Cyber Fraud Intelligence: Turning Crime-Database Questions into Verified Reports (2026)

Ask a question in plain language — “cyber cases in Madhapur in 2024” — and an AI agent queries the crime database, builds the report, checks it, and returns a download. The whole flow is: question → checks → agent thinks → queries the database → builds the Excel/Word/PDF → quality checks → saves and returns.

Asking a question

You write the ask in natural language and choose the output format — xlsx (Excel), docx (Word), or pdf — and a mode:

• Agentic — the agent runs on its own and the file is saved to your library automatically.
• Regular — the file comes back inline so you can keep editing before saving.

Gatekeeping

Only signed-in users get through, and a daily quota caps how many reports each user can generate — keeping usage controlled and accountable.

The thinking loop

The “agent” reasons step by step, but within firm limits so it never runs away or returns nothing:

• Step budget — a cap on how many thinking steps it may take (40).
• Time budget — a cap on how long it can run before it must wrap up.
• Final stretch — when steps or time are nearly gone, it stops researching and just builds the file, so you never get an empty result.
• Tool calls — it doesn’t answer from memory; it calls tools to look things up and to build files.

Looking up the data

The agent works against the Cyberabad FIR records (~250,000 cases). It peeks at the schema (list tables, describe table, sample rows), then runs the actual SQL to pull the matching cases, and can use ready-made breakdowns by crime type, station, and more.

Building the deliverable

From the rows it gathers, it can create charts, an Excel sheet (including straight from a query, with no row limit), a Word document, or a PDF — the finished file is the artifact, with a small preview of rows shown in the chat.

Quality checks before handing it over

A second AI verifier reviews the answer before delivery:

• Citation check — confirms the case numbers it cites are real, not invented.
• Location check — confirms it actually filtered to the right area or station.
• Degeneration guard — catches junk output like “2 ? ? ?” instead of a real number and makes it redo that part.
• Data integrity warnings — surfaces any flags the checks raise.

Saving and delivering

Agentic files are stored in your library automatically with a clean, human-friendly filename and searchable keywords pulled from the question. You get a download link, and a step trace records every step and how long it took.

Frequently asked questions

How do I ask for a report?

In plain language — for example, “cyber cases in Madhapur in 2024” — and you pick the output format (Excel, Word, or PDF) and whether it runs agentically or returns inline.

How do I know the report is accurate?

A verifier runs citation checks (real case numbers), a location check (correct area/station), and a degeneration guard (no junk values) before the file is handed over.

What data does it use?

The Cyberabad FIR records — around 250,000 cases — queried with SQL, plus ready-made breakdowns by crime type and station.

Will it ever return an empty result?

No. When the step or time budget is nearly spent, the final-stretch rule forces it to stop researching and build the file so you always get a deliverable.