ready to Invest?
TokeniMax is raising its Seed round. RA is Customer #1.