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Also serving Kochi and Cochin
Off-the-shelf AI knows nothing about pepper grades, prawn yields or a biotech lab's assay records; custom AI development learns from the data an Ernakulam business already holds.
Or call +91 62358 66111
Photo: Gautham Krishna S · Pexels License · via Pexels
Trusted by 200+ organisations across India & overseas — including
Spice trade and exports
Price and demand forecasting from past lots and shipments, automatic reading of quality certificates and buyer specifications, and matching of incoming enquiries to available stock by grade and origin for export houses registered with the Spices Board.
Seafood processing and export
Yield prediction by species, size grade and season from past landings at the harbour, models that flag a raw material lot likely to underperform, and document extraction from health certificates and buyer contracts.
Biotech and hi-tech startups
Search and summarisation across assay results, lab notebooks and regulatory filings for the incubated companies at Kalamassery, with access controls that keep each project's data separate.
Ernakulam's older trades sit on years of data nobody uses. A spice exporter in Mattancherry has records of every lot, price and buyer complaint. A seafood plant at Thoppumpady has yield figures for every boatload. A biotech startup at Kalamassery has assay results piling up in folders. General-purpose AI tools cannot see any of it.
As an AI development company in Kochi, we build models and applications around your own data: forecasting, classification, document understanding, search across internal files and assistants grounded in your records. Work starts with a narrow, provable use case. We measure it against how the job is done today, and scale only what earns its keep.
Case studies
BSS Green Life Solar Division
BSS Green Life Solar — ERPNext, CRM & a Geo-Tagged Site-Survey App
Connect N Cabs Tours & Travels Pvt. Ltd.
Connect N Cabs — A Custom Travel ERP That Lifted Fleet Utilisation 35%
Della Fashions
Della Fashions — ERPNext POS Made Checkout 60% Faster Across Every Outlet
Many firms have seen an AI demo that impressed in a meeting and then went nowhere. The missing piece is usually engineering: clean data pipelines, integration with the systems staff use, and monitoring. We build for production from the first sprint, not for a slide.
A model that invents a figure is worse than no model. We ground responses in your own documents and records, show the source beside each answer, and keep a person in the loop for any decision that carries money or safety risk.
The useful data sits in exports, spreadsheets, scanned certificates and inboxes. A large part of any AI project is the unglamorous work of collecting, cleaning and structuring it. We scope that honestly at the start.
An example
A spice house in Mattancherry receives a lot of black pepper from an upcountry agent on a Wednesday. The quality clerk enters moisture, bulk density and foreign matter, and an assistant trained on the firm's own lot history suggests a grade and a price band, showing the three past lots it is comparing against. It also flags that this agent's last two lots were regraded after arrival. The clerk agrees and lowers the price band. By afternoon an enquiry arrives from a European buyer; the assistant matches it to available lots by grade and origin and drafts the offer, which the export manager checks and sends.
An AI engagement with an Ernakulam business opens with a data audit at your premises: what exists, where, in what shape, and who may see it. From our Kochi office we then build a proof of concept on a sample, review results with the people who know the work, and decide together whether to proceed. Models can run in your own cloud account or on your servers.
Discovery and feasibility. Use cases ranked by payback and data readiness: a plan, not a pitch.
Pilot on real data. The first use case, measured for accuracy and time saved against your manual baseline.
Human review loop. Your team validates outputs while we tune to your threshold.
Production hardening. Audit logs, fallback paths, monitoring and access controls.
Scale and integrate. Rolled into your web and mobile apps and your dashboards.
The scope and timeline are agreed in writing before work starts.
Often more than you think, though rarely in a usable form. A few years of transactions, documents or call records is a workable start. The data audit tells you what is feasible; if the data cannot support a use case, we say so before any build begins.
A public chatbot answers from general knowledge and has no access to your records. A custom solution is connected to your data, follows your rules about who may see what, cites its sources and can be built into the software your staff already use, including ERPNext.
That is agreed in writing at the start. Options include your own cloud account, servers on your premises, or a managed environment with contractual limits on data use. For lot and buyer data with confidentiality attached, we default to the strictest option that still meets the project's needs.
After a first conversation about the use case, the data behind it and the accuracy you need, we agree the scope and send you a written quotation. The price, the deliverables and the timeline are fixed against that scope before any work starts, so nothing grows without your sign-off.
You speak to someone who does this work, not a call centre. They ask about the decision or task you want AI to handle, the data behind it and the result you need. If it looks like a fit, we agree the scope and put it in writing, with the timeline, before any work starts. If it does not, we tell you.
Spices Board of India, Kochiinstitution
central export-promotion and regulatory body for Indian spices, headquartered in Kochi
With the spice export regulator in the city, Kochi's export houses hold decades of lot, grade and price records that are exactly the training data a forecasting or grading model needs.
Source: en.wikipedia.orgCochin Fisheries Harbour, Thoppumpadymarket
the district's main fish landing centre and one of the state's biggest fishing harbours, supplying local markets and seafood exporters
The district's main fish landing centre, whose processors keep yield records by species, size and season that a model can turn into a prediction before a lot is bought.
Source: en.wikipedia.orgKINFRA Hi-Tech Park, Kalamasserypark
243-acre KINFRA park near HMT Colony for non-polluting hi-tech industry, with a biotechnology incubation centre, IT/ITES and electronics SEZ space and a gem and jewellery park; about 34 units.
A park with a biotechnology incubation centre and electronics and IT space, where young firms generate lab and product data faster than they can search it.
Source: kinfra.orgPlaces and organisations are named for local context only. Acube Innovations is not affiliated with or endorsed by them.
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Kochi (Kadavanthra & Infopark) · Thiruvananthapuram · across India & overseas · In business since 2011