SlimeTree-RLM × Platform Integrations
Cut LLM cost 60-80%.

Supported platforms

SlimeTree-RLM (D/µ/R deterministic classification + audit chain) is platform-neutral. We are rolling out the same pattern — RLM pre-filter wrapping each platform's API — one platform at a time.

* RLM itself is platform-independent. We release the combinations — each platform's API + RLM pre-filter — as units of "public demo + MIT source + commercial package." Priority-platform requests welcome via Contact.

Concrete example: 10,000 DMs / month ― per provider

One month for a typical BtoC brand. Average 200 in / 1,000 out tokens; the inbound volume is unchanged; RLM filters 73% (D 6,200 + µ 1,100 = 7,300 zero-LLM-call; only R 2,700 goes to the LLM).

audit_chain_head: a3f1d2e8b4c97f5e... [✓ verified, 10,000 records]
* Sample ratios (D 62% / µ 11% / R 27%) are averages from our 4-LLM cross-validation benchmark; adjustable by industry. Prices are public unit prices as of 2026-05; verify with each provider directly.

NEXT THEME · 2026-05-30 Pattern B · Cost-tier escalation ★ All 18 demos shipped (estimate → next: measured)

★ Multi-agent layered cut ― on top of the existing 73% cut

The table above assumes "all 2,700 R-verdicts go to the same premium LLM (e.g. GPT-5 / Claude Opus)".
The next theme — LLM multi-agent support — adds another layer on those 2,700: "try cheap LLM → RLM re-judges quality → escalate only insufficient items to premium".
On top of the 73% cut, the R portion is cut by another 50-70%. Combined: 85-92%.

[Current] 10,000 items
   ↓ SlimeTree-RLM (D/µ/R)
   ↓ D=6,200 / µ=1,100 handled deterministically (0 LLM calls)
   ↓ R=2,700  ───────────────────────→  premium LLM 2,700 calls

[After Pattern B layered] 10,000 items
   ↓ SlimeTree-RLM (D/µ/R)
   ↓ D=6,200 / µ=1,100 handled deterministically (0 LLM calls)
   ↓ R=2,700
   ↓ ① Try all 2,700 on cheap LLM (e.g. Gemini Flash)  →  cheap 2,700 calls
   ↓ ② RLM re-judges output quality (R-meta verdict)
   ↓ ③ Only the ~15% judged insufficient escalate to premium  →  premium 405 calls
                                                                ↑ 6.7× fewer

Concrete example: 10,000 DMs / month · cheap = Gemini Flash · premium = GPT-5

* The 15% escalation rate is an estimate (conservative re-judging setting). 10% → $10.5/month; 25% → $23/month.
* Gemini Flash 2,700 calls ≈ $2.03 (existing table); escalated 405 calls × GPT-5 unit price ≈ $12.6 (total $14.6).

Per premium: applying the same Pattern B to 6 configurations

* cheap = Gemini Flash, fixed. Each premium uses the same 15% R-meta escalation rate (raise the rate to push more onto premium when quality sensitivity is higher).
* In every combination, cheap = the same Gemini Flash, so SMBs can run the cheap layer at $0 on the free tier (15 RPM).

Why RLM works as an orchestrator

• Drops in without modification ― replace the R path of the existing D/µ/R verdict with a 3-stage "cheap try → quality re-judge → premium escalate." Same patch applies to all 18 routes.
• Audit doesn't break ― the existing SHA-256 WAL chain simply appends two records (cheap / premium). "Which query was handled by cheap, which escalated to premium" is fully traceable from the audit log.
• Preserves the Platform-native LLM principle ― on X, Grok cheap → Grok premium; on Google, Gemini Flash → Gemini Pro; on Meta, free cheap/premium mix. No forced Gemini stuffing.
• Fallback is natural ― if the premium API fails, the cheap response can be returned (with a quality flag). Frameworks like LangGraph require explicit wiring for this.

★ Estimated vs measured (confirmed 2026-06-01)

Results from the measurement dashboard (`/integrations/measurement/`) using real Gemini Flash calls. The R rate reproduced at 28% across 2 independent runs. The escalation rate was not fully measurable because of Free-tier 429 limits; we kept the estimated value with annotation for honesty.

★ Footnote: the R rate reproduced at 28% across 2 independent runs (2026-06-01T00:23:51Z + 00:26:55Z). The escalation rate was held at the estimated value because the Free-tier 429 limit prevents full measurement. Confirmation deferred to a follow-up on a paid tier or on the WASM version of RLM.
Method: The RLM mock module (`slimetree-rlm-mock.js`, KNOWN_FACTS 6 + MUTE_TRIGGERS 4) produces D/μ/R verdicts → only R items invoke Gemini 2.5 Flash → `judgeResponseQuality` re-judges → only insufficient items would escalate to Pro (Pro was not reached during the measurement runs, so the observed escalation rate depends on cheap-response length).

★ NEW: Measurement dashboard → 6-pattern showcase → Pattern detail → Hub →

6 routes ― who gets relief from what

Each route's typical pain in one line. See the demos for detail.

Delivery options ― RLM license and LLM usage are fully separated

Supported LLM providers (your free choice, 4 + α)

The same RLM implementation routes R verdicts via the LLM you pick. No need to fix one; combine for different use cases as needed.

* Recommended starting config: Gemini 2.5 Flash (free tier) ― verify connectivity individually; switching to another LLM in production needs only swapping the key in localStorage. No RLM configuration changes required.

Technical detail, benchmarks, patents, source

"What is it doing inside?" / "Validation of the −20.4 pt cut over 6,870 trials" / "Why is a Rust standalone binary just 272 KB" lives in Resource.

• Meta integration hub (technical detail of the 6 routes)
• SlimeTree-RLM primary materials ― measurement procedure / 4-LLM cross-validation / paper v10 / patent claims 1-44
• RLM tutorial (JA: intro → intermediate → advanced)
• Gateway source code (MIT)
• Product page: SlimeTree-RLM (DEVICE primary + AI applied)

Start 30-day trial via Gateway →   Custom build / OEM inquiry