Thanish AI Innovations turns the data already trapped inside your plants, networks, and hospitals into the decisions that set your margin. Built in India, for Indian industry with deployment options designed around customer data and security requirements.
Your SCADA doesn't talk to your billing system. Your lab doesn't talk to your ERP. Your batch records live on paper. Every department carries its own version of the truth and the decisions that set your margin are still made on spreadsheets, shift registers, and instinct.
India has spent a decade and thousands of crores digitising its industries. What's missing is not more software. It is the decision layer: the intelligence that connects what you already own, and tells you what to do next.
All six run on one shared platform core: one ontology engine, one connector framework, one agent/decision layer, one security layer designed for privacy and data-handling requirements.
An ontology is a living model of your operation meters, furnaces, patients, batches, kilns, reactors and every relationship between them, refreshed continuously from your own systems.
It knows that this transformer feeds those consumers. That this heat produced that coil. That this batch failed because of that raw-material lot. Because the model understands how your business actually works, it does more than answer the question you asked. It surfaces the ones you didn't know to ask and attaches a rupee value to each.
We are not a dashboard. We are not a data lake. We are the intelligence layer that sits above your existing systems HIS, SCADA, DCS, LIMS, ERP, billing and makes them behave as one connected model.
Our motion in every industry we serve:
Deployment options designed around customer data. On-premise or private cloud. Air-gapped edge deployment for remote sites.
Privacy-focused data handling built in not retrofitted after a legal review.
Self-hosted AI models for supported deployments. Every narrative, alert, and vernacular message is generated inside your walls.
Nothing gets ripped out. We sit above the systems you have already paid for and trained your people on.
Our engineering team can work with your technical and operations teams. We learn your operation from your control room floor.
ROI in the language you already use. Rupees per tonne. Recovered claims. kcal/kg. Release-days. Recoverable units per circle.
We map your existing systems and tell you plainly what the ontology can fuse today, what needs work, and what isn't worth doing yet. You get that answer whether or not you buy anything.
Your industry's domain model is configured for your use case and deployed on your premises. You own the data. You own the metrics. First insights typically inside the first month.
A tightly scoped PoC against one number you agreed in advance: recoverable units, rupees per tonne, claims recovered, or release-days cut.
Our engineers tune the model to your operation, then expand it site by site. The subscription adapts as you grow.
Each solution is a productised decision layer for its sector explore what each one decides.
Which claims will be denied, where beds are lost, which patients are deteriorating
Recovered claimsWhich batch will deviate, what is holding up release, and your current audit-readiness posture
Release-days savedWhere loss is recoverable, which connections are stealing, what to buy and when to dispatch
Recoverable units per circleCharge mix, energy and yield in rupees per tonne, heat by heat
₹ per tonneKiln heat, clinker factor, alternative fuel mix, carbon and freight per tonne
kcal/kg clinkerReaction yield, right-first-time, effluent load and process-safety risk
Yield points recoveredEvery engagement starts with a Proof of Concept scoped to a single number, agreed with you before we begin, computed on your own data, inside your own boundary. If we don't move that number, you have lost weeks not a capital programme. If we do, you have a result your board can read without a translator, and a foundation the next six use cases sit on for free.
No. Thanish AI is a decision layer that sits above the systems you already run. Nothing is ripped out, and your teams keep working in the tools they know we simply fuse what those systems already hold.
Nowhere it isn't already. The platform deploys on-premise, in your private cloud, or on an air-gapped edge box for remote sites. Your data can be processed within your chosen deployment boundary, using self-hosted models where supported.
Usually, yes. Our Data-Readiness Assessment tells you plainly what the ontology can fuse today, what needs work, and what isn't worth doing yet you get that answer whether or not you buy anything.
Every engagement starts with a Proof of Concept scoped to one number, agreed with you in advance, and computed on your own data inside your own boundary before any large commercial commitment.
Our engineering team works closely with yours during deployment and tuning not just a support hotline. Once live, the platform runs as an adaptive subscription that keeps learning your operation as it changes.
As an adaptive subscription tied to the ontology deployed and the sites covered, anchored to the outcome the Proof of Concept already proved on your own numbers.
Talk to us about a Proof of Concept scoped in weeks, measured in numbers your board will recognise.
Every module is a different domain model running on the same engine, the same connectors, the same security spine. That combination is designed to make the platform practical for Indian mid-market economics.
Analytics tools answer the question you asked. An ontology understands the system so it can answer the questions you haven't asked yet.
A dashboard shows you that specific energy rose 4% last month.
An ontology tells you which furnace, on which shift, with which charge mix and what that drift is costing you per tonne.
The 15-minute block in power. The heat in steel. The encounter in healthcare. The batch in pharma and chemicals. The kiln-hour in cement. Every fact hangs off it, so every number reconciles.
The graph rebuilds itself as your reality changes new consumers, new equipment, new sites instead of breaking the moment the network does.
Operations data, commercial data, and instrument data joined in one model rather than three reporting stacks.
The domain model is configured for the industry and can be tuned to the customer's specific operation.
Privacy-focused data handling with self-hosted, on-premise or air-gapped deployment options.
Each solution is the shared core plus its own atomic unit, its own connectors, and its own vocabulary which is why it lands in weeks rather than being built from scratch.
Which claims will be denied, where beds are lost, which patients are deteriorating
Recovered claimsWhich batch will deviate, what is holding up release, and your current audit-readiness posture
Release-days savedWhere loss is recoverable, which connections are stealing, what to buy and when to dispatch
Recoverable units per circleCharge mix, energy and yield in rupees per tonne, heat by heat
₹ per tonneKiln heat, clinker factor, alternative fuel mix, carbon and freight per tonne
kcal/kg clinkerReaction yield, right-first-time, effluent load and process-safety risk
Yield points recoveredDeploys on-premise, in your private cloud, or on an air-gapped edge box for remote and low-connectivity sites.
Self-hosted AI models for supported deployments. Narratives, alerts, and vernacular communication are generated inside your walls.
Role-based access, audit logging, and data-handling designed with applicable privacy requirements in mind.
For regulated use cases hospitals, pharma plants the platform can support consent, redaction and controlled data access before model processing.
An honest map of what your systems can support today.
The domain ontology is configured for your use case and deployed inside your boundary.
One agreed metric, measured in weeks.
Close hands-on support, expansion across lines, circles, facilities, and sister plants, on an adaptive subscription.
See how the ontology maps to your operation.
Incorporated 2nd June, 2026. Built in India. Built for India. We build the decision layer that sits above the systems Indian industry already owns.
Thanish stands for ambition, uncompromising determination, and the pursuit of excellence. That is exactly what we build: the definitive intelligence that empowers Indian industry to realize its highest ambitions by replacing operational guesswork with mathematical certainty.
Thanish AI Innovations was founded on a simple observation. India's utilities, mills, hospitals, and plants have spent a decade installing meters, sensors, HIS systems, and ERPs. The layer that turns all that data into decisions barely exists. Global platforms are too expensive, too generic, and too cloud-dependent for the Indian mid-market. So we built the alternative domain by domain, in the units each industry already runs on. We provide the connective intelligence that Indian industry's fragmented systems have never had.
Ambition · Determination · Excellence
Each solution uses a domain-specific ontology built on the shared platform core, drawing on a shared engineering core.
The specialists know your industry. The shared core keeps the price sane. That combination is designed to make the platform practical for Indian mid-market operations.
Semantic models of the real world not dashboards stacked over a data lake.
A decision layer over the systems you already run, so adoption is low-friction and your existing investment keeps paying.
Self-hosted models, on-premise deployment, and privacy-focused data handling.
Each engagement is anchored to a measurable operational outcome and reported in the customer's own units.
Our people learn your operation where it happens. Domain knowledge is not transferable by ticket.
Whether you run a mill, a circle, a hospital group, or a plant the conversation starts with your data, not our slides.
Founder
Engineer
Business Executive
“We didn't build another dashboard. We built the layer that decides.”
The Thanish AI Innovations team
Whether you run a mill, a circle, a hospital group, or a plant the conversation starts with your data, not our slides.
Tell us which system holds the number you care about. We'll tell you what the ontology does with it.
Every conversation starts at the same address tell us which of these you are, and we route you to the right people.
Tell us your sector, the metric you want moved, and the systems that hold the data. We come back with a scoped PoC outline.
contact@ai-innovationsthanish.inSystem integrators, OEMs, and potential technology partners interested in the platform.
contact@ai-innovationsthanish.inWe are interested in engineers and domain-focused professionals who want to work on industry AI systems.
contact@ai-innovationsthanish.inTHANISH AI INNOVATIONS (OPC) PRIVATE LIMITED
C/o R Sathyaprema, Sri Lakshmi Venkateshwar,
Krishnarajapuram, Bangalore, Bangalore North,
Karnataka, India, 560036.
Date of Incorporation: 2nd June, 2026.
Email contact@ai-innovationsthanish.inThe most useful first message names three things: your sector, the metric you want moved, and the systems that hold the data.
Thanks your message is ready to send. Our team will get back to you within 1–2 working days.
MedIQ fuses your HIS/EHR, LIS/RIS, billing, and TPA/claims data into one live ontology of your hospital and turns it into decisions on revenue integrity, patient flow, clinical risk, and compliance. All inside your own boundary, with patient data protected.
ABDM is building health IDs and interoperability. NHCX is digitising claims. PM-JAY empanelment keeps widening. NABH keeps raising the documentation bar. And yet hospitals still run on an HIS designed for billing, a lab system that doesn't talk to it, and a TPA desk buried in cashless paperwork.
The pain is measurable. Revenue leaks through denials and under-coding. Beds and theatres run below their potential. Clinical risk is spotted late. None of it is a staffing failure. All of it is a data failure.
MedIQ sits above your existing HIS as the intelligence layer starting where the return is fastest.
MedIQ represents your hospital as real-world objects patients, encounters, orders, results, claims, beds, staff, supplies and the relationships between them, time-stamped to the encounter and the event. Clinical, revenue, operations, and compliance stop being four disconnected systems and become one connected picture.
The atomic units: the Patient and the Encounter. Everything the hospital does hangs off them so the same patient and the same encounter mean exactly one thing across the whole hospital.
Encounter by encounter
Four modules on a shared core:
HIS/EHR/LIS/RIS data turned into clinical-risk, pathway, and operational insights.
The claim-to-cash flow: denials, coding, pre-auth, TPA/cashless, and leakage.
The hospital as a flow system: beds, theatres, staff, inventory, throughput.
NABH/ABDM-related workflows, clinical quality, safety, and population-level insight.
Denials, under-coding, and documentation gaps caught before submission, not discovered in the ageing report.
TPA workflows tracked live against payer rules; approvals chased by data, not by desk memory.
Services delivered but never billed, surfaced from the clinical record itself.
Discharge turnaround, bed occupancy, and OT utilisation made visible per hour, per ward.
Deterioration and safety signals flagged from live orders and results.
Documentation and quality-indicator posture as a live picture, not an audit-week scramble.
We assess your HIS/EHR, LIS/RIS, billing, TPA and claims systems.
The healthcare ontology is configured for your use case and deployed inside your hospital boundary or private cloud, behind a consent-and-redaction pipeline.
Revenue-cycle integrity and patient flow on one facility. Success metric: recovered claims and charges, plus a measured reduction in discharge turnaround time.
Across departments, facilities and the wider group on an adaptive subscription.
A redaction-and-consent pipeline runs before any model sees data.
Self-hosted medical AI models can be used for supported deployments, depending on the customer's infrastructure requirements.
Health data can be processed within the customer's hospital boundary or private cloud.
Privacy-focused data handling throughout: health data treated as sensitive personal data, with consent and localisation options built in.
Mid-to-large facilities revenue integrity, patient flow, and compliance on one model.
Scheme-heavy payer mixes managed with payer-grade intelligence.
High-volume, teaching-scale operations made visible per hour, per ward.
Orders, results, and claims connected across every centre.
A rising insured population, PM-JAY expansion and NHCX are pushing claim volumes up sharply and every claim is a place where revenue can leak. TPA and cashless approvals drive long accounts-receivable days; denials and under-coding drain margin silently.
NABH, ABDM and the DPDP Act have raised the bar on documentation, interoperability and data protection. Compliance has become a data problem.
Hospitals that treat their data as an asset will fund better care from the revenue they stop losing. That is the MedIQ thesis: revenue integrity and patient flow first because those pay for the platform then clinical and population-health value on the same foundation.
A scoped PoC on one facility recovered claims and faster discharges, measured on your own data, in weeks.
PharmaIQ fuses your batch records, LIMS, QMS, ERP, and equipment historians into one live ontology of your plant and turns it into decisions on batch yield, deviation prevention, release velocity, compliance readiness, and supply reliability.
India is a major hub for generic-medicine manufacturing and the regulatory bar is rising on every side at once. Revised Schedule M makes upgraded GMP mandatory for the whole industry. US FDA scrutiny keeps citing data integrity. Export serialisation keeps widening.
Compliance has become a data problem. Meanwhile, the commercial squeeze is real: DPCO price caps, imported-API cost exposure, and buyer audits mean margins are defended batch by batch.
PharmaIQ is the decision layer that connects the batch record, the lab, the QMS, and the ERP.
PharmaIQ represents your plant as real-world objects batches, materials, equipment, rooms, tests, deviations, dossiers, shipments time-stamped to the batch and the event. Manufacturing, the QC lab, the quality system, and the supply chain become one connected model instead of four systems and a mountain of paper.
The atomic unit: the Batch and its genealogy. Every material, process, test and release fact hangs off it.
Batch by batch
Four modules on a shared core:
Batch records, equipment, and shop-floor data turned into yield, cycle-time, and right-first-time decisions.
The QC lab, the quality system, and audit-readiness posture as one live picture.
API sourcing, inventory, cold chain, serialisation, and service levels.
Dossiers, price control, complaints, pharmacovigilance, and portfolio performance.
The bottlenecks between last test and release made visible; release-days recovered are working capital and capacity recovered.
Recurring deviation patterns detected across batches, products, and equipment before they repeat.
Batch-to-batch yield variance explained against materials, equipment, and process parameters.
Lab investigations shortened by connecting the test to the batch genealogy instantly.
Data-integrity gaps, overdue CAPAs, and documentation risk surfaced continuously.
iVEDA/DAVA readiness, cold-chain integrity, and service levels in one view.
We assess your batch records, LIMS, QMS, ERP and equipment historians paper and electronic alike.
The pharma ontology is configured for your use case and deployed inside your plant boundary or private cloud, with the audit trail intact.
Batch-release velocity and deviation prevention on one plant. Success metric: release-days saved and preventable-deviation share identified.
Across lines, products and sister sites on an adaptive subscription.
Audit logging designed around data-integrity principles attributable, legible, contemporaneous, original, accurate.
E-signature and controlled-access features can support regulated workflows.
Paper-to-graph first: batch records, logbooks, and certificates are structured into the ontology whatever their format.
Runs inside your plant boundary or private cloud on self-hosted models for supported deployments.
Oral solids, injectables, topicals yield, release velocity, and right-first-time per batch.
Process yield, cost exposure, and quality posture from reactor to dispatch.
Defending client audits and multi-product changeovers on one connected record.
US FDA / EU / WHO-GMP scrutiny met with a live data-integrity posture.
Revised Schedule M makes upgraded GMP mandatory across the industry a modernisation wave the mid-market cannot avoid. US FDA 483s, warning letters and import alerts recur, with data integrity the most-cited theme. Export serialisation and barcode mandates keep expanding.
DPCO/NPPA price caps mean scheduled formulations live and die on cost and yield, while a majority of APIs/KSMs are imported, exposing every batch to input-cost swings.
The plants that connect their batch record, lab, QMS, and ERP into one intelligent model release faster, deviate less, and maintain a clearer audit-readiness posture. That is PharmaIQ.
A scoped PoC on one plant's batch and lab data release-days and preventable deviations quantified, in weeks.
GridIQ fuses your HES, MDMS, billing, SCADA, and scheduling data into one live ontology of your network and turns it into loss, revenue, reliability, and dispatch decisions, down to the distribution transformer and the 15-minute block.
Utilities are investing heavily in smart metering but AT&C losses don't close themselves just because more meters go up.
Many distribution transformers still don't communicate, energy balances are still struck monthly on spreadsheets, and theft is chased on tips rather than data. The meters are going in. The data exists. What's missing is the decision layer. That is GridIQ.
GridIQ is not another dashboard. It is a semantic model that represents your power system as real-world objects meters, transformers, feeders, consumers, generating units, corridors, schedules and the relationships between them, time-stamped to the 15-minute block that governs all Indian scheduling and energy accounting.
One atomic unit: the 15-minute TimeBlock. Every reading, schedule, and deviation hangs off it.
Block by block
Four modules on a shared core:
Turns metering and SCADA data into loss, revenue, reliability, and procurement decisions.
A station-to-grid model centred on availability, fuel, RE curtailment, and dispatch economics.
Grid-operation and network-access: RE evacuation, congestion, stability, asset health.
A cross-cutting layer spanning generation, evacuation, and market participation.
The ontology fuses HES + MDMS + DT-meter + billing data to compute a per-DT energy balance separating recoverable commercial loss from unavoidable technical loss.
Meter events, consumption drops, and DT imbalance correlated into a ranked vigilance target list.
A feeder-segregation model estimates actual agri consumption versus booked, revealing mis-allocated loss.
Eligible consumption tied to subsidy claims and DBT disbursal in one view.
Merit-order and demand-forecast engines optimise drawal and flag avoidable fixed-charge bleed.
Loss-reduction progress evidenced from the network itself, not assembled by hand at reporting time.
We score your HES/MDMS, billing, GIS and SCADA landscape against a structured framework and tell you exactly what can be fused today.
The power-sector ontology is configured for your use case and deployed inside your boundary.
AT&C and commercial-loss detection with DT-level energy balance across one circle. Success metric: loss and theft hotspots quantified as recoverable units, and a ranked target list that utility teams can review and act on.
Circle by circle, division by division, on an adaptive subscription.
Self-hosted LLM layer for supported deployments for narratives and vernacular consumer communication.
Data handling designed around applicable privacy requirements.
Deploys on-premise or in your private cloud.
Entity resolution built for Indian network data: the consumer–DT–feeder graph rebuilds itself live from meter + GIS + billing data.
Loss reduction, revenue protection, billing integrity, RDSS outcome reporting.
Availability, heat rate and dispatch economics.
Congestion, evacuation and grid stability.
Generation forecasting, MAPE-band compliance, curtailment and shortfall analysis.
Irradiance-to-generation variance, performance-ratio tracking, soiling and degradation losses.
Resource variability, wake losses, turbine availability and generation shortfall.
Cycle-life optimisation, round-trip efficiency, state-of-charge management, arbitrage and ancillary revenue.
Co-optimisation of solar, wind and storage against a firm delivery obligation.
DSM exposure, pooling accuracy and market participation.
RDSS has funded the hardware; the outcome targets remain contractual. Utilities are now judged on loss reduction they must evidence, not on equipment they have installed.
Renewable penetration is rising, DSM exposure is real money, and open access is growing, alongside rising consumer expectations on billing accuracy and outage communication.
Every one of these is a data-fusion problem before it is anything else. Better data integration can help utilities identify and address loss more systematically.
A scoped PoC on your own HES/MDMS data recoverable units quantified in one circle, in weeks.
SteelIQ fuses your PLC/SCADA, weighbridge, energy meters, lab, and ERP into one live ontology of your mill and turns it into rupee-per-tonne decisions on charge mix, energy, yield, and quality.
India's secondary route induction furnace, EAF and re-rolling makes up a major share of the country's steel output. Yet most mills still run on shift registers, spreadsheets and the melter's experience.
Meanwhile the pressure is stacking up: power is the single largest conversion cost, the charge mix leans heavily on sponge iron, and carbon-reporting regimes are turning per-tonne carbon intensity into a hard, reportable number.
Energy and charge mix are your two biggest controllable margin levers. SteelIQ captures them with ROI measured in rupees per tonne, not abstractions.
SteelIQ represents your mill as real-world objects heats, ladles, furnaces, mills, coils, bars, energy meters, grades, orders time-stamped to the heat and the shift. The melt shop, rolling mill, energy system, and quality lab stop being four disconnected control rooms and become one connected story.
The atomic units: the Heat and the 15-minute energy block. Every material and energy fact in the mill hangs off one of them.
Heat by heat
Four modules on a shared core:
Furnace, weighbridge, and lab data turned into charge-mix, energy, and metallic-yield decisions.
The reheating-to-rebar flow: yield, TMT properties, mill utilisation, and cobbles.
Your mill as a power buyer and consumer: procurement, kWh/tonne, demand, captive, and renewables.
BIS conformance, yield reconciliation, maintenance, carbon, and plant-wide operations.
The sponge/scrap/pig blend that hits grade chemistry at the lowest cost per heat, updated with live input prices.
Heat-by-heat energy accounting instead of a monthly average, so drift is caught in hours, not billing cycles.
A live mass balance from weighbridge to dispatch that shows where tonnes disappear.
Chemistry, rolling parameters, and mechanical results in one traceable chain per heat (IS 1786: Fe-415 to Fe-600).
Exchange purchase, captive, and demand management run against actual heat schedules.
A CBAM- and Green-Steel-Taxonomy-ready number, computed from your own data, not an industry default.
We assess your PLC/SCADA, weighbridge, meters, spectrometer/lab and ERP.
The steel ontology is configured for your use case and deployed on an air-gapped edge box that keeps working through patchy connectivity.
Charge-mix, specific-energy and yield optimisation on one melt-shop line. Success metric: ₹/tonne saved on power and yield, computed on your own furnace data.
Across lines and sister units on an adaptive subscription optionally with performance-linked commercial terms.
Supports edge and restricted-connectivity deployment scenarios.
Self-hosted AI models for supported deployment environments
Rupee-per-tonne native: every insight expressed in the language mills use.
Built for the induction-furnace and EAF reality, not repurposed from integrated-steel software.
Built around the process-plant requirements of heavy industry.
Charge-mix, specific energy, and yield control, heat by heat.
TMT, structures, and wire rod yield, BIS conformance, and mill utilisation.
DRI → melt → roll, modelled as one connected flow.
Multiple units, one shared ontology, and a comparable ₹/tonne view.
Power dominates conversion cost in the IF route, and specific energy typically runs 600–750 kWh per tonne of liquid steel. A few percent of drift, multiplied across a year's tonnage, is a serious number and today it hides in monthly averages.
The EU's CBAM regime and India's Green Steel Taxonomy have made carbon intensity a graded, reportable attribute. Buyers will ask for your number; SteelIQ computes it heat by heat.
The mills that can measure will out-compete the mills that estimate.
A scoped PoC on one melt-shop line ₹/tonne savings quantified on your own furnace data, in weeks.
KilnIQ fuses your DCS/PLC historians, XRF/XRD quality lab, energy meters, ERP, and dispatch systems into one live ontology of your plant and turns it into decisions on kiln heat, clinker quality, specific power, alternative fuels, carbon intensity, and logistics cost.
Fuel, power, and logistics make up the bulk of your cost. Cement is one of India's most carbon-intensive sectors just as PAT targets tighten, the Carbon Credit Trading Scheme goes live, and CBAM starts pricing carbon at the EU border.
And yet: most plants run the kiln on operator intuition and a DCS, compute heat and power balances monthly in spreadsheets, and cannot say which practice, fuel, or raw-mix is costing them the most kiln-hour by kiln-hour.
The prizes are large and quantifiable. KilnIQ captures them.
KilnIQ represents your plant as real-world objects raw-mix, kiln feed, clinker, cement lots, mills, kilns, coolers, meters, silos, shipments time-stamped to the kiln-hour and the production lot. The raw mill, the pyro line, the cement mill, the energy team, and the dispatch yard become one connected model instead of a DCS island and a wall of spreadsheets.
The atomic units: the kiln-hour and the production lot. Every heat, quality, energy and carbon fact hangs off them.
Kiln-hour by kiln-hour
Four modules on a shared core:
Quarry, raw-mill, kiln, and cooler data turned into heat, stability, and clinker-quality decisions.
The cement mill, the quality lab, and the product mix as one live picture.
Specific heat, specific power, alternative fuels, and your carbon posture as one connected model.
The plant as a cost-to-serve system: procurement, logistics, dispatch, and market.
A live heat balance instead of a monthly spreadsheet; drift caught in hours, attributed to fuel, feed, and practice.
Alternative-fuel substitution optimised against heat, quality, and availability most plants have real headroom above where they run today.
Mill by mill, product by product, shift by shift.
Blended-cement economics computed against quality constraints and market demand.
A CCTS-, PAT-, and CBAM-ready number computed from your own operating data.
Freight per tonne, dispatch-mix, and market-serve decisions on the second-largest cost head.
We assess your DCS/PLC historians, quality lab, meters, ERP and dispatch systems.
The cement ontology is configured for your use case, available as an air-gapped edge deployment for remote plants.
Kiln specific-heat and TSR optimisation at one plant one agreed number your energy head and CFO both recognise.
Across kilns, mills and sister plants on an adaptive subscription.
Runs inside your plant boundary; air-gapped edge box for remote sites.
Self-hosted AI models for supported deployment environments
Built around process-plant operating requirements.
Carbon accounting designed to support PAT, CCTS and CBAM-related reporting requirements, from your own data.
Kiln heat, TSR, clinker quality, and carbon kiln-hour by kiln-hour.
Specific power, clinker factor, and product-mix economics per lot.
Practice standardised across sites on one comparable model.
CBAM carbon accounting at the EU border, computed from your own data.
India is among the world's largest cement producers, and the sector is a meaningfully carbon-intensive one. Three regimes now converge on that number: PAT (cement is a designated-consumer sector under BEE's Perform-Achieve-Trade scheme), CCTS (India's Carbon Credit Trading Scheme brings intensity targets and tradable credits), and CBAM (EU carbon accounting at the border for exporters).
Plants that can compute heat, power and carbon kiln-hour by kiln-hour will treat these regimes as an opportunity supporting carbon reporting, export requirements and operational cost analysis. Plants that compute them monthly in spreadsheets will treat them as a tax.
Plants that compute heat, power, and carbon kiln-hour by kiln-hour treat these regimes as an opportunity. Plants that compute them monthly in spreadsheets treat them as a tax.
A scoped PoC on one plant's DCS and meter data heat, TSR and power savings quantified, in weeks.
ReactIQ fuses your batch records, DCS/SCADA, QC lab (LIMS/CDS), effluent and emissions monitoring, ERP, and safety systems into one live ontology of your plant and turns it into decisions on reaction yield, right-first-time, deviation prevention, effluent load, process safety, and margin.
Indian specialty and agro-chemicals are growing as global supply chains shift. The operating reality on the ground is pressure from every direction at once.
Yield and right-first-time decide margin on price-sensitive molecules. Effluent and ZLD compliance are a hard, expensive constraint. Process-safety scrutiny has risen sharply after high-profile incidents. Export access runs through REACH and buyer audits.
And most mid-market plants run all of it on batch sheets, a DCS, a LIMS that talks to no one, and effluent logs reviewed after the fact. The prizes are large and measurable: a few points of reaction yield, a preventable batch failure, a solvent recovered, an effluent excursion avoided. ReactIQ captures them.
ReactIQ represents your plant as real-world objects batches, reactions, materials, reactors, tests, deviations, effluent streams, shipments time-stamped to the batch and the event. Process operations, the QC lab, the effluent plant, and the supply chain become one connected model instead of four systems and a stack of batch sheets.
The atomic unit: the Batch / Reaction lot and its genealogy. Every material, process, test, effluent, and cost fact hangs off it.
Reaction by reaction
Four modules on a shared core:
Batch records, DCS, and shop-floor data turned into yield, cycle-time, and right-first-time decisions.
The QC lab, the specifications, and the release posture as one live picture.
The effluent plant, emissions, hazardous waste, and process safety as one live picture.
Raw-material sourcing, registrations, market access, and portfolio performance.
Batch-to-batch yield variance explained against raw-material lots, reactor conditions, and operating practice.
Recurring failure patterns detected across batches, products, and reactors before they repeat.
Effluent generation tied back to the specific batches, reactions, and practices that produced it so ZLD cost is managed at the source, not just treated at the pipe.
Near-misses, interlock events, and reactive-chemistry risk connected to the operations that produced them.
A live material balance that shows where kilograms and rupees disappear.
Documentation posture and market-access status maintained continuously.
We assess your DCS/SCADA, LIMS/CDS, batch records, ETP monitoring and ERP.
The chemicals ontology is configured for your use case and deployed inside your plant boundary.
Reaction-yield and effluent-load optimisation at one plant one agreed number your plant head and CFO both feel.
Across blocks, products and sister plants on an adaptive subscription.
Supports on-premise deployment: reaction know-how and recipes can remain within your chosen boundary.
Self-hosted AI models for supported deployment environments
Environment- and safety-native: effluent, emissions, hazardous waste, and process-safety signals are first-class objects, not afterthoughts.
Built around the batch-manufacturing requirements of regulated industry.
Batch and semi-batch yield, right-first-time, and effluent per reaction lot.
Margin defended molecule by molecule, season by season.
CSM/CDMO client audits and multi-product changeovers on one record.
REACH and buyer audits met with a continuously maintained posture.
On price-sensitive molecules, yield, right-first-time, deviations and effluent cost decide margin batch by batch and today those numbers live in four systems that never reconcile. CPCB and state boards are pushing Zero-Liquid-Discharge and tight effluent norms across chemical clusters; managing at the source requires knowing which batch produced which load.
Post-incident scrutiny has made process safety and reactive-chemistry control a board-level topic. EU REACH and product registrations gate exports; PLI and bulk-drug policies reward documented, modern manufacturing.
Every one of these is, at its core, the same problem: disconnected data that no one has fused into decisions. ReactIQ is the fusion layer.
A scoped PoC on one plant's DCS, LIMS and ETP data yield lift and effluent reduction quantified, in weeks.
Effective from 2 June 2026, the date of incorporation.
This Privacy Policy explains how THANISH AI INNOVATIONS (OPC) PRIVATE LIMITED ("Thanish AI Innovations", "we", "us", "our") handles personal data collected through ai-innovationsthanish.in (the "Website"). It does not cover data processed on behalf of a customer as part of a specific deployment or Proof of Concept that processing is governed by the data-processing terms in the applicable service agreement with that customer.
THANISH AI INNOVATIONS (OPC) PRIVATE LIMITED, C/o R Sathyaprema, Sri Lakshmi Venkateshwar, Krishnarajapuram, Bangalore, Bangalore North, Karnataka, India, 560036. We can be reached at contact@ai-innovationsthanish.in.
When you use a form on this Website for example, to request a Proof of Concept discussion or a partnership conversation we collect what you submit: typically your name, email address, organisation, sector, and the contents of your message.
Like most websites, our hosting and any analytics tools in use may automatically log technical information such as IP address, browser and device type, and the pages you visit, for the purpose of operating and securing the Website.
To respond to your enquiry, scope a Proof of Concept or partnership discussion, operate and improve the Website, and meet our legal and accounting obligations. We do not use the information you submit here to make automated decisions about you.
The Website uses only the cookies or similar technologies strictly necessary for it to function correctly. If this changes for example, if we introduce analytics or marketing cookies we will update this section and, where required, seek your consent.
We do not sell personal data. We may share it with service providers who support our website or business operations (for example, hosting or email providers) under confidentiality obligations, or where required by law or a valid legal process.
We keep personal data only for as long as reasonably necessary for the purpose it was collected, or as required by applicable law, after which it is deleted or anonymised.
Subject to applicable law, you may ask us to confirm what personal data we hold about you, correct it, or delete it, and you may raise a grievance about how we have handled it. See our Data Protection & DPDP Notice for how to exercise these rights.
We take reasonable technical and organisational measures to protect the information you share with us. See our Security Statement for more detail.
We may update this policy from time to time. The "Last updated" date at the top will always reflect the most recent revision.
Questions about this policy can be sent to contact@ai-innovationsthanish.in.
Effective from 2 June 2026, the date of incorporation.
This notice supplements our Privacy Policy and sets out how THANISH AI INNOVATIONS (OPC) PRIVATE LIMITED approaches personal data in connection with India's Digital Personal Data Protection Act, 2023 ("DPDP Act").
This notice covers personal data we process as a data fiduciary in our own right for example, data submitted through this Website. Where we process data on a customer's behalf as part of a Proof of Concept or deployment, the customer is generally the data fiduciary for that data, and our processing is governed by the data-processing terms agreed in that engagement.
Contact and identification details you provide (name, email, organisation), the content of your enquiry, and technical data generated by your use of the Website (such as IP address and browsing activity on our pages).
We process personal data only for the purpose it was shared principally to respond to enquiries and operate the Website and we seek consent where the DPDP Act requires it.
You may request access to your personal data, ask us to correct or update it, ask us to erase it, nominate another person to exercise your rights in the event of death or incapacity, and register a grievance about how we have handled your data. Write to our Grievance Officer below to exercise any of these.
Personal data is retained only as long as necessary for the purpose it was collected, or as required by law, and is deleted or anonymised thereafter.
Where a specific engagement involves on-premise, private-cloud, self-hosted, or air-gapped deployment, the applicable data-handling and localisation arrangements including whether data leaves a particular boundary are set out in the agreement for that engagement, not in this general notice.
We maintain reasonable security safeguards appropriate to the data we hold. See our Security Statement.
Manish Kumar Atal, Business Executive, THANISH AI INNOVATIONS (OPC) PRIVATE LIMITED, C/o R Sathyaprema, Sri Lakshmi Venkateshwar, Krishnarajapuram, Bangalore, Bangalore North, Karnataka, India, 560036. Email: contact@ai-innovationsthanish.in. We aim to acknowledge grievances within a reasonable time and resolve them as required under applicable law.
We will revise this notice as our practices or the applicable law evolve, and update the date at the top accordingly.
Effective from 2 June 2026, the date of incorporation.
These Terms of Access govern your use of ai-innovationsthanish.in, operated by THANISH AI INNOVATIONS (OPC) PRIVATE LIMITED. By using this Website, you agree to these terms.
This Website is an informational site describing Thanish AI Innovations, its platform, and its industry solutions. It is intended to inform and to start a conversation it is not itself a software product, order portal, or a binding offer of any specific outcome.
You may browse and use this Website for lawful, personal, or legitimate business purposes, such as learning about our solutions or requesting a conversation with us.
You agree not to attempt unauthorised access to this Website or its underlying systems, to scrape or automatically harvest content at scale, or to interfere with or disrupt the Website's operation.
The Website's content, design, and the THANISH AI INNOVATIONS name and marks belong to us or our licensors, unless stated otherwise, and may not be copied or reused without permission.
Content on this Website including descriptions of our platform, solutions, and any figures or examples is provided for general information. We take care to keep it accurate, but we do not warrant that it is complete, current, or error-free, and it should not be relied on as a substitute for a scoped commercial or technical discussion with us.
To the extent permitted by law, Thanish AI Innovations is not liable for any loss or damage arising from your use of, or inability to use, this Website.
Where this Website links to third-party sites, we do not control and are not responsible for their content or practices.
We may update these terms from time to time; continued use of the Website after an update constitutes acceptance of the revised terms.
These terms are governed by the laws of India, and the courts at Bengaluru, Karnataka shall have exclusive jurisdiction over any dispute arising from them.
Questions about these terms can be sent to contact@ai-innovationsthanish.in.
Effective from 2 June 2026, the date of incorporation.
Security is treated as a design requirement, not an afterthought, both on this Website and in how we approach customer deployments. This statement describes our general approach; the specific controls for any engagement are scoped and documented in that engagement's agreement.
This Website is served over an encrypted (HTTPS/TLS) connection, and we rely on our hosting provider's infrastructure safeguards alongside standard web-security hardening practices.
For customer engagements, we support a range of deployment options including on-premise, private-cloud, self-hosted, and air-gapped edge deployment where a site's connectivity requires it designed around each customer's own data and security requirements.
Where the platform is deployed, we support role-based access controls and audit logging, and for regulated environments such as hospitals or pharmaceutical plants the platform can support consent, redaction and controlled data access ahead of any model processing.
Self-hosted AI models can be used for supported deployments, depending on a customer's infrastructure requirements, so that model inference can run within the customer's own environment rather than an external API.
If you believe you have found a security issue affecting this Website or our systems, please report it to contact@ai-innovationsthanish.in with enough detail for us to reproduce it. We will acknowledge genuine reports and work to address them; please give us a reasonable opportunity to investigate before disclosing an issue publicly.
In the event of a security incident affecting a customer's data, we will notify the affected customer and take the steps required under that engagement's agreement and applicable law.
As a newly incorporated company, our security practices continue to mature alongside our platform and our customer base. Specific certifications or audit outcomes, where they exist for a given engagement, are documented in that engagement's own materials rather than claimed generally here.
Security-related questions can be sent to contact@ai-innovationsthanish.in.
Effective from 2 June 2026, the date of incorporation.
Thanish AI Innovations builds AI-based decision layers for industries where the underlying decisions clinical, safety, financial, environmental carry real consequences. These principles describe how we try to build and deploy that technology responsibly.
Our platform is designed to surface information, risk signals, and recommendations for qualified people clinicians, plant operators, compliance teams to act on. It is not designed to replace their judgement in matters that affect health, safety, or regulatory compliance.
Where we describe what a solution can do, we aim to describe it accurately rather than as a guaranteed or validated outcome. Any specific performance figures are tied to a scoped Proof of Concept on a customer's own data, not to a general claim.
We aim to use only the data needed for a given decision or module, and, in regulated environments, to support consent and redaction ahead of model processing where the platform is deployed for that purpose.
Models are tuned and evaluated against a customer's own data during the Proof of Concept and deployment phases before they are relied on operationally, and their outputs remain subject to ongoing review rather than a one-time sign-off.
We aim to be clear with customers about what the ontology and decision layer are doing with their data and how a given recommendation was arrived at, rather than presenting it as an unexplainable black box.
We aim to identify and address ways a model's outputs could systematically disadvantage a group of people, and we welcome customers and users raising concerns of this kind with us.
Questions, concerns, or feedback about how our AI systems behave can be sent to contact@ai-innovationsthanish.in, and will be reviewed by our team.
As our platform, customer base, and the regulatory landscape evolve, we expect to revise this statement, and will update the date at the top when we do.
Effective from 2 June 2026, the date of incorporation.
This policy describes how to reach us and what to expect, whether you are exploring a Proof of Concept, in an active engagement, or already a customer.
Every conversation starts at contact@ai-innovationsthanish.in, whether you are asking about a Proof of Concept, a partnership, or support on an active engagement.
Tell us your sector, the metric you want moved, and the systems that hold the data. We aim to acknowledge new enquiries within 2 business days.
Support scope, response expectations, and any support hours for a specific deployment are defined in that engagement's service agreement, since they depend on the solution deployed and the customer's own operating environment.
If a matter is not resolved to your satisfaction, you may ask for it to be escalated to Manish Kumar Atal, Business Executive, and, where needed, to Reddappa Naveen, Founder.
We welcome feedback on our platform, our solutions, and this Website at the same address above.
We will update this policy as our support processes mature, and will update the date at the top when we do.