ফ্লিট ভলিউমে লেবেলযুক্ত ফিল্ড টেলিমেট্রি
প্রতিটি সম্পূর্ণ ব্লক সময়, কভারেজ, ব্যাটারি ড্র এবং পরিবেশ প্রসঙ্গ লগ করে। ৫ GW+ দৈনিক থ্রুপুটে লেবেল দ্রুত জমা হয়।
NECTYR · ফ্লিট অপারেশন ইন্টেলিজেন্স
সোলার ফ্লিট AI ও NECTYR অপারেশন ইন্টেলিজেন্স
TAYPRO's competitive advantage is not the robot alone — it is what the intelligence layer knows. Self-learning models trained on 11 billion+ annual panel passes across 150+ plants refine soiling prediction, battery routing, and weather scheduling with every cycle. Plants commissioned today inherit years of prior fleet intelligence.
Plants commissioned today inherit years of prior fleet intelligence. NECTYR autonomously initiates, postpones, or accelerates cleaning based on weather models, soiling risk, battery state, and robot health — without waiting for an operator to rewrite the schedule after every dust storm.
5 GW+
প্রতিদিন পরিষ্কার করা সোলার অ্যাসেট
11B+
বার্ষিক পরিষ্কার প্যানেল
150+
লাইভ প্ল্যান্ট স্থাপনা
5 GW+
স্থাপিত রোবট সক্ষমতা
Fleet impact figures are modelled from live deployments. See our পারফরম্যান্স ও পরীক্ষা পদ্ধতি for assumptions.
স্কেল কেন গুরুত্বপূর্ণ
৫ GW স্কেলে AI কেন আলাদা
ইউটিলিটি-স্কেল রোবোটিক ক্লিনিং এমন স্ট্রাকচার্ড টেলিমেট্রি তৈরি করে যা একক-সাইট পাইলট মিলাতে পারে না। ৫ GW+ দৈনিক থ্রুপুটে প্রতিটি চক্র ভূগোল, মৌসুম ও অ্যারে ধরন অনুযায়ী সয়েলিং প্রতিক্রিয়া লেবেল করে।
ক্রস-সাইট লার্নিং যৌগিক হয়
এই কোয়ার্টারে কমিশন করা প্ল্যান্ট শতাধিক পূর্ববর্তী স্থাপনা থেকে শিডিউলিং ইন্টেলিজেন্স উত্তরাধিকার পায়।
প্রতিটি সাইটে মোট গভীর হয়
আরও প্ল্যান্ট মানে আরও লেবেলযুক্ত চক্র, তীক্ষ্ণ সয়েলিং মডেল ও ভাল O&M পূর্বানুমেয়তা।
তিন-স্তর প্ল্যাটফর্ম
রোবট এক্সিকিউট করে। NECTYR সিদ্ধান্ত নেয়। ORION প্রসারিত করে।
Taypro is architected as a closed loop — field hardware generates data, fleet software turns data into nightly decisions, and the roadmap layer ties cleaning intelligence to full plant health.
Today · Field
Autonomous cleaning robots
GLYDE, GLYDE-X, NYUMA, and NYUMA-X robots carry sensors, self-powered drives, and on-board safety interlocks. Every pass produces labelled telemetry: row coverage, battery draw, brush torque, wet-element state, and fault signatures.
Today · Fleet
NECTYR operations intelligence
The secure web portal where fleet AI runs live: autonomous scheduling, live robot tracking on plant layout, root-cause fault classification, predictive maintenance alerts, and exportable cycle audit logs for PR and AMC evidence.
Roadmap · Plant
ORION plant health layer
Generation-aware monitoring that extends the same intelligence rails from cleaning operations to full asset health — so owners can act before the MWh is gone, not after SCADA shows the dip.
The data flywheel
How daily fleet cycles feed AI that improves itself
Every cleaning cycle across Taypro's 5 GW+ deployed fleet generates structured data — soiling rates by geography and season, weather correlation, battery performance, and fault patterns. Models ingest this telemetry continuously: a plant in Rajasthan benefits from monsoon-shoulder timing learned in Maharashtra; a new GLYDE-X tracker site inherits routing intelligence from hundreds of prior array maps. The dataset compounds with each site added. Competitors entering today would need years of equivalent utility-scale deployments to replicate it.
The dataset compounds with each site added. Sense → Schedule → Clean → Log → Learn — every night across the fleet.
- 1.Sense
- 2.Schedule
- 3.Clean
- 4.Log
- 5.Learn
প্রতিটি চক্র কী ধরে
ফ্লিট AI প্রশিক্ষণের ডেটা লেবেল
Generic uptime monitoring tells you a robot is online. Taypro's intelligence layer labels what happened, why it mattered, and how the next cycle should change.
Soiling rate by block
Post-clean performance response correlated with geography, season, and array type — building regional dust libraries across India's hardest environments.
Weather correlation
Windspeed, rain probability, humidity, pollen, and dew context tied to each cycle decision — not binary rain sensors alone.
Battery per row topology
Charge and discharge curves mapped to row length, gap geometry, and undulation — feeding ML route optimisation models.
Fault precursors
Motor current, brush torque, and sensor deviation signatures that precede bearing wear, brush saturation, or controller degradation.
Wet-element state
Microfiber moisture detection labels cycles postponed for module protection — preventing smear damage on dusty glass.
Coverage audit trail
Block-wise timestamps, progress percentage, and exportable records for O&M governance, AMC reconciliation, and asset-owner reporting.
ছয়টি AI ক্ষমতা
সেল্ফ-লার্নিং ফ্লিট ইন্টেলিজেন্স — আজ NECTYR-এ লাইভ
These are not roadmap features. They run across Taypro's active fleet every night.
Self-learning fleet intelligence
Taypro's AI model improves soiling prediction, timing optimisation, and routing decisions with every cleaning cycle executed across the 5 GW+ fleet. A plant commissioned today inherits intelligence built from years of prior operations — seasonality patterns, regional dust signatures, and post-storm recovery timing that no new entrant can replicate from scratch.
Battery-optimised cleaning via ML array mapping
On first deployment, ML maps the full panel array — row lengths, gaps, tilt variations, undulation profiles — and builds a persistent site model. Route planning draws from this map to maximise panels cleaned per charge. The result: approximately 2× cleaning coverage per charge compared to unoptimised traversal, reducing robot weight requirements and structural load on panels and frames over the asset life.
Battery-aware intelligence in low-generation conditions
In cloudy or partial-generation periods, available battery capacity is lower. Rather than attempting full-array traversal and leaving incomplete cycles, the system analyses real-time battery state against the site map and prioritises highest-soiling blocks first — completing meaningful cleaning within available energy rather than aborting mid-fleet.
95% accuracy weather scheduling
NECTYR ingests windspeed, rain probability, humidity, airborne pollen levels, and local environmental data — not just binary rain detection — to schedule cleaning cycles at optimal windows. Verified 95% scheduling accuracy in field conditions means fewer wasted cycles, better generation alignment, and more predictable O&M budgeting.
Wet microfiber detection
Taypro robots carry sensors detecting whether the microfiber has absorbed moisture from dew, overnight humidity, or residual rain. If wet, the system postpones the cleaning cycle automatically. A wet microfiber on a dusty panel smears particulates and risks glass micro-abrasion — no human intervention required.
Autonomous fleet orchestration
Fleet rules and model outputs initiate, pause, or tighten cadence when soiling risk, weather windows, and robot health align. Dust events tighten schedules; rain or wet-element states stand fleets down. Every decision writes to block-level audit trails for governance and AMC evidence.
How robots physically execute cleans — dual-pass dry cleaning, platform weights, and field hardware — is documented on পরিষ্কার প্রযুক্তি.
মাঠে কীভাবে কাজ করে
তিন ক্ষমতা যা রাতের O&M বদলায়
Site graph persistence in NECTYR
First-deployment traversal builds a stored site graph — panel dimensions, gaps, row lengths, undulations, end conditions — that NECTYR reuses every night. Route solvers read this graph instead of re-discovering geometry, cutting dead travel and raising cleaned area per charge by roughly 2× versus greedy unmapped paths.
Tracker portfolios inherit routing heuristics from hundreds of prior single-axis graphs. Operators see coverage plans and execution logs against the same map — one source of truth for O&M and model training.
Autonomous scheduling without operator rewrites
Fixed-schedule robots pause for rain and resume on a calendar. Taypro's fleet AI ingests multi-parameter weather models, soiling forecasts, battery state, and fleet health to initiate, postpone, or accelerate cycles — without waiting for an operator to edit timers after every dust storm.
95% field-verified scheduling accuracy means fewer wasted runs after ineffective conditions and better alignment with generation windows. Wet-element detection adds a second safety layer beyond forecast data alone.
Root-cause diagnostics before dispatch
When a robot stops, NECTYR identifies where it stopped and the probable cause: misaligned panel edge, physical obstacle, motor fault, brush saturation, battery threshold, or communication drop. Field teams dispatch with the right spares instead of exploratory truck rolls.
Per-robot health profiles built from motor current, charge curves, and brush torque flag deviation signatures before breakdown manifests — hardware predictive maintenance informed by fleet-wide pattern recognition.
NECTYR live intelligence
What operators see in the field
Fleet software turns robot telemetry into decisions plant teams actually use.
Live cleaning visualisation
NECTYR overlays real-time robot position on the plant layout map. Operators see which row is being cleaned, progress percentage, and fleet state at any moment — not a last-seen timestamp from hours ago.
Per-robot health profiles
Motor current draw, battery charge and discharge curves, brush rotation torque, and sensor signal patterns build a health profile for each robot. Deviation signatures that precede bearing wear or brush end-of-life are flagged before the fault becomes a breakdown.
Autonomous fleet orchestration
Cleaning cycles are initiated, paused, and accelerated by fleet AI based on weather models, soiling forecasts, battery state, and robot health. Block-level audit trails document every decision for O&M review and asset-owner reporting.
- Live robot position tracking on plant layout map
- Root-cause fault identification — where and why a robot stopped
- Battery state monitoring and charge curve analysis per robot
- Wet microfiber detection alerts and automatic cycle protection
- Predictive maintenance alerts from fleet-wide deviation signatures
- Cycle audit logs and exportable reports for PR and AMC evidence
ইউটিলিটি O&M-এর জন্য
ফ্লিট ইন্টেলিজেন্সে কে উপকৃত হয়
The intelligence layer serves different stakeholders across the same nightly data — from block-level operators to portfolio asset owners.
Plant O&M teams
Confirm last night's block-wise coverage before the morning shift. Compare planned vs. actual cycles against the monthly cleaning strategy. Dispatch field crews with root-cause context instead of guesswork.
Explore NECTYRCentral engineering & IPPs
Spot soiling trends across multi-site portfolios. Export audit-ready cleaning evidence for AMC reconciliation and performance ratio discussions. Inherit fleet-learning models on day one at newly commissioned plants.
Live projectsPartners & investors
Understand why field data at 5 GW+ daily throughput compounds into a deepening technology advantage. NECTYR is live today; ORION extends plant health on the same rails at AMC renewal.
Company & scaleRoadmap
ORION — plant intelligence on the same rails
ORION extends the intelligence layer from cleaning operations to full plant health — generation-aware monitoring that ties cleaning, weather, and performance so asset owners can act before the MWh is gone. ORION attaches at AMC renewal on NECTYR rails already live across IPP portfolios. Early access is open for select asset owners.
ORION early accessFor investors & enterprise buyers
ফ্লিট স্কেলে যৌগিক ডেটা মোট
Every cleaning cycle across Taypro's 5 GW+ operational fleet generates structured data that trains AI models improving continuously. A competitor entering the market today would need years of equivalent deployments to replicate this dataset. Taypro's technology advantage compounds with scale — and every new site makes the model smarter for the entire fleet.
- 11 billion+ annual panel passes across 150+ plants in desert, agricultural, and coastal zones
- Labelled cleaning telemetry on NECTYR since 2022 — the largest cleaning intelligence dataset in India
- Cross-site model transfer: new plants inherit scheduling and routing intelligence on day one
- Compounding gap: each cleaning season adds regional dust libraries, fault precursors, and weather correlation depth
প্রায়শই জিজ্ঞাসিত প্রশ্ন
Taypro AI ইন্টেলিজেন্স স্তর ও NECTYR সম্পর্কে উত্তর।
It is the self-learning software stack behind Taypro's robotic cleaning fleet — models trained on labelled field telemetry from 5 GW+ of daily operational throughput that improve soiling prediction, battery routing, weather scheduling, and fault detection with every cycle. NECTYR is the live operations portal where this intelligence runs today.
Core capabilities are live across the active fleet in NECTYR: self-learning soiling models, ML array mapping, battery-aware routing, 95% accuracy weather scheduling, wet microfiber detection, and autonomous cycle initiation. ORION — extending intelligence to full plant health — is on the roadmap with early access for select asset owners.
Models trained on the live fleet are applied at commissioning. A plant going live this quarter inherits scheduling intelligence from hundreds of prior deployments — monsoon-shoulder timing, regional dust signatures, tracker routing maps, and fault precursor libraries accumulated since 2022.
SCADA tells you generation dropped. Taypro's intelligence layer tells you why cleaning may have been missed, which blocks need priority, whether robots are healthy, and whether conditions were right to clean. NECTYR complements SCADA and CMMS — it is specialised for robotic solar cleaning operations.
Structured labels including soiling rate by block, weather correlation, battery consumption per row topology, coverage audit trails, wet-element state, and fault precursor signatures. This data feeds models that improve routing and scheduling fleet-wide — not just at the originating plant.
The cleaning technology page covers dual-pass dry cleaning, platform weights, live operations intelligence, and the full AI capability breakdown. NECTYR has a dedicated product page for fleet portal features, security, and O&M workflows.