Question 1

What is iDBQuery?

Accepted Answer

iDBQuery is a data-intelligence platform that lets you chat with your databases, spreadsheets, PDFs and APIs in plain language and get a cited answer in seconds. It connects everything into one live model, writes the SQL for you, and traces every number back to its exact source row.

Question 2

What problem does iDBQuery solve?

Accepted Answer

iDBQuery solves the gap between having data and getting answers from it. Instead of waiting days for an analyst, exporting to spreadsheets, or stitching together a warehouse, you ask a question in plain language and get a cited answer in seconds — across all your databases and files at once.

Question 3

What does it mean to chat with your data in plain language?

Accepted Answer

Chatting with your data means asking questions in ordinary English — like "which regions missed target last quarter?" — and getting a real answer from your actual databases and files. iDBQuery translates your question into SQL, runs it against your live data, and replies with a cited number, chart or table.

Question 4

What is a cited answer and why does it matter?

Accepted Answer

A cited answer is an answer where every number traces back to the exact source rows it came from. iDBQuery shows the query it ran and the specific records behind each figure, so you can verify the result yourself — no black box, no "trust me." It matters because data decisions need proof.

Question 5

What does iDBQuery mean by one live queryable model?

Accepted Answer

One live queryable model means iDBQuery joins all your sources — databases, spreadsheets, PDFs and APIs — into a single thing you can question, without copying them into a warehouse. It reads each source in place and queries them together, so one question can span everything you've connected at once.

Question 6

What makes iDBQuery different from a normal AI chatbot?

Accepted Answer

Unlike a normal AI chatbot, iDBQuery answers only from your real data and cites every number back to its exact source row. A chatbot generates plausible text with no way to verify it; iDBQuery writes and runs real queries against your connected sources and shows its work, so the answer is auditable.

Question 7

Who is iDBQuery for?

Accepted Answer

iDBQuery is for anyone who needs answers from data without writing code — finance and FP&A teams, analysts, operations, school and district admins, researchers, founders and execs. If you have data in databases, spreadsheets, PDFs or APIs and questions you want answered fast, iDBQuery is for you.

Question 8

What kinds of questions can I ask iDBQuery?

Accepted Answer

You can ask iDBQuery almost any question your data can answer — totals, trends, comparisons, rankings, anomalies, reconciliations and "why" questions. Ask it to chart results, build a table, make a dashboard, or investigate something on its own. If the answer is in your connected sources, you can ask for it in plain language.

Question 9

Is iDBQuery a business intelligence tool?

Accepted Answer

iDBQuery does what a BI tool does — charts, tables, live dashboards and shareable reports — but you build them by asking a question instead of dragging fields and writing queries. It also goes further with cited answers, cross-source querying, and an autonomous Analyst agent, so it complements or replaces parts of a BI stack.

Question 10

Do I need to know SQL to use iDBQuery?

Accepted Answer

No — you don't need to know SQL to use iDBQuery. You ask questions in plain language and iDBQuery writes and runs the SQL for you. If you do know SQL you can read the query it generated and verify it, but it's never required to get an answer.

Question 11

What is the Analyst agent in iDBQuery?

Accepted Answer

The Analyst is iDBQuery's autonomous agent that investigates a question on its own. Instead of answering a single query, it plans an approach, runs multiple queries, tests hypotheses, and reports back with findings and cited evidence — like handing a hard, open-ended question to a tireless data analyst.

Question 12

Does iDBQuery replace my data team?

Accepted Answer

No — iDBQuery makes your data team more effective, not redundant. It handles the flood of repetitive ad-hoc questions so analysts focus on hard, high-value work. Non-technical staff self-serve simple answers, and analysts use iDBQuery to explore, prototype and reconcile faster. It's a force multiplier, not a replacement.

Question 13

How does iDBQuery connect to my databases and files?

Accepted Answer

iDBQuery connects to your data through a guided connection screen: enter credentials for a database, or upload a spreadsheet, PDF or folder. It supports MySQL, PostgreSQL, MongoDB, Excel, CSV, Google Sheets, PDFs and REST APIs, then reads each source's structure automatically with nothing to install.

Question 14

How does iDBQuery build one live model from all my sources?

Accepted Answer

iDBQuery builds one live queryable model by introspecting every connected source and federating them into a single virtual layer you can query together. There is no warehouse to provision and no pipeline to maintain — your databases, spreadsheets, PDFs and APIs become one model that always reflects the live data underneath.

Question 15

How does iDBQuery write the SQL for me?

Accepted Answer

iDBQuery writes SQL for you by combining your plain-language question with the schema it has already learned from your sources. Its AI maps your words to the right tables, columns and joins, generates the query, runs it, and returns the answer — so you never touch SQL yourself.

Question 16

How does iDBQuery cite its answers back to source rows?

Accepted Answer

iDBQuery cites its answers by linking every number back to the exact source rows that produced it. When it runs a query, it keeps the underlying records and shows them alongside the result — so you can click any figure, see the rows it came from, and verify it instead of trusting a black box.

Question 17

Does iDBQuery copy my data or query it in place?

Accepted Answer

iDBQuery queries your data in place wherever possible — it reads your databases live at query time rather than copying them into a separate store. It never writes back to your systems, uses read-only access, and on Desktop or air-gapped deployments your data never leaves your environment at all.

Question 18

What is the semantic layer and how does it help?

Accepted Answer

The semantic layer is a meaning layer iDBQuery builds on top of your raw schema so it understands what cryptic table and column names actually represent. It records definitions, business terms and relationships — so when you ask about "revenue" or "active customers," iDBQuery maps those words to the right fields.

Question 19

How does iDBQuery understand a cryptic or obscure database schema?

Accepted Answer

iDBQuery understands cryptic schemas by enriching the raw structure with a semantic layer: it infers what abbreviated table and column names mean, pulls in definitions from data dictionaries and documentation, learns from the data itself, and lets you correct it — so obscure field names stop being a barrier to good answers.

Question 20

How does iDBQuery read PDFs and scanned documents?

Accepted Answer

iDBQuery reads PDFs and scanned documents by extracting their text and structure — using OCR for scanned or image-based pages — and turning the content into part of your live queryable model. You can then ask questions across your documents the same way you query a database, with answers cited back to the source page.

Question 21

How does iDBQuery ingest a folder of files with OCR?

Accepted Answer

iDBQuery ingests a folder of files by processing every document in it — extracting text, running OCR on scanned pages, and capturing structure — so the whole folder becomes one queryable model. Ingestion runs in the background, and you can track each file's status as it's processed and made ready to query.

Question 22

How does the Analyst agent investigate a question on its own?

Accepted Answer

The Analyst agent investigates on its own by breaking your question into steps, running its own queries, examining the results, and following the evidence further — checking, slicing and cross-referencing data without you steering each move. It returns a reasoned, cited findings summary, not just a single number.

Question 23

Where does my data live when I use iDBQuery?

Accepted Answer

Where your data lives depends on the deployment you choose. With iDBQuery's cloud, sources are queried in place under read-only access; in your own VPC, everything runs inside your network; and on the Desktop app or air-gapped deployment, your data never leaves your machine or environment at all.

Question 24

How long does it take to set up iDBQuery?

Accepted Answer

iDBQuery takes minutes to set up, not weeks. You connect a source by entering credentials or uploading a file, iDBQuery introspects it automatically, and you can ask your first plain-language question right away — there's no warehouse to build, no pipelines to wire, and no schema modelling to do first.

Question 25

How does iDBQuery make sure its answers are accurate?

Accepted Answer

iDBQuery keeps answers accurate by grounding every response in your real data, not the model's memory: it writes and runs an actual query, checks the results, and cites the exact source rows behind each number. A semantic layer ensures it queries the right fields, and you can verify any figure yourself.

Question 26

How do I do ad-hoc data analysis without writing SQL?

Accepted Answer

You do ad-hoc analysis in iDBQuery by typing your question in plain language and getting a cited answer in seconds. iDBQuery writes the SQL, runs it against your live data, and shows the chart, table, and exact source rows behind every number — no query language, no spreadsheet exports.

Question 27

How do I build board and executive reports with iDBQuery?

Accepted Answer

You build board and executive reports in iDBQuery by asking for the numbers you need in plain language, then assembling the cited answers into a shareable report. iDBQuery pulls live data from every source, writes the queries, generates the charts and tables, and keeps each figure traceable to its source.

Question 28

How does iDBQuery enable self-serve BI for a whole team?

Accepted Answer

iDBQuery enables self-serve BI by letting anyone on the team ask questions in plain language and get cited answers — no SQL, no dashboard-building, no analyst queue. It connects once to your sources, builds one live model, and writes every query, so non-technical staff explore data safely and independently.

Question 29

How do I investigate an anomaly or unexpected number in my data?

Accepted Answer

To investigate an anomaly in iDBQuery, ask why the number looks off and let the autonomous Analyst agent dig in. It queries your live data across sources, isolates what changed — a segment, a period, a bad record — and returns a written, cited finding pointing to the exact rows behind the spike or drop.

Question 30

How do I turn a question into a live dashboard?

Accepted Answer

In iDBQuery you turn a question into a live dashboard simply by asking it. Each plain-language question becomes a cited chart or table backed by a live query, and you pin those into a dashboard that refreshes against your real data — no chart-building, no dragging fields, no manual modeling.

Question 31

How do I explore a database I have never seen before?

Accepted Answer

To explore an unfamiliar database, connect it to iDBQuery and just start asking questions. iDBQuery introspects the schema, uses its semantic layer to decode cryptic table and column names, and answers in plain language — so you understand what's in there without reading documentation or reverse-engineering joins.

Question 32

How do I answer a question that spans several databases and spreadsheets at once?

Accepted Answer

iDBQuery answers a question that spans several databases and spreadsheets by federating them into one live model and querying them together. Ask once — "compare sales from our database with the budget in this spreadsheet" — and iDBQuery joins across sources and returns a single cited answer, no warehouse or pipeline required.

Question 33

How do I create a shareable report from my data?

Accepted Answer

To create a shareable report in iDBQuery, ask for the numbers you want, then save the cited answers, charts, and tables as a report you can share by link or export. iDBQuery pulls the data live from every source, so recipients get an accurate, traceable report without access to the underlying systems.

Question 34

How do I track KPIs and metrics with iDBQuery?

Accepted Answer

You track KPIs in iDBQuery by asking for each metric in plain language and pinning the cited answers into a live dashboard that refreshes against your real data. iDBQuery writes the queries, joins across sources, and keeps every KPI traceable to its source rows — so the numbers stay current and defensible.

Question 35

How do I reconcile numbers that disagree between two systems?

Accepted Answer

iDBQuery reconciles numbers that disagree between two systems by querying both as one model and comparing them row by row. Connect each source, ask iDBQuery to match the figures, and it surfaces the exact records that differ — with citations to both sides — so you find the cause of the discrepancy instead of guessing.

Question 36

How do I answer ad-hoc stakeholder questions in seconds instead of days?

Accepted Answer

iDBQuery answers ad-hoc stakeholder questions in seconds by turning each one into a query against your live data and returning a cited answer. Instead of opening a ticket and waiting days for an analyst, you type the question, iDBQuery writes the SQL, and you reply with a chart and numbers you can defend.

Question 37

How do I trace a number in a report back to the exact source row?

Accepted Answer

In iDBQuery every number is cited, so you trace any figure to its exact source row by clicking it. iDBQuery shows the query it ran, the source, and the specific records behind the result — so a figure in a report or dashboard is always auditable, never a black box.

Question 38

How does iDBQuery help finance and FP&A teams?

Accepted Answer

iDBQuery lets finance and FP&A teams ask questions across their ledger, ERP, spreadsheets and reports in plain language and get a cited answer in seconds. It builds one live model from every system, writes the SQL, and traces every number back to its exact source row.

Question 39

How does iDBQuery help data analysts work faster?

Accepted Answer

iDBQuery makes data analysts faster by writing the first-draft SQL, federating every source into one model, and citing each result back to its source row. Analysts spend less time on plumbing and ad-hoc requests and more on the analysis that actually needs human judgment.

Question 40

How does iDBQuery help operations teams?

Accepted Answer

iDBQuery helps operations teams ask questions across orders, inventory, logistics and ticketing systems in plain language and get cited answers instantly. It unifies your operational sources into one live model, surfaces anomalies, and tracks the KPIs that keep the day running.

Question 41

How does iDBQuery help school and district administrators?

Accepted Answer

iDBQuery gives school and district administrators plain-language answers across enrollment, finance, academic and operational data in seconds, each one cited back to its source. It builds one live model from your student, finance and reporting systems so leaders get evidence without a data team.

Question 42

How does iDBQuery help researchers and academics?

Accepted Answer

iDBQuery helps researchers and academics query datasets, spreadsheets, document corpora and research APIs in plain language and get cited results. It federates heterogeneous sources into one live model and traces every figure to its origin, making analysis fast, transparent and reproducible.

Question 43

How does iDBQuery help founders and executives?

Accepted Answer

iDBQuery lets founders and executives ask the business anything in plain language and get a cited answer in seconds, without pinging an analyst. It unifies your databases, spreadsheets and tools into one live model so you self-serve metrics, dashboards and board reports directly.

Question 44

How does iDBQuery help non-technical teams use data?

Accepted Answer

iDBQuery lets non-technical teams use their data by asking questions in plain language and getting cited answers, no SQL or BI training required. It writes the queries, builds charts and dashboards automatically, and shows the source behind every number so anyone can trust the result.

Question 45

How does iDBQuery help product and growth teams?

Accepted Answer

iDBQuery helps product and growth teams answer questions about funnels, retention, activation and experiments in plain language, with every number cited. It joins your product database, analytics exports and spreadsheets into one live model so you investigate metrics without waiting on engineering.

Question 46

How does iDBQuery help real estate teams?

Accepted Answer

iDBQuery helps real estate teams query portfolios, leases, rent rolls, financials and market data in plain language and get cited answers. It unifies property databases, spreadsheets and PDF documents into one live model so you analyze performance and reporting without a data team.

Question 47

How does iDBQuery help consultants and advisors?

Accepted Answer

iDBQuery helps consultants and advisors get up to speed on a client's unfamiliar data fast, asking questions in plain language and producing cited deliverables. It federates whatever sources the client hands you into one live model, so you deliver insight in hours instead of weeks.

Question 48

How does iDBQuery help small business owners?

Accepted Answer

iDBQuery lets small business owners ask questions across their sales, accounting, inventory and spreadsheet data in plain language and get cited answers in seconds, with no analyst or BI tool needed. It connects everything into one live model so you understand your business directly.

Question 49

How is iDBQuery used in finance?

Accepted Answer

In finance, iDBQuery lets teams chat with their accounting, ERP, billing, and spreadsheet data in plain language and get cited answers in seconds. It powers FP&A work like variance analysis, forecasting, ratios, and board reporting without writing SQL, every figure traceable to its exact source row.

Question 50

How is iDBQuery used in schools and education?

Accepted Answer

In schools and education, iDBQuery lets administrators ask plain-language questions across student information systems, finance, attendance, and assessment data and get cited answers in seconds. It powers a K-12 intelligence and advisory layer — finance dashboards, academic trends, and benchmarking — with no SQL and no data team required.

Question 51

How is iDBQuery used in research and academia?

Accepted Answer

In research and academia, iDBQuery lets researchers query datasets, repositories, spreadsheets, and document libraries in plain language and get cited answers fast. It builds one queryable model across messy sources, reads PDFs with OCR, and traces every finding to its exact source — ideal for reproducible, defensible analysis.

Question 52

How is iDBQuery used for operations intelligence?

Accepted Answer

For operations intelligence, iDBQuery lets teams ask plain-language questions across ERP, ticketing, logistics, and spreadsheet data and get cited answers in seconds. It unifies operational sources into one live model, surfaces bottlenecks and anomalies, and traces every metric back to the exact records behind it.

Question 53

How is iDBQuery used in real estate?

Accepted Answer

In real estate, iDBQuery lets teams chat with portfolio, lease, valuation, and financial data in plain language and get cited answers in seconds. It unifies property management systems, spreadsheets, and documents into one live model, so occupancy, yield, and cash-flow questions are answered instantly and traceably.

Question 54

How is iDBQuery used in retail and e-commerce?

Accepted Answer

In retail and e-commerce, iDBQuery lets teams ask plain-language questions across sales, inventory, marketing, and order data and get cited answers in seconds. It joins your store platform, ERP, ad exports, and spreadsheets into one live model — so sales, margin, and inventory questions are answered instantly without SQL.

Question 55

How is iDBQuery used with healthcare data?

Accepted Answer

With healthcare data, iDBQuery lets teams query operational, financial, and administrative datasets in plain language and get cited answers — while keeping sensitive data local. It can run air-gapped, in your VPC, or as a Desktop app so records never leave your control, with every answer traceable to its exact source row.

Question 56

How is iDBQuery used by SaaS companies?

Accepted Answer

SaaS companies use iDBQuery to chat with their product, billing, CRM, and support data in plain language and get cited answers in seconds. It joins Postgres, MongoDB, billing APIs, and spreadsheets into one live model — so MRR, churn, and usage questions are answered instantly without bothering an engineer.

Question 57

How is iDBQuery used by nonprofits and government?

Accepted Answer

Nonprofits and government teams use iDBQuery to ask plain-language questions across grant, programme, finance, and service data and get cited answers in seconds. It unifies databases, spreadsheets, and documents into one live model, so transparency and reporting needs are met with figures traceable to their exact source.

Question 58

How is iDBQuery used in manufacturing and supply chain?

Accepted Answer

In manufacturing and supply chain, iDBQuery lets teams query ERP, inventory, production, and supplier data in plain language and get cited answers in seconds. It unifies systems and spreadsheets into one live model, surfacing yield, inventory, and supplier issues without SQL — every metric traceable to its source.

Question 59

Can iDBQuery work in any industry that has data?

Accepted Answer

Yes — iDBQuery works in any industry that has data. It is general-purpose: connect MySQL, Postgres, MongoDB, Excel, CSV, PDFs, or REST APIs, and chat with them in plain language for cited answers in seconds. The same engine serves finance, schools, research, operations, real estate, retail, and beyond.

Question 60

Can I connect MySQL to iDBQuery?

Accepted Answer

Yes. iDBQuery connects directly to MySQL using a read-only connection string, introspects every table and column automatically, and lets you ask questions in plain language within minutes. No exports, no schema mapping, and no SQL knowledge required to get cited answers from your live MySQL database.

Question 61

Can I connect PostgreSQL to iDBQuery?

Accepted Answer

Yes. iDBQuery connects to PostgreSQL with a read-only connection string, automatically introspects all your schemas, tables and columns, and lets you ask questions in plain language right away. Every answer is cited back to the exact source rows, with no exports or pipelines required.

Question 62

Can I connect MongoDB to iDBQuery?

Accepted Answer

Yes. iDBQuery connects to MongoDB, introspects your collections and document fields, and lets you ask questions in plain language. It handles nested documents and flexible schemas automatically, then returns cited answers traced back to the exact documents your data came from.

Question 63

Can I connect Excel spreadsheets to iDBQuery?

Accepted Answer

Yes. iDBQuery reads Excel spreadsheets directly — upload an .xlsx file and it detects sheets, headers and data types, then lets you ask questions in plain language. Your spreadsheet becomes a queryable source you can blend with databases and other files, with cited answers throughout.

Question 64

Can I connect CSV files and Google Sheets to iDBQuery?

Accepted Answer

Yes. iDBQuery ingests CSV files and Google Sheets exports, detecting headers and column types automatically so you can ask questions in plain language immediately. Each file becomes a queryable source you can blend with databases, Excel and other files, with every answer cited to its rows.

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