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Also serving Trichur
A Thrissur lender has decades of repayment history and a jeweller has years of design-wise sales; custom AI turns that private data into decisions no public tool can make.
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Photo: Jpullokaran · CC BY-SA 3.0, cropped · via Wikimedia Commons
Trusted by 200+ organisations across India & overseas — including
Banking and financial services
Risk scoring on your own repayment history, document extraction for KYC and collateral papers, and a policy assistant for branch staff, each validated against past cases before it influences a live decision.
Gold and jewellery manufacturing and retail
Demand forecasts by design family and branch, identification of slow-moving pieces that should be melted or moved, and customer segments for scheme renewals, all drawn from billing and stock data you already hold.
Tiles and ceramics
Dealer order prediction for the works at Pudukad and Amballur so the yard holds the right sizes ahead of the building season, and reorder suggestions that allow for supplier lead times from Gujarat and beyond.
The valuable data in Thrissur is not on the internet. It is in the repayment records of finance companies that have lent against gold for generations, in the design-by-design sales of the jewellery houses around the Round, in the dealer order books of tile makers along the Ollur belt. General-purpose AI tools know nothing about any of it, so their answers about your business are confident and useless.
AI development at Acube means building models and assistants on your own data: forecasting, classification, document understanding, and retrieval-based assistants that answer from your manuals and records with the source cited. We build narrow, measurable systems first, keep a human reviewer in the loop, and deploy inside infrastructure you control.
Case studies
Muthoot Securities Limited
Muthoot Securities — MScan, the Flutter App That Cut Document Search Time by 70%
Queue Off
Queue Off — Digital Payments That Work on a Feature Phone, With No Internet
Adventure Sports Cover 360 (ASC360)
ASC360 — ERPNext + Mobile App Cut Policy Issuance Time 70%
Which designs to stock before the wedding season is usually decided by the senior partner's feel. A forecasting model trained on past sales by category, weight range and branch gives the purchase team a second opinion grounded in numbers, not a replacement for judgement.
A finance company's circulars, credit policies and audit guidelines run to thousands of pages. A retrieval assistant lets a branch manager ask a question in plain words and get the relevant clause with its document reference, instead of phoning head office.
Accounts slide into overdue in patterns that are visible in hindsight. A model that scores accounts on repayment behaviour flags the ones worth a call this week, so collection staff spend time where it changes the outcome.
An example
On a Thursday in early summer, the purchasing partners of a jewellery house with showrooms in Thrissur and Chalakudy meet to plan stock for the wedding season. Beside the usual instinct there is a forecast on the screen, built from several years of the group's own billing: sales by design family, weight band and showroom, adjusted for the festival calendar. It suggests more lightweight chains for the Chalakudy branch than last year and fewer heavy sets for the town showroom, and lists the pieces that have sat unsold since the previous season. The senior partner disagrees on one line and overrides it; the system records the override. The purchase list goes to the workshop and to outside goldsmiths as job-work orders. When the season ends, the same screen shows what actually sold, and the model learns from where it was wrong.
We begin with a data review: what you have, how clean it is, and whether the question you want answered can be answered from it. For a Thrissur client that conversation happens face to face. The model build, evaluation and deployment are handled by our engineers, with results shown at each stage against an agreed baseline.
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, yes. A few years of clean billing, stock or repayment records is enough for many forecasting and classification tasks. Where data is thin, we say so early and may suggest fixing data capture first, rather than deliver a model that guesses.
We favour approaches that can be explained: which factors drove a score, which document a reply came from. For regulated work we keep records of model versions, training data ranges and test results, so a reviewer can follow how an output was produced.
It does not need to. Development can happen in your own cloud account or on an isolated environment with access limited to named engineers under a confidentiality agreement. We set out the data flow in writing before work starts.
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.
Gold Ornaments Industry Cluster, Thrissurcluster
DC-MSME cluster taken up in 2004-05 through MSME-DI Thrissur; the district's jewellery workshops are the backbone of Kerala's plain-gold manufacturing trade
The cluster's workshops hold years of job-work, design and sales records, the private, structured history a demand or wastage model needs.
Source: dcmsme.gov.inTile belt at Ollur, Pudukad, Karuvannur and Amballurmarket
century-old clay roofing-tile factories built on local clay deposits
Tile works here sell through dealers across central Kerala with strong seasonality, so order prediction and yard-stock planning are natural first models.
Source: thrissur.nic.inInfopark Thrissur, Korattypark
Government of Kerala IT campus at Koratty near Chalakudy with an SEZ building (Indeevaram, about 3.35 lakh sq ft) and a set of smaller non-SEZ buildings; runs as a spoke of Infopark Kochi
Software firms on the Koratty campus often need an AI component built into a product they already ship, which suits a scoped development engagement.
Source: infopark.inPlaces 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