Natural-Language Data Dashboard
A natural-language dashboard lets anyone ask questions of your business data in plain English and get a chart and an answer. "Which product lost money last quarter" returns the figures rather than a request to the analyst who is on leave. It works well when the underlying data is clean and defined, and badly when it is not.
Who it's for
Owners and managers who cannot get reports out of their own systems.
What changes
Analytics without waiting on anyone.
- Starting at
- ₹6,00,000
- Timeline
- 10–20 weeks
- Category
- AI Web Apps & SaaS
- Built from
- Vashi, Navi Mumbai
Key takeaways
- Question accuracy depends almost entirely on how well your data model is defined.
- Every answer must show the query it ran — an unexplained number is untrustworthy.
- Ambiguous business terms must be defined once, centrally, not interpreted per question.
- Read-only access, always. This system never writes to your database.
- Builds in 8–14 weeks from ₹3,50,000.
What this is actually solving
In most Indian SMBs, data questions queue behind one person who knows the system. A manager wanting last month's margin by branch either learns to build the report themselves, waits three days, or does without and decides on instinct.
The consequence is not that questions go unanswered. It is that they go unasked, because the friction is high enough that people stop wondering.
A system where asking costs ten seconds changes what people look at. That behavioural change is the return, more than any individual report.
Why the semantic layer decides everything
The model translates a question into a database query. To do that correctly it needs to know what your terms mean, and your database schema does not contain that.
| Business term | The ambiguity | Must be defined as |
|---|---|---|
| Revenue | Gross, net of returns, with or without GST | One agreed definition |
| Active customer | Bought when? Within what window? | Explicit rule |
| Last quarter | Calendar or financial year | Indian FY, April–March |
| Margin | Gross, contribution, after which costs | Named formula |
| Region | Sales territory or postal geography | Mapped hierarchy |
| Churn | Cancelled, lapsed, or inactive | Stated threshold |
Where it works well and where it does not
It handles aggregation, filtering, comparison and trend questions reliably — totals by dimension, period-on-period changes, rankings, filtered counts. That covers a large share of what people actually ask.
It struggles with questions requiring multi-step reasoning across several sources, anything depending on business context not present in the data, and "why" questions. "Why did sales drop in June" cannot be answered from a sales table; the answer is usually outside the database entirely.
We are explicit about this boundary in the interface. A system that attempts every question produces confident nonsense on the ones it cannot handle, which costs more credibility than declining would.
Connecting to what you already run
The data has to come from somewhere, and in most Indian SMBs it is scattered.
- Tally. Extracted via its XML interface into a reporting store, since querying Tally directly is not practical.
- ERP and accounting systems. Zoho, Odoo, SAP and most others through their APIs or database replicas.
- Spreadsheets. Still where a surprising amount of real business data lives, and worth including rather than pretending otherwise.
- Operational databases. Read replicas, never the production database directly.
- A consolidation layer. Where data spans several sources, a small warehouse is usually necessary before any of this works.
Access control, which matters more than in a normal dashboard
A conventional report is built once with its permissions baked in. A system that answers arbitrary questions can be asked anything, including about salaries, margins and other branches' performance.
Row and column level permissions have to be enforced at the query layer, not in the interface. A branch manager asking about company-wide margins should get a refusal, not a chart.
We build this as an explicit permission model reviewed with you before launch. It is also the reason the system holds read-only credentials with restricted scope rather than general database access.
Build and handover
Eight to fourteen weeks from ₹3,50,000, with the semantic layer and data consolidation taking more of that than the interface does.
You receive the system on your infrastructure, the semantic layer as documented configuration your team can extend, the permission model, query logs for audit, and full source.
The semantic layer is worth keeping current. As the business adds product lines, branches or metrics, definitions need adding, and that is a configuration task rather than a development one by design.
FAQ
Natural-Language Data Dashboard — your questions
How accurate are the answers?
With a well-built semantic layer, correct on the large majority of questions within its scope, and the errors are usually ambiguity rather than arithmetic — the system interpreting "last month" or "top customers" differently from the asker. Showing the query catches those immediately. Where the semantic layer is thin or the underlying data is inconsistent, accuracy drops sharply, which is why we spend more of the project on definitions than on the model.
Can it work directly on our Tally data?
Not directly, and anyone who says otherwise has not tried. Tally's data structure is not designed for analytical querying and its interface is not suited to it. The working approach is a scheduled extraction from Tally into a proper reporting database, typically nightly, which then serves the dashboard. This also means queries never touch your accounting system, which your accountant will appreciate.
Does it work in Hindi?
Yes, question understanding in Hindi and Hinglish works well, and for many Indian teams this is what makes the system usable beyond the head office. The data and column names stay in English; the translation happens at the question layer. Answers can be returned in the same language as the question. We configure this per client based on who will actually be asking.
Can it write data back or take actions?
No, by design. The system holds read-only credentials and cannot modify anything. A model that misinterprets a question and produces a wrong chart is a recoverable problem; one that misinterprets and updates a record is not. Where clients want action-taking, that belongs in a separate system with explicit confirmation steps, not in a tool designed for open-ended questions.
What if our data is messy?
Then fix that first, and we will say so at the assessment rather than after. Inconsistent customer names, products entered three different ways, missing dates and duplicated records all produce wrong answers that look right. Part of most engagements is a data quality assessment and a cleanup, and occasionally we have recommended a client spend three months tidying their data before commissioning the dashboard at all.
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