When revenue decisions are fractured, opportunity leaks

Enterprises have more data than ever: browsing signals, purchase records, chat logs, inventory feeds, partner telemetry. But the problem is not scarcity of data. It is fragmentation.

• Pricing teams optimize margins in isolation.
• Merchandisers chase availability from a different spreadsheet.
• Expansion planners run scenarios offline.

The result: missed opportunities, inventory whiplash, and automated recommendations that leaders cannot explain or defend.

Stratum Predict™️: one decision fabric for revenue

Stratum Predict™️ is built to stop that. We believe the future of revenue is a single decision fabric: an intelligence layer where pricing, inventory, and expansion decisions

• Share live context
• Operate under transparent guardrails
• Produce human readable rationales

One Signal Fabric: many decisions, one truth

At the center of Stratum Predict™️ is the Signal Fabric Engine™️: a decision layer that consolidates

• Customer signals: behavior, conversations, orders
• Market inputs: demand, competitor moves
• Operational constraints: inventory, SLAs, channel rules

From that single fabric we generate

• Scenario simulations that explain assumptions and outcomes
• Actionable recommendations for pricing, promotions, and restocking
• Narratives and audit trails that make each decision defendable

Teams stop arguing over whose dashboard is right and start collaborating around one coherent plan.

Research where it matters: making LLMs practical for enterprise decisions

We do not bolt on an LLM. Revenue decisions demand accuracy, speed, and explainability at scale, and recent model research turns into a practical advantage here.

One research idea we are inspired by is Mixture of Recursions, a Transformer variant that aims to squeeze more performance out of models by letting the hard tokens think deeper while keeping overall compute and memory efficient.

What Mixture of Recursions brings to the table

• Smarter, adaptive compute Instead of running every token through a deep stack of unique layers, Mixture of Recursions reuses a shared block and lets the model loop tokens through multiple times when they need extra reasoning. Easy tokens exit earlier. Hard tokens go deeper.

• Token level routing A lightweight router decides step by step which tokens deserve another pass, so compute effort focuses where it is needed most.

• KV cache designs that match recursion Caching reduces memory and I O by storing keys and values only for tokens that continue, or by sharing cached state across recursions.

In published experiments, Mixture of Recursions has shown improved compute versus quality tradeoffs and measurable throughput gains under the authors’ setup.

For Stratum, ideas like these translate into product wins: faster scenario simulation, lower inference cost, and more consistent reasoning on tricky inputs such as urgent chat lines or ambiguous purchase intents.

How Mixture of Recursions style thinking improves Stratum in practice

1. Faster, deeper reasoning on urgent signals A customer chat that says “I need this today” contains a few high leverage tokens. Mixture of Recursions style routing lets those urgency tokens get extra passes, producing sharper recommendations without forcing the entire record to pay the compute cost.

2. Real time scenario simulation at scale Decision simulations like “what if we raise price by five percent and push express shipping” require many inference runs. Efficiency gains from these architectures let Stratum simulate more scenarios in less time.

3. Cost efficient enterprise deployment By focusing compute on the parts of the input that need it, these approaches reduce overall FLOPs and memory pressure, lowering inference cost and supporting more concurrent decision threads.

4. Better explainability by design Because fewer tokens go deeper, it is easier to trace which pieces of input drove a decision. Decision narratives and audit trails become sharper. We can point to the exact chat sentence or inventory signal that changed the recommendation.

What Stratum actually produces: the intelligence you can act on

We organize outputs into a consistent, business ready insights taxonomy

• Next purchase and product affinity: top candidate items and why, with confidence and evidence
• Urgency and intent: is the customer browsing or actively buying, and should we prioritize express shipping
• Sentiment and friction signals: sentiment trendline and issues flagged from transcripts
• Price sensitivity and willingness to pay: recommended price or offer ranges
• Churn risk and retention levers: early flags and suggested playbooks
• Inventory impact: stock at risk, replenishment urgency, and allocation suggestions
• Decision narrative: two to three plain English sentences that summarize the rationale

Every output includes confidence values and the assumptions used. That makes it equally usable in approval workflows, dashboards, or automated APIs with human in the loop controls.

Concrete scenarios: how customers would use Stratum

• E commerce: A shopper views a perfume repeatedly and messages “need it tomorrow.” Stratum flags urgency, recommends a premium express offer, and simulates inventory impact if express orders spike.

• Insurance: A policyholder asks about extended coverage after a claim. Stratum identifies cross sell potential, estimates LTV uplift, simulates margin impact, and produces an outreach script.

• Retail inventory: A regional warehouse sees rising views on a product. Stratum simulates price and promotion strategies, suggests a modest promotion, and produces audit trails for finance.

Governance, safety, and trust: non negotiables

Generative decisions must be accountable. Stratum builds three safety layers into every recommendation

1. Guardrails and constraints: configurable caps, compliance filters, legal checks
2. Explainability by default: every output documents assumptions, outcomes, and signals
3. Auditability and monitoring: logs of inputs, outputs, and business outcomes to detect drift or bias

Why enterprises should care now

• Fragmentation costs money. Siloed decisions leak margin and erode experience.
• Opaque automation undermines trust. Boards and regulators expect explainability.
• Research makes production better. Ideas like Mixture of Recursions translate to faster, cheaper, and clearer decisions.

Stratum Predict™️ brings these elements together

• A Signal Fabric Engine that centralizes context
• Generative models that explain their choices
• Architecture advances that make enterprise grade performance feasible

The invitation

We are building a platform for teams who want to replace guesswork with governed decisions. If you lead pricing, inventory, or revenue operations and care about transparency, explainability, and measurable outcomes, we want to talk.