Sera turns complex greenhouse data into clear, actionable understanding. Here’s what commercial growers like you ask most about our platform, implementation, and impact.
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Question
What is Sera?
Sera is a Greenhouse Intelligence Platform (GIP) that acts as the reasoning layer above your existing climate computers, sensors, irrigation systems, and operational tools. It continuously interprets data across topics like climate, irrigation, nutrition, energy, pests/disease, and more—delivering plain-English observations, root causes, and recommendations via an assistant chat, automated reports, and workflows. Our core promise: Leave dashboards behind. Instead of digging through graphs, you get pushed insights like "Zone 3 RH +8% overnight—caused by dehumidifier lag; adjust startup offset +5 min to prevent transpiration stall." We focus on turning your expertise into tailored intelligence through our configurable knowledge base, ensuring consistency across teams and sites.
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Who is Sera for?
Sera is built for sophisticated commercial greenhouse operators with meaningful scale and complexity—typically growing high-wire crops like tomatoes, peppers, cucumbers, or lettuce. Our ideal customers are those facing data overload from multiple systems, needing better cause-effect understanding to optimize yields, cut waste, and scale reliably. Buyers are often CEOs, GMs, head growers, or operations leads; daily users include growers, facility managers, and IPM specialists. If you’re a small hobby farm or basic hydroponic setup, Sera might be overkill—our focus is on enterprise-grade intelligence for professional operations.
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How does Sera differ from traditional greenhouse software or dashboards?
Traditional tools provide data visualization or simple alerts—Sera provides interpretation. We sit above your stack as the "why" and "what next" layer: correlating millions of points to explain behaviors (e.g., "Heating lag from boiler short-cycling—impact: RH +6% and minor yield drag"), recommend actions aligned with your SOPs, and orchestrate responses automatically. No more reactive firefighting; our assistant lets you ask "Audit irrigation vs. slab trends this week" for instant answers, while the knowledge base operationalizes your best practices (e.g., site quirks like sticky vents or cold corners). Competitors might unify data for reports—we unify it for reasoning, tailored to your operation, not generic rules.
Question
What data sources does Sera integrate with?
Sera connects seamlessly to major climate computers (Priva, Hoogendoorn, Ridder, etc.), substrate sensors (WC, EC, pH), energy meters, weather stations, scouting logs, spreadsheets, and ERP systems—without custom development. We handle flexible ingestion for manual entries or legacy files, turning everything into a unified, secure warehouse. If you have a unique setup, our team can assist with integration during onboarding. All data stays yours, encrypted, and versioned for compliance.
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How long does implementation take?
Most setups go live in 4–8 weeks, depending on your stack complexity. Week 1–2: Data mapping and integrations (e.g., connecting your Priva system and sensors). Week 3–4: Configuring your knowledge base with SOPs, thresholds, and site context. Week 5+: Testing with your team, initial observations, and fine-tuning workflows. We handle the heavy lifting remotely, with minimal disruption—many customers see value in the first week through basic assistant queries. No hardware installs required.
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What about security and data ownership?
You own your data—Sera acts as a secure processor, not a holder. All information is encrypted at rest and in transit, stored in enterprise-grade cloud warehouses with role-based access, audit logs, and compliance for GDPR/EU equivalents. We silo data per customer, never share or use it for training models. Regular penetration testing and backups ensure resilience. If needed, we support on-prem options for ultra-sensitive operations.
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How much does Sera cost?
Pricing is tailored to your scale (hectares, sites, complexity) and starts at $X per hectare/month [placeholder—use actual tier]. It includes unlimited assistant queries, workflows, knowledge base, and integrations. No setup fees for standard implementations; we offer pilots for qualified operations to test value. ROI typically hits in 3–6 months through yield lifts (2–12% in cases), energy savings (5–15%), and reduced waste. Contact us for a quote based on your setup.
Question
What kind of support do you provide?
Our team includes horticultural experts and engineers for ongoing guidance—available via chat, email, or calls during business hours (extendable for 24/7 operations). Onboarding includes customized training sessions for your growers and ops team. Post-launch, we monitor platform health, suggest optimizations, and update your knowledge base as needed. Early adopters report faster issue resolution and more proactive advice than traditional software support.
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Can Sera help with specific challenges like pest management or energy costs?
Yes—Sera excels at topic-specific intelligence. For IPM, it forecasts pressure from climate-trap correlations (e.g., "Whitefly risk rising—release Encarsia per threshold"). For energy, it spots waste like short-cycling and recommends fixes (e.g., "Raise min runtime 2 min to cut 4% usage"). See our solution pages for details on climate, irrigation, nutrition, energy, pests/disease, and more. All tied to your SOPs for tailored relevance.
Question
What results can I expect?
Based on real cases: 2–12% yield increases from optimized control; 5–15% input savings (water, energy, fert); 50% error reduction in multi-site ops; and reclaimed hours daily from automated insights. Results vary by your baseline—pilots help quantify. We focus on concrete outcomes like tighter dry-back consistency or faster anomaly resolution, not vague efficiencies.
Question
How do I get started?
Book a demo—we’ll walk through your setup, show tailored examples (e.g., assistant queries on your crop type), and discuss a pilot if it fits. No commitment required. If you’re ready, email [placeholder] or use our contact form.
Question
There’s a lot I see with my eyes that’s not in the data. This is important stuff. How can the system know that? Can it work without that?
Yes — visual observations, grower intuition, and on-the-floor notes are extremely valuable, and Sera is designed to incorporate them rather than ignore them.
The system works perfectly well with only digital data (climate logs, sensors, irrigation events, trap counts, etc.), but it becomes significantly more accurate and relevant when you feed in what you see. Here are the main ways Sera captures and uses grower eyes-on knowledge:
Manual logging directly in the platform: Quick entry of observations like “slab cracking observed in Sector B truss 5–7,” “early signs of interveinal chlorosis lower canopy,” “sticky vents in Zone 3,” or “uneven fruit set on north side Block 4.” These become part of the reasoning instantly — the assistant references them in future answers and root-cause analyses.
Knowledge Base entries: Upload or type in one-time notes about site quirks (“cold corner Zone 3 always reads 1.5 °C low — ignore RH alerts there during misting”), recurring patterns you notice (“whitefly pressure always higher near propagation doors”), or visual rules (“do not trust leaf temperature sensors in high-radiation periods — use canopy temp from IR gun”). Once in the knowledge base, Sera factors them into every observation, recommendation, and alert suppression.
Chat context: You can tell the assistant what you’re seeing right now (“I see wilting in the top of Block 2 even though WC is 62% — what’s going on?”) and it will reason over your input together with sensor data, historical patterns, and your protocols.
Iterative refinement: As you log more visual observations over time, the system learns your operation’s real-world behavior better — e.g., “this grower consistently reports condensation 30 min before RH sensors show it in Zone 4 → adjust dehumidifier trigger logic.”
In short: Sera starts strong with digital data alone, but the more grower eyes-on knowledge you add (via logs, knowledge base, or chat), the more precise and trustworthy it becomes. Many of our most advanced users treat the platform as a two-way conversation — they feed in what they see, and Sera gives back explanations and suggestions that respect both the numbers and the grower’s real-world read.
Question
Do you write to the climate computer?
No — Sera does not write directly to your climate computer or any control hardware.
We are deliberately read-only on the control side. Sera ingests data from your climate computer (setpoints, actuator positions, sensor readings, alarms, etc.), but we do not send commands back to change setpoints, open vents, start boilers, adjust irrigation valves, or modify any control logic.
This design choice gives you several important advantages:
Full safety and control — you (or your trusted automation vendor) always retain final authority over what the hardware actually does.
No risk of unintended control conflicts or overrides.
Easier integration and faster onboarding — most climate systems allow read access via existing APIs, Modbus, or file exports without opening write permissions.
Compliance-friendly — audit trails show exactly what was recommended vs. what was executed, which is valuable for food safety, sustainability reporting, or insurance.
That said, we do support closing the loop in other ways:
Smart alerts & workflows route high-priority recommendations to the right person (e.g., “VPD spike likely from vent lag — check motor response”) with one-tap accept/apply if your team uses mobile tasks.
Orchestration integrations can push approved changes to downstream task systems (e.g., create work order in your CMMS, notify maintenance via app, or trigger a checklist in your ERP).
Protocol-driven suggestions are written so clearly that growers can manually implement them quickly — many customers report implementing 70–90% of high-confidence recommendations within the same shift.
If you ever want semi-automated or fully closed-loop control in the future, we can discuss integrations with compatible execution layers (e.g., third-party auto-pilot modules that accept write commands), but our core platform remains read-only on control hardware by design.
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How accurate are the recommendations?
Recommendations are only as good as the data and knowledge base you provide. With clean sensor data and your uploaded SOPs/site quirks, accuracy is very high—many users implement 70–90% of high-confidence suggestions. You always review and approve actions; Sera explains its reasoning so you can trust (or override) it. Early feedback loops (logging what you see) make it even sharper over time.
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How does the system actually generate recommendations?
Sera generates recommendations through a continuous reasoning process that combines:
Real-time data ingestion — It pulls millions of data points every minute from your climate computer, sensors (WC, EC, RH, temp, PAR, etc.), irrigation logs, energy meters, scouting entries, and external weather.
Physics-based and domain-specific models — We apply horticultural principles (transpiration models, VPD–uptake relationships, CO₂ assimilation curves, pest reproduction rates at given temps/RH, boiler thermal dynamics, etc.) to interpret how the greenhouse is behaving right now.
Your uploaded knowledge base — This is the key differentiator. Your SOPs, crop strategies, stage-specific targets (e.g., generative dry-back 14%, whitefly threshold >5/trap), site quirks (“cold corner Zone 3 always reads 1.5 °C low”), residue rules, and best practices are turned into active logic. Every recommendation is cross-checked against these rules so it stays tailored to your operation—not generic.
Historical context and pattern matching — Sera compares current conditions to your past cycles, spotting deviations from proven high-performers (e.g., “This dry-back pattern matches your best 28-day lettuce cycle—advance next irrigation 12 min to replicate”).
Cause-effect correlation — It traces anomalies across variables (e.g., “RH +9% overnight → delayed dehumidifier after pipe ramp → boiler short-cycling under load-shed rule → minor transpiration stall”). Recommendations include the why, the projected impact, and the exact next step (e.g., “Raise min runtime 2 min or disable load-shed during ramp window”).
The output is always in plain English, prioritized by crop impact, and delivered via:
Pushed daily briefs / anomaly alerts
On-demand assistant chat (“Why heating lag?” → full explanation + fix)
Automated workflows (threshold hit → push SOP checklist)
You review and act—no black-box auto-control. The more accurate your knowledge base and the more visual observations you log, the sharper and more trustworthy the recommendations become over time.
In short: Data + horticultural models + your expertise = reasoned, protocol-safe suggestions that explain why and what to do next.
