Investor materials and financial notes on a conference table

Token Savings ROI

The ROI case: lower the AI bill.

Enterprise buyers already feel the pressure. AI is useful, but the monthly token bill keeps growing. RecordorAI sells a direct answer: use fewer tokens first, then pay less for the tokens that remain.

Contact See $15M plan Full one-pager

What buyers hear firstLower the billA budget-owner framing anchored in spend reduction.

What makes it credible#2 scoreRecordorAI measured 98.22% R@5 and 92.67% R@1 on the latest LongMemEval-S held-out run.

What makes it biggerAccount expansionOnce memory is in the workflow, inference can increase savings and margin.

Accuracy ranking

RecordorAI ranks #2 among published memory benchmark scores.

Ranked by the best public memory benchmark score we found as of June 2026. The spread column shows how RecordorAI compares to each score.

#1Supermemory ASMR~99.0%LongMemEval-S0.78 pp ahead

#2RecordorAI98.22%LongMemEval-S held-out R@5Current rank

#3WorldDB97.11%LongMemEval-S task-avgRecordorAI +1.11 pp

#4MemPalace96.6%LongMemEval R@5RecordorAI +1.62 pp

#5OMEGA95.4%LongMemEvalRecordorAI +2.82 pp

#6Mastra OM94.87%LongMemEvalRecordorAI +3.35 pp

#7Mem094.4%LongMemEvalRecordorAI +3.82 pp

#8Backboard.io93.4%LongMemEvalRecordorAI +4.82 pp

#9Engram91.6%LongMemEval-SRecordorAI +6.62 pp

#10Hindsight91.4%LongMemEvalRecordorAI +6.82 pp

Also tracked: Zep at 90.2% on LongMemEval and Memvid at 85.7% on LoCoMo.

The story

A four-step enterprise ROI narrative

Find visible AI spend

Start with a business already paying $100,000 per month for model usage. The budget owner can see the pain immediately.

$100K/mo

Cut repeated context

RecordorAI retrieves the right memory instead of resending full history. In the example, monthly spend drops to $60,000.

$40K/mo saved

Improve remaining cost

For eligible workloads, inference routing gives the buyer a lower-cost path without changing the core ROI story.

$60K -> $42K

Use $15M to make it repeatable

The round funds the product, sales motion, compliance, deployments, and compute reserve needed to prove the motion across accounts.

3-year plan

The bundle

The first sale should open a larger account plan.

Start with one workflow where the customer can verify spend reduction. Then expand into more teams, governed use cases, and eligible inference volume. The sales motion stays focused while the contract can grow.

Landone measured workflow

Expandmore teams and use cases

Deepeninference capacity where it fits

The $15M plan

Spend the round on the pieces that prove this can repeat

The plan funds 36 months of execution: product hardening, pilot conversion, compliance, customer deployment, and enough compute readiness to avoid being blocked if demand arrives early.

Three-year runway, with $1M shifted into compute reserve for fast demand.

Build the memory product

Core product, engineering, and enterprise-grade memory workflows.

$5.0M

Sell and prove savings

Sales, targeted marketing, deployment success, pilots, and ROI proof with customers.

$4.1M

Start inference capacity

Initial Blackwell pilot systems, Lambda capacity, ML systems support, and supply buffer.

$3.2M

Run safely for 3 years

Compliance, operations, legal, finance, working capital, and runway buffer.

$2.7M

Detailed allocation

Where the $15M goes

Product + engineering$5.0M

Sales motion$2.8M

Hardware start$2.1M

Operations$1.0M

Working capital$1.0M

Deployment + success$0.8M

Compliance$0.7M

Specialists$0.6M

Marketing$0.5M

Colo + networking$0.5M

Year 1 deployment

First four quarters after funding

Q1$1.4M

Fund the core team, kick off SOC 2, lock pilot scope, and place orders for the first Blackwell pilot systems.

Q2$1.2M

Ship the inference beta, onboard the first SaaS pilots, and set up Lambda burst capacity.

Q3$1.0M

Convert pilot usage into paid contracts and publish measured token-savings proof.

Q4$1.2M

Bring additional pilot capacity online as demand justifies, complete compliance evidence, and build the Year 2 pipeline.