Straight answers on data, AI and analytics

Conversational BI is business intelligence you use by asking questions in plain language, such as "Which branch sold the most last month?", and getting charts and numbers back from your live data. It removes the need to build a new report or learn a query tool for every question.

Read more: Conversational AI Vs Static Reports

Traditional BI tools show dashboards that someone designs in advance, and new questions usually need an analyst to build more views. Conversational BI lets any authorised user ask a new question directly. Many businesses use both: dashboards for routine tracking, and conversation for follow-up questions.

Read more: Conversational AI Vs Static Reports

The cost depends on the number of users, how many data sources you connect, how much setup and customisation you need, and where the platform is hosted. Vyakhyan is built with SME-focused pricing, so contact Navaantrix with your data sources and team size for a quote that fits.

Read more: The Data Challenge Holding SMEs Back

OEE (Overall Equipment Effectiveness) shows how much of your planned production time is truly productive. It equals Availability × Performance × Quality. For example, 90% availability, 95% performance and 98% quality give an OEE of about 83.8%.

Read more: Reducing Manufacturing Downtime with Real-Time Data

Measure it first: record when each important machine stops and why, rank the causes by total hours lost, and fix the biggest ones. Automatic data from sensors or machine signals is more accurate than paper logs, and real-time alerts let teams respond within minutes.

Read more: Reducing Manufacturing Downtime with Real-Time Data

Core measures are OEE, downtime and its causes, production output against plan, scrap and rework rate, and energy use per unit. Add on-time delivery and stock levels once the shopfloor numbers are reliable.

Read more: Unlocking the Power of Manufacturing Analytics

Useful measures include sales by store and product, gross margin, stock turnover, average basket size and demand trends over time. Comparing the same measures across stores and periods shows where to act.

Read more: Discover Smarter KPI Tracking with Vyakhyan's Interactive Dashboard

Predictive maintenance uses sensor data such as temperature, vibration and power use to spot abnormal patterns and estimate when equipment is likely to fail, so repairs happen before a breakdown. It needs a history of reliable machine data to learn what normal looks like.

Read more: Revolutionizing EOT Crane Monitoring

Yes, with a conversational BI platform. It connects to your ERP or POS system, understands a question written in everyday language and returns the answer as a chart or number, which you can drill into. Vyakhyan is built for this.

Read more: AI-Powered Answers From Your Business Data

Reports are slow when data sits in several systems and every question needs someone to assemble it by hand. Connecting the sources, agreeing on the key measures and letting managers query the data directly removes most of the waiting.

Read more: The hidden cost of slow data

Pick one problem that costs you money, such as slow reporting or machine downtime. Gather the data behind it, agree on two or three measures, and review them regularly with your team. Prove value on that one project before you expand.

Read more: The Data Challenge Holding SMEs Back

A first working view can often be ready within weeks when the data is accessible and clean. Projects take longer when data is scattered, inconsistent or needs custom connections, so a short data review at the start gives a realistic timeline.

Read more: Why Your Business Has the Data But Not the Answers

It depends on how the platform is built and hosted. Look for role-based access so people only see what they are allowed to, and ask the vendor where your data is processed and stored. Navaantrix can explain how this works for your deployment.

Read more: Beyond Dashboards: Monetize Your Data Through Conversation with Vyakhyan

Data Science as a Service means getting data processing, analysis, visualisation and machine learning work from an outside team instead of hiring a full in-house one. It suits businesses that need these skills but do not have the budget or constant workload to justify hiring.

Read more: Data Science as a Service (DSaaS)

RAG lets an AI model look up information from your own documents and data before it answers, so responses reflect your business and can be traced to a source. It is a common way to use AI on private company knowledge.

Read more: AI and GPT Trends of 2025

AI models analyse images of the road surface, including photos taken with smartphones, to detect and classify defects such as cracks and potholes. This covers more road faster than manual surveys and keeps a record for planning repairs.

Read more: From Boots on the Ground to Brains in the Cloud: The AI Revolution in Road Monitoring

Navaantrix's analytics platform is designed for manufacturing, retail (multi-store and multi-brand), wholesale, trading and distribution, IoT and Industry 4.0, and logistics. It has been tested or deployed in areas that include manufacturing analytics, retail, healthcare and toll and road operations.

Read more: The Story Behind Vyakhyan: From POC to Product

Yes. Navaantrix is based in Baner, Pune, and builds software solutions on data science, AI, machine learning and business intelligence. Get in touch to discuss your project and how the team can work with you.

Read more: The Story Behind Vyakhyan: From POC to Product

A static report shows a fixed set of numbers that someone prepared in advance. Conversational AI lets you ask a new question in plain language and get a fresh answer from your live data, so you are not limited to what the report builder anticipated.

Every new question usually means asking an analyst to build or change a report, which can take days. By the time the answer arrives, the decision has often already been made without it.

No. Many teams keep their regular reports for routine tracking and add a conversational layer for the follow-up questions that reports cannot answer.

Yes. That is the main point of it: managers can ask questions the way they would ask a colleague, without learning query tools or waiting for an expert to interpret a report.

It means turning data you already collect into decisions and insights that save cost or raise revenue, by making it easy for people to ask questions of it. Data that nobody can easily query rarely produces value.

Dashboards answer the questions they were designed for. When a manager needs to dig into a new question, a dashboard cannot follow, and the data stays unused.

Vyakhyan is built to connect to existing business systems such as ERP, POS and IoT data, along with databases and documents. Confirm the exact list for your setup with the Navaantrix team.

Access is controlled by user roles, so each person sees only the data they are permitted to see, even when everyone asks questions in the same way.

The AI connects to your data sources, interprets a question written in everyday language, finds the relevant numbers and returns an answer with a chart or table. You can then drill into the result with follow-up questions.

Answers are only as reliable as the data behind them, so the data needs to be clean and the source should be visible. Good tools show the underlying figures so you can check an answer rather than take it on faith.

Typical examples are sales by product or branch, how this month compares with last month, which items are selling slowest, and what is driving a change in a metric.

That depends on how the platform is set up, so ask the vendor directly where your data is processed and stored. Navaantrix can explain the arrangement for your deployment.

Vyakhyan is Navaantrix's conversational business intelligence platform. It lets people ask questions of their business data in natural language and get charts, metrics and drill-down insights in return.

Vyakhyan is a Sanskrit word meaning interpretation, explanation or detailed description. It reflects the aim of turning complex data into clear explanations that people can act on.

It is designed for small and mid-sized organisations, especially in manufacturing, retail, wholesale, distribution and logistics, and for the people who make decisions there, from senior leaders to operations managers.

Navaantrix's BI work has been tested or deployed in areas such as manufacturing analytics, sales and retail, healthcare, and toll and road operations. Ask the team for examples relevant to your industry.

You can click into any number to see what is behind it, filter by time, branch or product, and move from a summary to the underlying detail without leaving the screen.

Yes. Vyakhyan supports profile-based views, so a CEO, a department head and a shopfloor supervisor can each see the metrics that matter to their role.

Yes. You can compare any two date ranges and parameters, such as this quarter against the same quarter last year, to spot changes in demand, pricing or performance.

The platform is designed for mobile, desktop and large display use, so teams can open the same view on a phone or put it on a screen during a review meeting.

A leaderboard ranks people, branches, products or machines on a chosen metric, such as sales, output or efficiency, so you can see who is leading and who is falling behind at a glance.

A dynamic leaderboard updates as the data changes and can be re-ranked by different measures or periods, so you are always comparing current performance and not last month's snapshot.

Compare like with like: adjust for store size, shift length or product mix, and use the same time period for every item, otherwise the ranking can mislead.

Sales teams, multi-store retailers and multi-line manufacturers tend to benefit most, because they have many comparable units and a clear metric to rank them on.

They show direction and rhythm, such as growth, seasonality and sudden drops, which a single total number hides. A trend often explains the problem faster than a table of figures.

Common findings include seasonal demand, the effect of a price change or promotion, slow declines that go unnoticed, and the point where performance changed.

Place the two periods on the same chart, or use a date-range comparison, so the difference in level and shape is visible straight away.

Yes. Drill-down lets you move from a monthly view to weeks or days, or into the products or branches behind a peak or dip.

It shows when a machine stops and why, as it happens, so teams can respond within minutes. Over time the same data reveals repeating causes, which can be fixed at the source.

Start with uptime and downtime, the reason for each stop and the production count on your most important machines. Add speed, temperature and power use as you go.

Not always. Sensors give the most accurate picture, but many plants begin with production counters, machine signals or ERP data and a simple stop-reason log, then add sensors where they matter most.

OEE (Overall Equipment Effectiveness) multiplies availability, performance and quality to show how much of your planned time is truly productive. It gives one number to track improvement against.

It is the use of production, machine, quality and supply data to understand how a plant performs and to decide how to improve it, instead of relying on experience and spreadsheets alone.

Typical uses include reducing downtime, improving OEE, cutting scrap and rework, planning maintenance, and keeping better control of stock and costs.

Smaller plants often gain a lot because a few recurring losses account for much of their waste. The tools do not need to be large or complex to find them.

Pick one line or machine, agree on two or three measures, collect the data consistently for a few weeks, and review it together with the people on the shopfloor.

Tread dimensions and process settings affect quality and waste. Measuring and analysing them consistently helps teams spot drift early and keep output within tolerance.

It can show how thickness and other dimensions vary across batches, shifts and machines, and which conditions are linked to out-of-tolerance product.

When a problem is caught while it is developing, fewer units are scrapped or reworked, and settings can be corrected before a whole batch is affected.

Yes. Any process where a measurable dimension drives quality, such as extrusion, moulding or cutting, can use the same measure, analyse and adjust cycle.

An EOT (electric overhead travelling) crane is a bridge crane that moves loads along overhead rails inside a workshop or factory. Many production lines depend on them, so a failure can stop output.

Remote monitoring shows usage, load and faults in real time, so maintenance can be planned before a breakdown and safety limits are respected.

Common measures are operating hours, duty cycles, load against rated capacity, motor condition and fault events, depending on which sensors are installed.

Usage and condition data turn fixed-interval servicing into condition-based servicing, so work is done when the equipment needs it and not just when the calendar says so.

Breweries run several linked processes, from brewing and fermentation to packaging and distribution. Tracking each one shows where time, yield and product are being lost.

Useful measures include batch yield, fermentation and process timings, packaging line efficiency, downtime, wastage and stock levels across the supply chain.

Yes. Comparing process readings across batches makes it easier to see what is behind a good or poor batch and to repeat what works.

Yes. Food processors and beverage makers share the same needs for yield, line efficiency, traceability and demand planning.

Data is often scattered across ERP, spreadsheets and other tools, with no single view and no easy way to ask questions. The information exists but cannot be reached at the moment of decision.

Decide which decisions matter most, then identify the few measures that inform them and bring those together in one place.

Not necessarily. Modern tools, including conversational BI, reduce dependence on specialists, though someone should own data quality and definitions.

Check that the same figure matches across systems, that gaps are rare and that people trust the numbers. Start with the data you can fix quickly and improve it as you go.

It is the cost of decisions made late or on stale numbers, such as missed sales, excess stock, delayed fixes and time spent building reports by hand.

Common signs are monthly reports that arrive after the month has moved on, meetings spent reconciling numbers, and managers waiting days for answers to simple questions.

Automate data collection from your systems, standardise definitions, and let managers query the data directly instead of requesting each report.

Only if the data is also accurate and relevant. Speed helps most when the right measures are already agreed and trusted.

Typical challenges are data spread across several systems, limited analytics skills, small budgets and tools built for large enterprises that are too heavy or costly.

Many enterprise tools need specialist staff, long implementations and high licence costs, which smaller businesses cannot easily support.

Look for quick setup, connection to your existing ERP or POS, easy use by non-experts, mobile access and pricing that suits your size.

Start with one clear problem, such as slow reporting or high downtime, solve it with a small project, then expand once it has shown results.

DSaaS means getting data science work, such as data processing, analysis, visualisation and machine learning, from an outside provider instead of building a full in-house team.

It suits businesses that need data science skills but lack the budget, time or volume of work to hire a full team, or that want to test an idea before committing.

Navaantrix offers data processing, analysis and visualisation, along with end-to-end machine learning and AI work and research and development as a service.

Ownership should be agreed in the contract before work starts. Ask the provider to state clearly who owns the data, the models and the outputs.

Key themes included more capable language models, AI assistants built into everyday business software, growing use of retrieval over company documents, and a stronger focus on cost, safety and measurable results.

RAG lets an AI model look up information from your own documents and data before answering, which makes answers more relevant to your business and easier to trace to a source.

Pick one practical use case with a clear measure of success, test it on a small scale, and involve the people who will use it before you expand.

The main risks are inaccurate answers, exposure of sensitive data and unclear responsibility. Review outputs, limit what data the system can see and keep a person accountable for decisions.

A strategy on slides changes nothing until a working system is built, used and measured. Many AI projects stall between the plan and a tool that people rely on every day.

Common reasons are poor data quality, unclear goals, no owner, and pilots that never connect to real workflows or systems.

Start small, tie the project to a measurable business result, use real data from the beginning and plan how the tool will be used and maintained.

A clear problem, reliable data, a committed owner and users who help shape the solution and keep using it.

AI models analyse images of road surfaces, such as those captured by smartphones, to detect and classify defects like cracks and potholes, then map them for review.

Automated assessment covers more road in less time, gives consistent results and creates a record that can be tracked over time, instead of relying on slow, subjective manual surveys.

Road authorities, concessionaires, toll operators and infrastructure maintenance teams can use it to prioritise repairs and check road condition.

It can work with ordinary smartphone cameras, which keeps the cost of data collection low. Accuracy depends on image quality, lighting and the training of the model.

DuckDB is an open-source database designed for analytical queries. It runs inside your application or notebook, so there is no separate server to install or manage.

It stores and processes data by column and uses vectorised execution, which suits scans, aggregations and joins over large tables.

Yes. It can query formats such as CSV and Parquet files directly, which removes the need to load them into a database first.

Use it for analysis on a single machine, prototyping, and moderate data sizes. Choose a warehouse when many users need shared, always-on access to very large data.

There is nothing to set up: install the library, point it at your files and write standard SQL. It works from Python, R and the command line.

Basic SQL is enough for most tasks, and DuckDB supports familiar syntax with helpful extras for working with files and dates.

Often yes. It handles files that are too big or slow for a spreadsheet and lets you repeat the same analysis each time the data updates.

It is widely used in data pipelines and applications. Check that its single-process design fits your concurrency and availability needs.

A systemd timer is a unit that starts a service at a set time or interval. It is the systemd alternative to a cron job.

Timers log to the journal, can run a missed job after downtime, handle dependencies between services and give clear status through systemctl.

Set Persistent=true in the timer. systemd will then run any job that was missed while the machine was powered off.

Use systemctl list-timers to see the schedule and journalctl -u your-service to read the output and any errors.