Stop buying software. Start buying decisions. Aluvial gives product, feedstock, and investment teams go/no-go answers on performance, placement, and commercial viability in weeks — not seasons.
The models are calibrated against real production environments. The outputs are built for the decision in front of you, not averaged across conditions that don't apply to your commercial case.
Deployment intelligence for
Placement decisions that need to hold up under scrutiny.
SEQ 03 // THE SOLUTION
Aluvial is built around one question most agricultural trials fail to answer: where does this product work, for which genetics, and under what conditions?
That answer requires resolving three interacting dimensions across real production systems, not averaging them away.
Decision framework
What you plant
What you plant determines how a product performs. A fungicide applied to a susceptible versus a tolerant variety can produce a 15–25 bu/ac yield response difference in wheat — same product, same environment, genotype drives the outcome.
Where and when it grows
Soil water capacity, temperature, and nitrogen availability interact with genotype and management in ways that averages obscure and only conditional modeling reveals.
How it is grown
Application timing, nitrogen rate, termination window, and tillage practice modify both the product signal and the environmental response.
Aluvial resolves the G×E×M interaction at field scale — producing responder classifications, placement maps, and go/no-go recommendations grounded in how crops actually behave across real production systems.
Operating scale
Aluvial accelerates client R&D work by leveraging AI inference and biophysical modeling at the 'micro' scale and the 'macro' scale. Micro focuses on product-level response questions. Macro focuses on system-level placement and management questions.
Micro
Which genetics respond, where the signal holds, and when environment changes the answer. This is the product question.
Macro
How place, timing, practices, and constraints shape performance across production regions. This is the placement question.
Bio-industrial value chain
Three segments. Different questions. The same need for a confident answer before capital moves.
What Aluvial is not
Maps and charts are delivery surfaces, not the product. The product is the bounded prediction behind the decision.
Aluvial doesn't ask a language model to pattern-match toward plausible advice. The output is a bounded prediction with a confidence interval, not a recommendation that sounds right.
The engine simulates crop behavior under genetics, environment, and management constraints — then returns uncertainty when the evidence isn't strong enough to decide.
Why this matters now
If the evidence does not support a confident go recommendation, Aluvial says that early. A Fast No protects field budgets, channel relationships, and launch timing before a weak signal gets scaled.
45Z applies to fuel produced after December 31, 2024. CORSIA enters its first phase. RED III transport targets are moving into EU implementation. Feedstock programs that contract now without field-level CI intelligence risk locking supply sheds that won't clear.
P50 is the median outcome. P10 is the stress-year view. For launch, contracting, and compliance decisions, the P10 often determines whether the program still works when conditions turn against it.
Single-point yield assumptions don't stress-test a deal. Aluvial converts agronomic variance into P10/P50/P90 exposure by geography — so investment teams can defend portfolio risk, CI exposure, and asset fit.
Customer-specific precision
Every engagement makes the models more precise — compounding across your geographies, your genetics, your management systems.
When your proprietary trial data and operational knowledge are integrated with Aluvial's continental-scale foundation models, the result is a customer-specific model no competitor can replicate. Your data reflects conditions and decisions only you have accumulated. Combined with Aluvial's biophysical priors, predictions sharpen beyond what either source produces alone.
Client data is isolated by engagement — never used to train shared models or inform outputs for other clients.
Scope the decision worth winning — geographies, products, success criteria. We define what a go/no-go answer looks like before any modeling begins.
Aluvial scientists work directly with your team, combining our foundation models with your proprietary data to iterate against the actual commercial decision.
Results land where decisions get made — through the Aluvial interface, API, or MCP integration into your existing workflows.
SEQ 06 // SEE IT IN ACTION
Frame is the client entry point for Aluvial's AI work: a place to inspect modeled evidence, run scenario questions, and turn outputs into shareable commercial recommendations. It gives teams a common operating surface before results move into API, MCP, or internal reporting workflows.
What customers do here
Maps, charts, and reasoning over endpoints.
Test products, practices, and geographies at scale.
Share results that drive R&D and commercial decisions.
6. API & MCP Integration
For clients with internal data-science capacity, Aluvial delivers machine-readable outputs directly — via REST APIs and MCP servers hosted at alvl.io.
Pull trial placement, CI scoring, genotype-conditional simulations, and decision endpoints directly into data science pipelines and product workflows.
Connect via MCP and Aluvial's decision engine becomes a tool your own models can reason over on demand.
Structured access covers both micro-trial simulation outputs and field-scale macro-trial synthesis.
Your systems. Our intelligence. No intermediary.
Four output families anchor Torrent engagements: performance, feedstock, diligence, and enterprise delivery. Each produces decision-ready evidence your team can use, extend, or connect into existing systems.
Launch decisions improve when you can see responder classes, trial placement priorities, and downside conditions before broad field spend.
Feedstock programs need localized CI, yield, and contract risk views before acreage and procurement move forward.
Compare assets and regional strategies with modeled yield, risk, and CI exposure rather than single-point assumptions.
Some clients need direct exports and ongoing compliance infrastructure, not just a one-time memo.
What you retain
A first Torrent scope establishes the decision baseline your next scope builds from. Three engagements over 18 months should not produce three disconnected PDFs; they should produce a sharper model of your products, geographies, and decision thresholds.
The starting model, assumptions, thresholds, and known uncertainty bands for your scope.
Maps, confidence distributions, ranked geographies, and recommendation status your team can reuse.
Aluvial Frame for human review, with API/MCP delivery when your internal systems need structured access.
Subsequent scopes compare against the prior baseline, narrowing uncertainty where evidence accumulates.
Aluvial Torrent can start with a focused placement question or expand into a strategic system for feedstock, diligence, MRV, and machine-readable delivery.
Controlled response modeling identifies where a product works, who responds, and where a launch is likely to stall — before field budgets and channel relationships are committed.
The output is a defensible launch case with placement recommendations, not an average across environments.
Model where yield, oil content, carbon intensity, and profitability clear together — before acreage, procurement, or compliance claims move into the market.
The result is a pathway screen that distinguishes where a program actually qualifies from where it only looks like it does on paper — and where a single stress year breaks the economics.
Convert agronomic variance into investment-grade diligence — expected performance, downside exposure, and regional fit modeled before capital is committed.
The output can hold up in investment review, commercialization planning, or partner diligence.
Once the value is proven, some teams need to operationalize it — structured exports, MRV workflows, and machine-readable delivery built into their internal systems.
This is the path from a validated Torrent engagement to permanent infrastructure.
Every Torrent engagement is scoped around a named commercial decision, not billable field days or disconnected data exports.
Client data is isolated by engagement and never used to train shared models or inform outputs for other clients.
Outputs are built for product managers, BD leads, investment teams, and technical teams that need structured access.
SEQ 08 // THE PROCESS
A repeatable engagement model — scoped to the decision, delivered against it, built to compound.
Aluvial Torrent tiers
Understand customer objective
Onboard users to Frame + API for foundation models — foundation model data only
12-month license
Understand customer objective
Add one customer data variable; simulate over foundation models
Deliver Aluvial Frame UI/UX + API access
Understand customer objective
Multi-factor data: foundation + customer + additional variables. Access, compile, calibrate, target, simulate.
Client-branded Aluvial Frame workspace + API/MCP, 12-month license
Prior-only Performance Intelligence starts at $10K; managed Frame, Execute, and Deploy stages are quoted as bounded sprint packages.
SEQ 09 // THE TEAM
Founder and CEO
Commercial & AI Strategy
Translates commercial reality into AI strategy — setting the implementation path so the science lands as customer outcomes.
Founder
Plant Physiology & AI Research
Bridges biophysical science and machine learning — building the models that turn G × E × M complexity into reliable, in-field predictions.
Aluvial Advantage
Decades of commercial and scientific depth, deployed as focused strategic implementation — customers see value fast.
Every decision Aluvial produces — from a single product placement to a multi-region portfolio — is built on models calibrated against field and greenhouse observations across major commodity and oilseed crops.
The physics constraints are not a claim about rigor. They are what make the confidence intervals meaningful — bounding predictions within what plant physiology and environmental forcing actually allow, so uncertainty estimates reflect the real sources of variance in the decision, not a sensitivity analysis applied after the fact.
SEQ 10 // CLOSE
Book a Goal session.
30-minute call to scope your objective and decision question.
Pilot, Targeted, or Strategic — sized to the answer you need.
Frame UI/UX, API, and optional MCP delivered on the timeline you pick.
