Data · Applications · AI · Stories

Your data is your most
underused asset.

DataVolt helps businesses turn their data into three kinds of value — stories that build authority, applications that automate processes, and foundations that make AI work.

01 · LEADS
📰
Data Stories
Original research and data journalism from your proprietary data — published under your brand, impossible to copy.
02 · CORE
⚙️
Data Applications
Custom applications that connect your databases, APIs, and files into one reliable, accurate process.
03 · FORWARD
🤖
AI Readiness
The data foundation your business needs before AI can actually work — built systematically, not rushed.
Why This Order

Stories. Applications. AI.
Not arbitrary.

Most businesses want to jump to AI. Some want to automate first. Very few think about publishing their data. We think the order matters — and here's why.

Data Stories comes first because it forces the most valuable discipline: finding what's actually interesting in your data. That clarity makes your applications more focused and your AI investments more targeted.

1
Data Stories builds external authority
Proprietary research nobody else can replicate. The lowest competition, highest credibility play in B2B marketing.
2
Data Applications builds operational reliability
Accurate, repeatable processes replace fragile manual work. This is the core — every business needs it.
3
AI Readiness builds strategic advantage
Once your data is clean, structured, and flowing — AI actually works. Not before.
📊
📰Data Stories
⚙️Applications
🤖AI Readiness
Case Studies

Across all three services.

From original research reports to FMCG reconciliation apps to HR analytics — here's what DataVolt has delivered for real clients.

Data Stories

Ecommerce Returns by Geography

Turned a D2C client's 200K return records into a published benchmark report on return rates by ZIP code cluster and product category — generating 40+ inbound leads for the client.

40+
inbound leads generated
1
dataset → published report
Data Applications

Attrition Analysis Automation

Replaced a manual Excel-based HR attrition process for a consulting firm — from 2 days of prep per client to a one-click, upload-and-run application.

90%
less report prep time
more client capacity
Data Applications

Transaction Classification — FMCG

Built an automated transaction classification system for a mid-size FMCG distributor — handling multi-UPI payment routing and bank reconciliation without manual intervention.

↓80%
manual effort
UPI sources reconciled
Work With Us

Not sure where to start?
Start with a conversation.

Tell us what you're working on — a dataset, a manual process, or an AI ambition. We'll tell you honestly which service fits, in what order, and what it would cost.

No commitment · No sales deck · Just an honest conversation

Data Stories · Original Research

Your data has a story
your competitors
can't copy.

DataVolt turns your proprietary business data into original research reports, benchmark studies, and data-backed articles — published under your brand, impossible to replicate with AI or generic content.

ORIGINAL RESEARCH · Q3 2025
Ecommerce Returns by Geography:
What ZIP Codes Tell You
About Your Loss Rate
Based on 200,000+ return records across 14 states
34%
avg return rate, Tier-1 cities
11%
avg return rate, Tier-3 towns
₹280
avg logistics cost per return
Return Rate by Region
What This Is

Not content marketing.
Data journalism.

There's a category of content that the best B2B companies — Stripe, Shopify, Semrush — use to dominate their markets. They publish original research from their own data. Reports that become industry benchmarks. Articles that get cited for years.

Most companies can't do this because they don't have the analytical capability to find the story inside the data, and most content agencies can't do it because they don't understand data.

DataVolt sits exactly at that intersection.

Generic Content Writing
DataVolt Data Stories
Based on secondary research anyone can Google
Identical to what your competitors publish
No original data, no real differentiation
Replaceable by AI content tools tomorrow
Agencies can't interpret or analyse raw data
Built from your proprietary business data
Nobody else has access to your dataset
Findings that become industry reference points
Cannot be replicated by AI or competitors
We query, analyse, find the story, then write it
How It Works

From your dataset to a published piece.

Three steps. You provide the raw material. We provide everything else.

1

You Share the Data

Give us access to a dataset — transaction records, survey responses, usage logs, customer data, operational data. It doesn't have to be clean. We handle that. We sign NDAs, we keep it confidential, always.

2

We Find the Story

We analyse the data, look for patterns, anomalies, benchmarks, and surprising findings. Then we decide the angle — the specific insight that's interesting enough to publish and share. This is where the real work happens.

3

We Produce the Piece

We write the report, article, or study — with your brand on it. Data visualisations, key findings, methodology, executive summary. Ready to publish on your blog, LinkedIn, or as a gated lead magnet.

What We Produce

Four formats. All original. All data-backed.

Each format serves a different marketing goal — from thought leadership to lead generation to PR.

📊
Benchmark Reports
Industry-wide or segment-level benchmarks drawn from your aggregated data. Positions your company as the definitive source of truth in your market.
e.g. "Fashion Retail Returns Benchmark 2025 — by Category, Channel & Geography"
🔬
Segment & Cohort Analyses
Deep dives into specific cuts of your data — by geography, user type, tenure, behaviour — that reveal patterns invisible at the aggregate level.
e.g. "How Customer LTV Varies by Acquisition Channel — A Cohort Study"
📋
Survey-to-Report Conversion
You run the survey. We turn the raw responses into a structured, publishable research report — with methodology, key findings, charts, and narrative.
e.g. "State of D2C Logistics in India 2025 — Based on 500 Brand Responses"
📈
Trend & Pattern Articles
Data-backed long-form articles that explain what's changing in your market — grounded in your operational data rather than opinion or secondary sources.
e.g. "What 1M Transactions Tell Us About Changing B2B Payment Behaviour"
Example Angles

What your data could become.

These are representative examples of the kind of angles we find — based on data types we commonly work with.

ECOMMERCE · ORIGINAL RESEARCH
Returns at ZIP Code Level: The Geography of Ecommerce Loss
For: D2C / Ecommerce SaaS
Return rates vary 3× between Tier-1 and Tier-3 locations. We identify which ZIP code clusters drive disproportionate return costs and why.
📦 Source data: 200K+ return records with location, SKU, reason codes
FASHION RETAIL · BENCHMARK REPORT
Fashion Retail Performance Index: Who's Growing, Who's Not
For: Retail Analytics SaaS
Category-level performance across 50+ fashion brands — which segments (ethnic, athleisure, kidswear) outperformed in each quarter and what drove the delta.
🛍️ Source data: Brand performance data across categories and channels
PAYMENTS · DATA INVESTIGATION
How Payment Timing Predicts Customer LTV — A Transaction Study
For: Fintech / Payments SaaS
Customers who pay within 24 hours have 2.4× higher LTV. We explore what transaction timing reveals about intent, quality, and retention across cohorts.
💳 Source data: 1M+ payment transactions with timestamps and customer IDs
Why DataVolt

A data firm that
learned to tell stories.

Not a content agency that learned about data. The difference matters enormously in practice.

Most content agencies will interview your team and paraphrase what they say. We go to the data directly, run the analysis ourselves, and find insights your own team may have missed.

  • 🔍
    We analyse before we write
    Every piece starts with EDA — exploratory data analysis. We don't take your team's word for what's interesting. We find it ourselves.
  • 🔒
    Full confidentiality, always
    We sign NDAs before seeing any data. Aggregated findings are published; raw records never leave our secure environment.
  • 🎯
    We write for technical buyers
    Your audience is analytical. They spot weak methodology instantly. Our reports are rigorous — sample sizes, caveats, and all.
  • 📣
    Built to get shared
    We design for virality among your buyer persona — data points that prompt a "send this to my team" reaction.
What goes into each piece
Data Cleaning Exploratory Analysis Statistical Testing Segment Cuts Anomaly Detection Narrative Structure Executive Summary Data Visualisation Methodology Section Brand Voice Alignment Distribution Strategy
Verticals we've worked with
D2C / Ecommerce FMCG Distribution HR & People Analytics Astrology / Consumer Tech Social Media Platforms Retail & Fashion Fintech / Payments
Legend
tagData work
tagWriting work
tagBoth
Get Started

Share a dataset.
We'll show you the story inside it.

Send us a description of your data — what it covers, how many records, what business you're in. In 48 hours we'll come back with 2–3 story angles and a rough scope.

"We had two years of return data sitting in our warehouse. DataVolt found a geographic pattern our entire team had missed — and turned it into a report that became the most-shared piece of content we'd ever published."

— Head of Marketing, D2C Brand (India)

NDA signed before we see any data · 48hr turnaround on story angles

Data Applications · Custom Built

Your data is ready.
Your tools aren't.

DataVolt builds custom data applications that connect your databases, APIs, and files into one reliable process — with the calculation accuracy your business decisions depend on.

100%
calculation accuracy
1-click
results, always
10×
faster than manual
commission_calculator.app — DataVolt
Total Payout
₹8.4L
▲ 12% vs last
Agents
142
Processed ✓
Accuracy
100%
No errors
Monthly Commission Trend
Rajesh Kumar
₹42,500
Paid
Priya Sharma
₹38,200
Paid
Amit Verma
₹29,750
Pending
processed in
2.3 seconds
We connect to
MySQL / PostgreSQLREST APIsExcel / CSVShopifyZoho CRMGoogle SheetsTallyCustom Sources
The Problem

Your data lives in
five different places.

Your CRM has the sales data. Your database has the transactions. Your finance team has the Excel. And someone has to manually stitch it together — every month, every quarter.

The real risk isn't the time lost. It's the calculation errors that slip through, and the decisions made on numbers nobody can fully trust.

  • Data scattered across databases, APIs, files, and spreadsheets
  • Manual exports and formula-heavy Excel as the glue
  • Calculation errors in commissions, billing, or reconciliations
  • No single source of truth — every team has a different number
  • Processing takes days; decisions wait on the numbers
How it works today — without DataVolt
🗄️
CRM Database
MySQL export
🔌
Shopify API
Manual pull
📋
Finance Sheet
Excel / CSV
📧
Email Reports
Copy-paste
manual stitching every cycle
⚠️
commission_Q3_FINAL_v7.xlsx
1 person · 2 days · every month · error-prone
delayed, unreliable output
Wrong commissions
Billing disputes
Stale decisions
How It Works

Set it up once. Run it forever.

We translate your business rules and data sources into a reliable, repeatable application.

1

We Map Your Process

We understand where your data lives — databases, APIs, files — and exactly what your business rules require. Every calculation, every edge case, documented.

DiscoveryData MappingRule Design
2

We Build the Application

We connect your data sources and build an application that pulls, processes, and calculates with precision. One run, correct output, every time.

API IntegrationsDB ConnectorsCustom Logic
3

You Run It. We Evolve It.

Your team operates the application independently. As your rules change or new sources are added, we update it. You scale, we keep pace.

HandoffTrainingOngoing Support
Use Cases

Complex calculations. Any data source.

Whether your data lives in a database, API, Excel, or all three — DataVolt processes it reliably.

💰

Commission Calculators

Multi-tier, role-based commission structures pulling live data from your CRM or database — calculated automatically, every period.

🤝

Revenue Sharing

Split revenues across partners or agents with complex attribution logic connected directly to your transaction database.

🏷️

Pricing Calculators

Dynamic pricing engines with discount rules, volume tiers, and margin floors — fed from your product catalog or database in real time.

📊

Usage-Based Billing

Pull usage data via API, apply your rate cards, and generate reconciled billing outputs — without a single manual step.

🔄

Reconciliations

Connect to ERP, bank feeds, CRM simultaneously — match, flag, and resolve discrepancies automatically.

📈

Sales Schemes & Incentives

Complex slab-based, retroactive, SKU-level scheme calculations — from Excel upload, Tally export, or a live database.

100%
Calculation accuracy — every run, every time. No more hunting for the one wrong formula that cascaded into 300 rows.
10×
Faster than manual Excel processing for large datasets
Rows — no crashes, no slowdown, no file size limits
Why DataVolt

One application.
All your sources. Zero errors.

  • 🔌
    Connects to Any Data Source
    Databases, REST APIs, Excel/CSV, ERP exports — wherever your data lives.
  • 🎯
    Your Exact Business Logic
    Every tier, every exception, every edge case encoded precisely.
  • 🔍
    Full Audit Trail
    Every calculation logged with inputs and outputs — always defensible.
  • 💸
    Fixed-Scope, Fixed-Price
    No SaaS subscriptions. Scoped engagements that deliver a working application.
Case Studies

Built for real businesses.

From HR analytics to FMCG reconciliations.

FMCG Distribution

Transaction Classification System

A mid-size FMCG distributor needed to classify bank transactions across costs, payments, and sub-categories — with multi-UPI routing making reconciliation complex.

What we delivered

Automated classification on upload — reducing manual effort
Multi-source UPI payment handling across company, sales, and receivables
Customisable categories with review and override capability
↓80%
manual effort
Auto
classification on upload
Computer Vision

Visual Fabric Similarity Search

A handloom fabric brand needed to find visually similar fabrics from a large swatch library — for designers finding replacements for out-of-stock items.

What we delivered

CV model identifying close alternatives based on colour, texture, and weave
Upload a swatch, retrieve the closest matches instantly
Scalable pipeline across the full product catalogue
Auto
substitute discovery
CV
colour + texture matching
Get Started

Tell us where your
data bottleneck is.

Whether it's commissions in Excel, billing stitched across three APIs, or a reconciliation that costs your team two days every month — in 30 minutes we'll show you what a DataVolt application would look like.

No commitment · No sales deck · Just an honest conversation

AI Readiness · For Growing Businesses

AI is only as good as
the data feeding it.

Most businesses aren't failing at AI because of the model. They're failing because their data is scattered, dirty, and unstructured. DataVolt fixes the foundation first — then helps you identify where AI actually creates value.

Your AI Readiness Journey
🗄️
Data Foundation
Clean pipelines · Structured storage
Complete ✓
🔌
Source Connectors
DB · APIs · Files unified
Complete ✓
🧭
AI Use Case Mapping
Identifying real opportunities
In Progress
🤖
AI Layer Integration
Models · Forecasting · Intelligence
Up Next
The AI Readiness Gap

Why most AI projects
fail before they start.

Businesses rush to buy AI tools only to find the output is unreliable. The bottleneck is almost never the AI. It's always the data underneath — fragmented, inconsistent, and not structured for machine consumption.

Before any AI can work reliably, four things need to be true about your data.

📍
Centralized
All relevant data in one place — not split across ten exports and three team inboxes.
🧹
Clean
Consistent formats, no duplicates, no missing fields. AI on dirty data produces dirty predictions.
📐
Structured
Data modelled in a way that's machine-readable — not optimised for human eyeballs in Excel.
📅
Historical
Sufficient history to train, validate, and trust a model. Most SMBs have never stored data systematically.
Our AI Readiness Framework

A structured path from data chaos to working AI.

We don't start with the AI layer. We start with the data layer — and work up.

1

Data Audit

Map every data source, assess quality, identify what's missing or unreliable.

Source inventory
Quality scoring
Gap identification
Readiness report
1–2 weeks
2

Data Foundation

Build clean pipelines, structured storage, and unified connectors.

Pipeline build
Source connectors
DB schema
Historical backfill
3–6 weeks
3

AI Use Case Mapping

Identify which processes are genuinely AI-ready — and which aren't yet.

Process analysis
ROI estimation
Prioritised roadmap
Honest no-go flags
1 week
4

AI Layer Build

Integrate the right AI — classification, forecasting, anomaly detection, or LLM.

Model selection
Integration & testing
Team handoff
Monitoring setup
4–8 weeks
What AI Can Do For Your Business

Practical AI. Not science projects.

AI applications that make real, measurable difference — grounded in the verticals we work with.

🧾

Smart Transaction Classification

Automatically classify bank transactions into cost categories, payment types, and sub-buckets — with human review only for exceptions.

FMCG · Finance · Distribution
📈

Demand Forecasting

Predict demand at SKU and channel level using historical sales, seasonal patterns, and external signals.

D2C · FMCG · Retail
🚨

Anomaly Detection

Flag billing irregularities, reconciliation mismatches, or unusual patterns before they become disputes.

Finance · Ops · Sales
💬

AI-Assisted Reporting

Ask questions about your data in plain language and get structured answers — no SQL, no pivot tables.

Leadership · Finance · Ops
📄

Document Intelligence

Extract structured data from PDFs, invoices, contracts, and delivery challans — fed into your data pipeline.

Procurement · Legal · Finance
🔮

Churn & Retention Prediction

Identify customers at high churn risk before they leave — based on behavioural patterns in your data.

D2C · SaaS · Subscriptions
Our Commitment

We'll tell you when
you're not ready.

The AI consulting space is full of firms that will sell you a model before you have the data to support it. We won't. Our job is real ROI — and that sometimes means telling you what not to do first.

🚫
We don't sell AI you don't need
If your process doesn't have sufficient data or clear ROI from AI, we'll tell you upfront and suggest what to fix first.
🚫
We don't promise AI on broken data
Every AI engagement starts with a data audit. If the foundation isn't right, we fix that first — no exceptions.
We build for your scale
You don't need a data science team or a ₹50L budget. We scope AI projects to your actual size.
We hand it over and train your team
AI shouldn't be a black box. We document everything, train your team, and make sure you're not dependent on us forever.
Free Assessment

Find out where you stand.
In 45 minutes.

Our AI Readiness Assessment is a structured conversation — not a sales pitch.

What you'll walk away with
A clear picture of your AI readiness — at no cost.
Data source inventory & quality assessment
2–3 specific AI opportunities for your business
Honest view of what needs fixing first
Rough effort & cost estimates
No vendor lock-in recommendations
A written summary you can share internally

45-minute call · No commitment · Written summary included