Prayag Labs builds agentic AI systems that let a business ask questions of its own data — databases, documents, and operations — in plain English, and get back answers it can verify. We package and deploy the whole system for you, so teams without data scientists get enterprise-grade capability without the enterprise-grade setup.
Deployed on your infrastructure · Your data stays yours
Sales, stock and customers usually sit in different systems. Ask across all of them:
Markdown and buying calls made the same day, not next week.
Order patterns tell you about a customer long before the customer does:
Catch a slipping account while it is still recoverable.
Churn is never spread evenly — it hides in a plan, a cohort, a month:
Point retention effort where it actually pays.
Variance reports tell you what moved. They rarely tell you why:
Close the month knowing the cause, not just the number.
Why did revenue dip in the North region last quarter?
Read the question
Compare North net revenue for the most recent closed quarter against the previous five, then explain the change.
Mapped it to your schema
Ran this query read‑only
SELECT DATE_TRUNC('quarter', o.order_date) AS quarter, SUM(o.net_revenue) AS revenue, COUNT(DISTINCT o.account_id) AS accounts FROM fct_orders o JOIN dim_region r ON r.region_id = o.region_id WHERE r.region_name = 'North' AND o.order_date >= DATEADD(quarter, -6, CURRENT_DATE) GROUP BY 1 ORDER BY 1;
Checked what came back
6 quarters returned · 1,284 orders · no null revenue rows · quarter totals reconcile against your finance cube.
North revenue fell 38.6% quarter over quarter. The decline is concentrated in two accounts that reduced order volume, not a broad drop across the region — the remaining accounts were roughly flat.
Grounded in your own sources, cited, and checkable. Here is what that looks like in practice.
Terms are buried in agreements nobody has time to re-read:
Every answer points at the clause it came from.
The same questions arrive every week, and the answer is always in a document:
People self-serve, and HR stops re-answering the same thing.
The procedure exists. Finding the right step at the right moment is the problem:
Answers carry the page and revision, so nobody works off an old manual.
Obligations sit scattered across regulations, policies and past audit responses:
Cited answers you can put in front of an auditor.
What’s our refund window on enterprise contracts?
60 days from the invoice date — provided the request is submitted in writing and the account is in good standing.
Where this came from“…Customer may request a refund within sixty (60) days of the invoice date, provided such request is submitted in writing to the Supplier…”
“…enterprise accounts in good standing are eligible under the standard sixty-day window…”
A part’s story is spread across orders, inspections, emails and reports:
One timeline, every row traceable to the file it came from.
The facts are all in the bundle. Assembling them is the work:
A dated chronology with a source on every entry.
A claim file is dozens of documents written by different parties:
Reviewers see the whole history without reading the whole file.
After an incident, the account has to be rebuilt from what everyone wrote down:
A defensible account, sourced line by line.
Reconstruct what happened with part 4471.
“Qty 500 · supplier Meridian Tooling · due 24 Mar”
po_4471.pdf · p.1“OD 12.04 mm against 12.00 ±0.02 — 14 of 50 sampled”
ncr_218.xlsx · row 87“8D report requested, due 29 Mar”
supplier_thread.msg · msg 4“Root cause: tool wear on die 3. Die replaced, re-qualified.”
car_07.docx · p.36 files · 2 systems · 41 pages read · every row opens to its source
Six systems, one foundation: every answer traces back to a source you can check.
Ask your business database in plain English — “what was revenue last quarter?”, “why did the North region dip?” — and get a clear answer, an interactive chart, and the exact query it ran, so you can verify the number. It follows the thread like an analyst would: describe → diagnose → forecast → recommend, all in one conversation.
Best forOperators and owners who need answers from their data without waiting on a BI team.
Ask questions across your documents — policies, manuals, contracts, reports — and get answers grounded in the source, with citations to the exact page or passage. Works across PDFs, slides, spreadsheets, and images, so nothing is locked away in a file no one has time to read.
Best forTeams sitting on large, messy document libraries.
Resolve the bulk of self-serve (L0) and first-line (L1) support questions from your knowledge base and your own customer data — grounded, cited, and with a built-in handoff to a human whenever it isn’t confident. It deflects the routine so your people handle what actually needs them.
Still unresolved — handed to a human, whole thread attached.
Best forSupport teams drowning in repetitive tickets.
Move from “what happened” to “what should I do.” Demand forecasting, inventory and reorder-point recommendations, and cost planning — built on proven statistical and machine-learning models, with every recommendation left for a human to approve. The forecast feeds the decision; you stay in control.
Best forRetail, e-commerce, and operations teams managing stock and spend.
Summarize long documents on demand, walk through them section by section, and reconstruct timelines of events stitched across many files — useful when the answer isn’t in one document but scattered across dozens.
Best forCompliance, operations, and any review over large document sets.
When an off-the-shelf approach isn’t enough, we design and build a bespoke model or pipeline for your specific problem — and integrate it into the same verifiable, packaged system as everything above.
Best forProblems that don’t fit a standard product.
The hard part of AI on real data isn’t sounding fluent — it’s being right when a wrong answer looks exactly like a right one. Every answer we produce traces back to a real source you can check.
No data team required on your side. We do the setup, the data mapping, and the tuning, and hand you a system your people can just use.
Deployed on your infrastructure, working against your data, with your own AI provider keys. Nothing is handed off to us to hold.
The same capability the big companies pay data-science teams for — packaged so a lean team can adopt it.
If the data is already there and nobody has the time — or the tooling — to interrogate it, that’s the gap we fill.
Three steps, and the middle one is where the accuracy comes from.
To your data and documents — wherever they already live.
Mapping what your metrics, documents, and terms actually mean, so answers are correct — not just fluent.
Questions in plain English. Your team gets verifiable answers, charts, and recommendations.
We scope against your actual data and a real question you need answered — not a generic demo — then build, deploy, and hand over.
On your infrastructure, against your data, using your own AI provider keys. We don’t hold your data to operate the system.
A working system your team can use directly, with every answer traceable to the source it came from.
References and case studies are available on request — sales@prayaglabs.com
Tell us what you’re trying to figure out. If we’re a fit, we’ll show you what it looks like against data like yours.