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Also serving Vizag
Visakhapatnam sits on years of cargo records, production logs and customer files; custom AI is how a firm starts getting answers from them.
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Photo: Prof Ranga Sai · CC0 · via Wikimedia Commons
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In your environment, under your access controls. We design so that confidential records are not sent for public model training, and the arrangement is documented. For Vizag firms with overseas clients or regulated data, this is settled before development begins.
We test on your own historical data and compare with how your staff perform the same task today. You see the honest error rate before go-live. Uncertain cases route to a person, and accuracy continues to be monitored after launch.
Only for the right problem. If a task is high in volume, rule-bound and currently eats skilled hours, a narrow AI system can pay back. If the data is thin or the process keeps changing, we will say so and suggest ordinary automation instead.
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.
Public AI tools impress in a demonstration and disappoint at work. They know nothing about your vessels, your grades of steel or your loan customers, and pasting confidential documents into them is a risk no compliance officer in Vizag's fintech firms would sign off. What businesses here actually need is smaller and more useful: a model that knows their own data and sits inside their own systems.
We build that kind of AI as an engineering project. Typical work includes document understanding, search and question answering across internal files, demand and cash forecasting from transaction history, and AI features inside web or mobile products. Each system is grounded in your data, tested against your manual baseline, and connected to ERPNext, a CRM or an app.
Case studies
EVM Wheels (part of the EVM Group)
EVM Wheels — 70% of Self-Drive Rentals Now Book Themselves Through the App
Kera Cabs
Kera Cabs — 55% of Bookings Moved Off the Phone and Into the App
Kerala State Road Transport Corporation (KSRTC)
KSRTC — ERPNext Courier System Delivered ₹1 Crore in Profit
Specifications, contracts and standard procedures pile up over decades in marine engineering firms around Hindustan Shipyard. Finding the clause or the drawing note takes hours. A retrieval system built on your own archive answers in plain language and cites the source.
Distributors and processors plan purchases on last year's average plus instinct. With several years of sales history in the ERP, a forecasting model can account for festival and shutdown cycles, and show its error rate openly so planners know how far to trust it.
Software firms at Madhurawada are asked by their own customers for AI inside the product, and the team has never shipped one to production. We build the feature with them, including evaluation, monitoring and the dull safeguards that keep it reliable.
An example
A marine engineering subcontractor that has worked on ships in the harbour for decades keeps its history in a records room: repair reports, class surveyor notes, vendor manuals, drawings with pencilled corrections. A planning engineer preparing a quotation for a stern tube job types a question into the internal search built on that archive. It returns the three previous jobs of that type, the surveyor's remarks on each, and the manual page that specifies the clearance, with the source file named for every answer. He checks the sources, adjusts the estimate and sends the quotation before lunch. Nothing has left the company's own server.
We run AI development for Visakhapatnam clients from Kochi, starting with a discovery stage on a sample of your real data. That stage ends with a written finding: what is feasible, what accuracy to expect and what it needs from you. Builds are then reviewed fortnightly over calls, and we travel for launch and training where users are on a shop floor.
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.
Shipbuilding and marine engineering
Search across drawings, class requirements, repair histories and vendor manuals, so an engineer planning a dry-dock job can ask a question and get the relevant pages instead of walking to the records room.
IT, ITeS and fintech
AI features for fintech products such as document verification in onboarding, transaction categorisation or support assistants, built with audit logs and human review for any decision that carries money or regulatory weight.
Pharmaceuticals
Extraction of data from certificates of analysis and supplier documents at units in the Duvvada SEZ, with a qualified person approving every output before it enters a controlled record.
IT and software services, Rushikonda and Madhurawadacluster
The city article records more than 350 IT firms and turnover of about Rs 5,400 crore in 2016-17, centred on the APIIC IT SEZ hills; do not name individual companies on the page.
The city's software cluster is where clients now ask for AI inside a product and where the buyer can tell a real evaluation from a demo, so the engineering bar is set here.
Source: en.wikipedia.orgHindustan Shipyardpark
Government-owned shipyard under the Ministry of Defence on about 46 hectares; India's second-largest, building vessels up to 80,000 DWT and overhauling naval submarines.
A shipyard that overhauls submarines and builds large vessels means a ring of engineering firms holding decades of technical records that nobody can search quickly.
Source: en.wikipedia.orgIndian Institute of Petroleum and Energyinstitution
central institute for petroleum and energy engineering
A central institute for petroleum and energy engineering keeps a local pool of graduates comfortable with sensor data and models, which energy-sector firms here can draw on for maintenance analytics.
Source: en.wikipedia.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