Tell us your problem
AI & Business Consulting

Your data in.
A moving needle out.

For leaders whose AI pilots haven’t yet moved a business number.

Book a 30-minute callWhy not do it yourself?
Playing muted
The 2-minute story: from the AI race to a result you can see.
  1. The raceEvery business is in a constant race to be better.
  2. The questionDo we really know if AI is doing us any good?
  3. The research88% of companies use AI. Only 39% see it in their profits.McKinsey, State of AI 2025
  4. The riskUnmonitored, AI can cost more than the person it replaced.
  5. The researchOnly 25% of AI initiatives delivered the ROI expected.IBM CEO Study 2025
  6. The partnerA partner who takes on the uncertainty, and still delivers.
  7. What it means for youYou buy an outcome, not an experiment.
  8. The research95% of AI pilots show little or no P&L impact.MIT NANDA, The GenAI Divide 2025
  9. What we believeAnswers are cheap. Questions are the asset.
  10. What we believeYour data is the moat. The algorithm isn’t.
  11. The research30%+ of GenAI projects forecast to stall after the pilot.Gartner forecast, 2024
  12. What we believeThe needle, not the model.
  13. What it means for youEvery dollar tied to a result you can see.
  14. How it worksData in. Result out.
  15. Automation & AI agentsHand the busywork to AI. Keep people on the decisions.
  16. The researchAI leaders put 70% of the effort into people and process.BCG, 2024
  17. What it means for youNo migration. No new screens. A sharper team.
  18. The truthInsight takes seconds. Change takes weeks.
  19. The resultOne fix. Every number moves.
  20. The resultSame leader. Same AI. Now it pays back.
01 — Why hire us
Why hire Dsciencepro

Prompting is an experiment. Integration is a result.

Any leader can ask ChatGPT or Claude a question today. But the goal was never an answer — it was an objective hit. And every hour you spend prompting is an hour not spent growing the business.

On your ownOne person, one chat window
Export copy · paste Chat Thanks! 👍 KPIspikes, then slips back
  • Someone exports and pastes data by hand, every time
  • A good answer — but nothing changes in your systems
  • Your week disappears into prompting instead of growing the business
  • Nobody can say whether it helped
With DscienceproYour systems, every day
CRM ERP Support desk Sheets AI + your data, inside your tools every day KPIclimbs and holds
  • Reads your systems directly — nobody copies anything
  • Decisions land in the CRM, inbox or report your team already uses
  • Your time goes back to customers, people and growth
  • A before-and-after you can show your board
Exhibit 1 · How it works

Five steps. Two are yours.

You bring the data and act on the signal. We handle everything in between — and keep the loop running.

YOU
01Your data

Whatever you run today. No migration, no new platform to learn.

DSCIENCEPRO
02Integrates

Cleaned, modelled and governed in one pipeline.

AI
03Interprets

Models and agents forecast, score, flag and recommend the next action.

DSCIENCEPRO
04Measures & tracks

A baseline before launch, a target after it, a scorecard in between.

YOU
05Move the needle

Your team acts on sharper signals. The number moves.

What we measure decides what we integrate next.
03 — The problem
We understand the problem

Sound familiar?

The four problems we’re asked to solve most often — and what AI does about each one.

Target Q1Q2Q3Q4 gap
Sales vs target, by quarter
Sales

Sales miss target, quarter after quarter

The pipeline looks healthy, but deals stall and discounts creep up.

What AI doesPinpoints which products, regions and deals are leaking revenue.
left quietly
Example: 3 in 18 customers leave without a word
Customer engagement

Customers leave without telling you

By the time they cancel, the warning signs are months old.

What AI doesFlags at-risk customers early from usage, tickets and feedback.
Survey:“all fine” warning signs Resigns Next survey JanAprDec AI flags here
January’s survey said “all fine”. She resigned in April.
Employee engagement

Good people leave before you see it coming

An engagement survey is a once-a-year snapshot. Overtime, pay gaps and mood shifts build up in between — and no one is watching.

What AI doesWatches workload, pay and sentiment data and flags who’s at risk while there’s still time to act.
A typical analyst’s month Building reports Deciding ~2/3 of time~1/3
Where the time goes
Operating cost

Skilled teams stuck building reports

Days every month go into copying data between spreadsheets.

What AI doesAutomates the reporting so people spend their time on decisions.
Our point of view

What we believe about AI in business.

Three beliefs shape every engagement — and what we say no to.

answer answer answer answer answer answer ?
01

Most sayThe right answer wins.

We say

Answers are cheap. Questions are the asset.

AI made analysis fast and cheap. The advantage now belongs to whoever asks the right business question first.

YOUR DATA same AI same AI same AI
02

Most sayThe best algorithm wins.

We say

Your data is the moat. The algorithm isn’t.

Every competitor can rent the same AI. What they can’t copy is your data, joined up and put to work.

MODEL ACCURACY98% BUSINESS NUMBER
03

Most sayA 98%-accurate model is a success.

We say

The needle, not the model.

A 98%-accurate model that changes nothing is a cost. We judge our work by the business metric alone.

What we say no toAI for its own sakeAccuracy scores with no business numberPilots with no ownerPlatforms you have to migrate to
05 — Credentials
Why clients trust us

A track record you can check.

9,000+hours on client problems
300+projects delivered
100%Job Success on Upwork
Top Ratedon Upwork
Background

Accenture (Pinnacle Award) · Hewitt Associates · Upwork Inc. · IMA India

Verify it yourself

Upwork profile & reviews ↗ · LinkedIn ↗

Services

Six ways we move your numbers.

Every service starts with a business problem and ends with a result you can measure.

Every service is measured on one of five numbers · tap one
Scenario · Sales

Discounts were eating the margin

A distributor’s average discount crept from 8% to 17% in one quarter. The engine showed 11 deals in one region leaking margin, and the cause was late deliveries, not price.

Before17% average discount→AfterBack toward 8%
Scenario · Customers kept

Good customers left without a word

A software firm only found out at renewal. Usage, support tickets and survey scores now flag at-risk accounts months earlier, while there is still time to act.

BeforeNo warning→After3 months’ warning
Scenario · People retained

The best analyst resigned “for a better offer”

Workload, missed 1:1s, declined leave and pay against market were all visible weeks before. Now they raise a flag while a manager can still step in.

BeforeFound out at resignation→After8 weeks’ warning
Scenario · Cost to run

Every new customer added cost

Process analytics showed where work waited, repeated and bounced between teams. Automating the repeats brought down the cost of serving each customer.

BeforeWork repeated across teams→AfterRepeats automated
Scenario · Manual hours

Month-end took four days

ERP exports, six spreadsheets and emails chasing managers. Now an agent builds the report overnight and people only review the exceptions.

Before4 days a month→AfterHalf a day
Illustrative scenarios. Real numbers depend on your data.
>
goal
01

AI Strategy & Readiness

Where AI will pay back in your business — and where it won’t.

You getA prioritised AI roadmap tied to your P&L
SalesCost
02

Data Science & Analytics

Dashboards, forecasts and models that explain what’s driving your numbers.

You getLive dashboards and predictive models your team uses
SalesCustomers
AI
03

AI Integration & Agents

LLM assistants and research agents built into the tools you already use.

You getA working AI agent, deployed and owned by you
See it for your industry →
CostEmployees
InboxAIDonehumans handle exceptions
04

Process Automation

Hand repetitive work to AI, keep people on the decisions.

You getAutomated workflows with a human checkpoint
See it for your industry →
CostEmployees
05

People Analytics

Engagement, attrition and pay insight — before good people leave.

You getAttrition signals, engagement drivers, pay benchmarks
EmployeesCost
06

Research & White Papers

Evidence-based research your board and market will trust.

You getBoard-ready research and thought leadership
SalesBrand
Automation & AI agents

Hand the busywork to AI. Keep people on the decisions.

We connect the tools you already run, automate the repetitive steps, and build AI agents that read, decide and act, with a person signing off wherever it matters.

I run abusiness and my problem is

A person signs off on every decision that matters.This is my problem →
01 · Workflow automation

No more copy, paste and chase

Repetitive steps across your tools, done automatically and reliably.

  • Leads captured, enriched and routed to the right person
  • Forms, invoices and emails read and keyed in for you
  • Weekly reports built and sent before Monday
  • Data synced between CRM, sheets and finance tools
Google Apps ScriptPythonREST APIsZohoSlackGoogle Workspace
02 · AI agents

Assistants that do the work

Agents that read, reason and act inside the tools your team already opens.

  • Research agents that produce client-ready reports
  • Inbox and enquiry triage, with drafted replies
  • Browser agents for repetitive web tasks
  • Slack assistants that answer from your own data
Claude APIMCP toolsPlaywrightStreamlitFastAPIFlask
03 · Data & integration

One version of the truth

Your systems connected, so the numbers agree and the AI has something real to work with.

  • Pipelines from CRM, ERP, e-commerce and public data
  • Dashboards leaders actually use
  • Forecasts and scoring models
  • Clean, documented data your team owns
SQLPostgreSQLPower BIGoogle CloudZoho AnalyticsAmazon & SAM.gov APIs
Built to be safe to runA human checkpoint wherever a mistake would cost you, and access only to the systems the job needs, under NDA.
How to engage

Start small. Scale what works.

1–2 weeks

Diagnostic

We find the number worth moving and test whether AI can move it.

4–6 weeks

Build sprint

We build the tool or model, deploy it and measure the change.

Ongoing

AI partner

A monthly partnership to keep improving the numbers that matter.

Results

Real problems. Real work.

Before and after, from real engagements.

Before → after
A US insurance services firmCustomer service · Conversation intelligence
Before

Conversations about claim-form submissions sat as unstructured data. No one could see where filings went wrong.

After

AI conversation intelligence turned them into a learning, development and efficiency engine, and a long-term strategy.

→
Coaching · process · strategy
A biotech manufacturer selling in the US, Canada and EUSocial media · AI advisor
Before

Plenty of social media effort, but no way of knowing what actually moves page views and enquiries.

After

A research-backed AI advisor recommends the next move and tracks whether each one hit its target.

  • Recommendation ✓ attained
  • Recommendation ✓ attained
  • Recommendation tracking
A US government-contracting consultancyPublic sector · Opportunity intelligence
Before

Federal opportunities were hunted by hand on SAM.gov, one listing at a time.

After

A tool that scores every opportunity against the firm’s sector codes, certifications and skills, and surfaces sub-contracting leads.

Opportunity92
Opportunity74
Opportunity38
A large US IT solutions providerTechnology · AI adoption
Before

AI licences were rolled out, but no one could say who was really using them, or why not.

After

Adoption measured team by team, with change-management tools to lift usage where it lagged.

Weekly active use, by team
A compensation consultancyReward · Pay benchmarking
Before

Pay decisions were being made without a reliable view of the market.

After

Benchmarking models compare every role against market ranges, for fair and competitive pay.

Roles settling into the market range
A global online talent marketplaceMarketplace · Customer experience
Before

Leadership needed to know what really drives customer experience and loyalty.

After

18 months leading customer-experience analytics: insight, reporting and decision support.

Visitors → sign-ups → first project → repeat
Client names withheld by design. NDAs and confidentiality are respected on every project.
research_agent
Assess market entry for a B2B SaaS in Germany
Running 22 frameworks…
PESTLEPorterSWOTTAM/SAM+18
PPTXPDFMarkdown
ConsultingAI agent

A strategy consulting firm · AI strategy research agent

Challenge: Strategy research took consultants days of desk work per question.

What we did: Built an AI research agent that applies 22 strategic frameworks and exports client-ready decks and reports.

Claude APIPython · FlaskRender
copilot_adoption
Sales
78%
Finance
61%
Operations
44%
HR
52%
Illustrative view · weekly active use by team
TechnologyAI adoption

A large US IT solutions provider · Microsoft Copilot adoption assessment

Challenge: Licences were rolled out, but no one could say who was really using AI — or why not.

What we did: Designed AI adoption metrics and change-management tools to track and lift usage.

Adoption metricsSurvey analyticsChange management
pay_benchmark
market range vs your position (illustrative)
RewardPeople analytics

A compensation consultancy · Compensation benchmarking

Challenge: Pay decisions were made without a reliable view of the market.

What we did: Built benchmarking models comparing roles against market ranges to support fair, competitive pay.

BenchmarkingStatistical modellingExcel / BI
cx_insights
Visitors
Sign-ups
First project
Repeat clients
Illustrative funnel
MarketplaceCustomer experience

A global online talent marketplace · Customer experience analytics

Challenge: A global marketplace needed to understand what drives customer experience and loyalty.

What we did: Led business analytics for the Customer Experience function for 1.5 years — insight, reporting and decision support.

CX analyticsSQLDashboards
How we work

We fit into your work. Not the other way round.

We listen first, learn how your team likes to work, respect your time and your calendar, and stay alongside you as part of the team until the number moves.

Before any work starts, you’ll know
  • The scopeWhat we’ll build, and what we won’t
  • The timeHow long each stage takes
  • The expected outcomeThe number we expect to move
Shared in writing, before you commit.See the four stages ↓
>>
The process

We listen first. Then we show you what AI can move.

Four stages, and something you walk away with at each one. Scope, time and the expected outcome are shared in writing before any work starts.

  1. 01

    We listen

    We start by listening, not pitching. You tell us what’s hurting, in your own words, and we ask where the work and the data live today.

    You walk away withYour pain points, written back to you
    Why it works ↓
  2. 02

    We learn how you work

    Your culture, your tools, your calendar. We learn how decisions get made and assess where AI and automation can genuinely help, and where they can’t.

    You walk away withAn honest view of what can move, and what can’t
    Why it works ↓
  3. 03

    We agree, in advance

    Before you commit, you get it in writing: what we’ll build, how long each stage takes, and the outcome we expect.

    You walk away withA clear scope, timeline and expected outcome
    Why it works ↓
  4. 04

    We work alongside you

    Built into the tools you already use, with check-ins at times that suit you. We measure before and after, and stay until the number moves.

    You walk away withA partner on your side, and a result you can measure
    Why it works ↓
Book a free 30-minute callTell us your problem →

Prefer hourly or ongoing support? We also work on a monthly retainer or through Upwork.

Why our way works

We didn’t make this up. The research agrees.

The four stages above are how we work. Here is why: two independent studies behind each one, with links if you’d like to read them yourself.

Stage 02We learn how you work
70%of AI challenges are about people and process

Only 10% come from the algorithms. The rest is how people, processes and technology fit together.

BCG · Where’s the value in AI? · 2024Read the study
Also

Projects with excellent change management are about 7× more likely to meet their objectives (88% vs 13%).

Prosci research
That’s why we learn how you work.
Stage 03We agree, in advance
30%+of GenAI projects abandoned after the pilot

Among the reasons Gartner gives: unclear business value and escalating costs.

Gartner · 2024Read the study
Also

Tools stall when they don’t learn from or adapt to the way people work.

MIT NANDA · 2025, via Fortune
That’s why scope, time and the expected outcome are agreed before you commit.
Stage 04We work alongside you
67%success rate with a specialist partner

AI bought from specialised vendors or built through partnerships succeeds about 67% of the time. Internal builds succeed only a third as often.

MIT NANDA · The GenAI Divide · 2025, via FortuneRead the study
Also

About 3 in 4 AI high performers fundamentally redesign their workflows. Among everyone else, it’s 1 in 4.

McKinsey · The state of AI
That’s why we work alongside you, inside your workflow.
A note before we talk

Thank you for being here. Before you tell me anything, I’d like you to know how I’ll work with you.

I’ll listen more than I talk. I’ll learn how your team works before I suggest any change, because the best idea is useless if it doesn’t fit your culture.

Your time matters as much as mine. Calls happen in your business hours, with an agenda, and they stay short. You’ll have the scope, the time and the expected outcome in writing before we start.

And I won’t disappear after the handover. I’ll work alongside you, as part of your team, until the number moves.

Ashish MishraFounder, Dsciencepro
  • We listen before we suggestThe first call is about your problem, not our services.
  • We fit your cultureYour tools, your language, your approval steps. We adapt to them.
  • We respect your timeCalls in your business hours, with an agenda, kept short.
  • We become part of your teamOne familiar person from the first call to the result, on your side until the number moves.
Questions clients ask

Good to know before we talk.

What does it cost?

The first call is free. After that, the price is agreed in writing together with the scope, the time and the expected outcome, before any work starts — no surprise invoices.

Is our data safe?

We work inside your environment wherever possible, sign an NDA on request, and never use your data to train public models.

Do we need clean data or a data team?

No. Cleaning and joining data is part of the work. We tell you up front if the data can’t answer the question.

Who actually does the work?

Ashish leads every engagement personally. AI speeds up the analysis; a human checks and signs off every conclusion.

What if AI can’t move our metric?

We’ll say so on the first call or when we assess it — before you spend on a build.

Which tools do you use?

Whatever fits your stack: Python, SQL, Power BI, Looker Studio, Excel, and LLMs such as Claude and GPT.

Dsciencepro Research

Thinking you can use today.

White papers, short articles for decision makers and free guides. New pieces added regularly. Free to read, no email wall.

Dsciencepro Research · added regularly
  1. W
    White papersDeeper research on AI, data and people
  2. A
    Articles for decision makersShort reads, one idea each
  3. G
    Free guidesStarting with AI readiness
Get each new piece in your inboxOne email when we publish. No newsletter noise.

About Dsciencepro

Business first. AI second.

Dsciencepro is an AI integration partner for growing businesses. We connect AI to the data you already keep, and judge the work by one number you run on: sales, customers kept, people kept or cost.

What Dsciencepro solves
  • Sales are stuckAI flags the deals leaking margin, every Monday
  • Customers drift awayEarly warnings, before they cancel
  • Good people quitRisk signals while there’s still time to act
  • Reports eat the weekThe reporting runs itself

One business number moves, or we tell you why.

Why Dsciencepro

Expert-grade thinking, for any business with data.

My philosophy

Technology made the world small.

A business anywhere can work with an expert anywhere.

Data is the key.

The records you already keep unlock every answer.

AI is the enabler.

Expert-grade recommendations, for the corner shop and the corporation alike.

You no longer need a room full of elite consultants to solve your problem. You need your data, and AI that reads it.

How we work
  1. 1Baseline the number. Before any AI, we measure where you are.
  2. 2Wire AI into your tools. No migration. It reads the data you already keep.
  3. 3Scorecard after. The number moved, or we tell you why. You own everything we build.
Cases & impact

Problems we’ve solved, and what changed.

Consulting · AI agent

A strategy consulting firm

AI strategy research agent
Problem
Strategy research took consultants days of desk work per question.
Built
An AI agent that applies 22 strategy frameworks and exports client-ready decks.

What changedFrom days of desk research to a framework-backed deck, on demand.

Technology · AI adoption

A large US IT solutions provider

Copilot adoption assessment
Problem
Licences were rolled out, but no one could say who was really using AI, or why not.
Built
AI-adoption metrics and change-management tools, measured against business and workforce numbers.

What changedLeaders see adoption by team, and what is holding the rest back.

People analytics · Reward

A compensation consultancy

Compensation benchmarking
Problem
Pay decisions were made without a reliable view of the market.
Built
Benchmarking models that place every role against market ranges.

What changedPay decisions start from the market, not a guess.

Marketplace · Customer experience

A global online talent marketplace

CX analytics, full-time contract
Problem
A global marketplace needed one view of what drives customer experience.
Built
Led CX analytics for 18 months and moved support reporting from Zendesk into the data warehouse.

What changedLeadership reporting on one source of truth, with no break in service.

Client details stay confidential; measured results are shared on a call.

About the founder
AM

Ashish Mishra

Founder & Principal Consultant

Top Rated on Upwork100% Job Success

An economist by training and an analyst by trade. I don’t count years, I count hours: 9,000+ of them spent on real client problems, turning data into decisions for consulting firms, global platforms and growing businesses.

How I work
The world got smaller.Technology put every market within reach. Data is the way in.
AI standardises expertise.You no longer need an elite-university team to get a sound recommendation. AI can put it on the table.
Hours, not years.Experience is the time spent solving real problems, not the time on a CV.
Read my articles on LinkedIn →
The journey

From 2005 to today.

Full-time, part-time, independent, contract and founder: each stop is marked for what it was, and Upwork has run alongside since 2009.

0hours
0projects
0job success
Start
Today
2009 first $30 · paused while employed, 2010–13
Upwork, still with meTop Rated · 100% Job Success · 300+ projects
Full-time · start2005IMA IndiaResearch Analyst
Independent2006IHC InvestConsultant
Part-time2008CanadaCapilano · KIKA
IndependentMay 2009oDeskFirst $30, in a recession
Full-time2010Hewitt AssociatesAssociate
Full-time2011AccentureAnalyst · Pinnacle Award
IndependentDec 2013UpworkIndependent contractor
ContractJul 2019Upwork Inc.Analytics Lead, CX
Founder · today2019DscienceproFounder · AI since 2022
Full-time employeePart-timeIndependentFull-time contractFounderUpwork lane, from May 2009
2005IMA India

Full-time employee. Research and C-suite surveys.

2006–07IHC Invest

Full-time independent consulting with patent owners and investors.

2008Canada

Part-time: Capilano University on campus, as and when, and KIKA off campus. Learned Canadian work culture.

2009The seed

Back in India in a deep recession. In May 2009, the first oDesk project: $30.

2010–13Hewitt Associates, Accenture

Full-time in consulting, serving North American and European clients.

2013 → todayUpwork, then Dsciencepro

Top Rated, 300+ projects. Founded Dsciencepro in 2019; full-time contract with Upwork Inc., Jul 2019 to Dec 2020.

9,000+hours delivered on Upwork
300+projects on Upwork
Jul 2019 – Dec 2020full-time contract, Upwork Inc.
2008part-time in Canada, learning its work culture
Since 2019Same promise, new tools.

The technology changes every few years. The question doesn’t: did the number move?

2019–21Cloud analytics and prediction

Dashboards on GCP and Power BI; machine-learning models that forecast

2022AI APIs on business data

Large language models wired into the data, turning it into insight

2024Measuring AI’s real impact

AI adoption tracked against business and workforce numbers

2025AI agents and automation

Tools that read, decide and act, built for clients and for Dsciencepro

TodayAI that moves a number

Integrated into your tools, judged on the business result

The skill bucket

What I bring, and where I learned it.

Every stop added one skill, and the newest layers are AI. Nothing has been poured out since.

since 2005
  1. 2024to todayAI integration, automation and impactAI built into everyday workflows, automated, and measured in business results
  2. 2022AI APIs on business dataDsciencepro: insight from data, tied to a business number
  3. 2019Cloud analytics and predictive modelsDsciencepro: GCP, Power BI, machine learning
  4. 2019Leading analytics for a global companyUpwork Inc., full-time contract
  5. 2013Delivering alone, end to end300+ Upwork projects since Dec 2013
  6. 2011Workforce and talent analyticsAccenture
  7. 2010Survey and engagement analyticsHewitt Associates
  8. 2009Winning and serving clients remotelyoDesk, from a first $30 project
  9. 2008Reading a new marketCanada, KIKA Inc. (part-time)
  10. 2006Negotiating and running a budgeta venture, IHC Invest
  11. 2005Asking the right questionsmarket research, IMA India
Why a stranger can trust this
  • ✓
    Top Rated on Upwork100% Job Success scoreCheck the profile ↗
  • ✓
    300+ projects deliveredFor clients including Airbnb, CDW and Michael PageRead client reviews ↗
  • ✓
    Hired by Upwork itselfFull-time contract, Jul 2019 to Dec 2020, as Business Analytics Lead, CX, on the strength of a consistent Top Rated record
  • ✓
    Pinnacle Award, AccenturePlus co-author of an Accenture Institute for High Performance white paper
  • ✓
    Published on ExperfyTalent Analytics: The Art of Predicting Employee RetentionRead the article ↗
  • ✓
    Udyam-registered MSMEGovernment of India · UDYAM-UP-29-0159650
Legal

Privacy policy.

Last updated 18 October 2026.

Who we are

Dsciencepro is a Udyam-registered micro enterprise in India (Udyam No. UDYAM-UP-29-0159650). This policy explains what we collect through dsciencepro.com and how we use it.

What we collect

When you use the contact form we ask for your name, work email and, optionally, your company and a note, plus the problem you picked. We do not use advertising trackers on this site.

Why we collect it

To reply to your enquiry and, if you agree, to talk about related work. We do not sell or rent your details.

How long we keep it

Until you ask us to delete it, or for up to two years after our last contact, whichever comes first.

Your choices

You can ask to see, correct or delete your details at any time by messaging us on LinkedIn.

Third parties

Booking links open Google Calendar, and LinkedIn and Upwork links open those sites, each under their own privacy policies.

Legal

Terms of use.

Last updated 18 October 2026.

Content

All content on this site, including text, graphics, video and the Dsciencepro name and logo, belongs to Dsciencepro. All rights reserved. You may share links and quote short passages with credit.

Illustrations

Charts and engines marked “illustrative” show the kind of change we design for and are not client results.

No advice

Content on this site is general information, not professional advice for your situation. Engagements are governed by a separate written agreement.

Law

These terms are governed by the laws of India.

Work with us

Two ways to hire. One partner who listens.

Contract with Dsciencepro directly, or through Upwork if you prefer its contracts, escrow and payment protection. Either way, the work is led by Ashish Mishra, backed by 9,000+ hours of hands-on client work in data, analytics and AI.

Direct

Hire directly

Best for ongoing work and larger scopes. Agree the problem first, then a fixed scope and price, before any work begins.

  1. 1We listen
  2. 2We assess
  3. 3Scope & outcome agreed
  4. 4Work begins
Calls in your business hours
New York--:--
London--:--
Sydney--:--
What you get directly
  • Fixed price agreed upfront, so no surprise invoices
  • NDAs and client confidentiality on every project
  • Led by Ashish, from the first call to the result
Book a 30-minute call →
Many clients hire directly, beyond the 300+ Upwork projects.
Upwork

Hire on Upwork

Prefer a platform you already use? Same work, same person, with Upwork’s protection around it.

Top Rated on Upwork
  • 300+projects on Upwork
  • 100%Job Success
  • 9,000+hours delivered on Upwork
What Upwork adds
  • Payments held in escrow
  • Milestone billing
  • Verified client reviews
  • Identity verified
2009joined oDesk2015oDesk becomes UpworkTodaystill delivering
View profile & reviews on Upwork →
Identity verified on Upwork · Upwork also hired Ashish directly for an 18-month analytics engagement.
Start here

Tell us what’s hurting. We’ll show you what AI can move.

1 · What’s the biggest problem right now?
Sounds like you? Tick all that apply.
Other
Sounds like you? Tick all that apply.
Other
Sounds like you? Tick all that apply.
Other
Sounds like you? Tick all that apply.
Other
Sounds like you? Tick all that apply.
Other
Sounds like you? Tick all that apply.
Other
Tell us in your own words
2 · How do we reach you?

We reply personally — no sales pitch, no mailing list.

The story we hear every weekAI engine · live

Lead-finding engine

A typical month for a small sales team

  1. Week 1400 contacts in the CRM. Nobody knows who to call first.
  2. Week 2Reps work through an old trade-show list. Most never reply.
  3. Week 3Two inbound demo requests sit unanswered for three days.
  4. Month endNo new deals. A competitor wins the warm lead you missed.
What the engine shows on Monday of week 1
!
Call these 20 accounts todayNorthwind Foods visited your pricing page 4 times this week and looks like your best customers.
Pricing page visitsMatches past winsOpened last 3 emailsNew buyer joined
Buying intent

Suggested actionYour reps start the day with a ranked call list, not a spreadsheet.

The data we’d connect
CRM contacts & dealsWebsite visitsEmail opens & repliesInbound formsLinkedIn activityPast won & lost dealsCompany size & industryIntent signals
The needle we’d move · Reply time to a warm lead
TODAY3daysWITH THE ENGINESameday

Pricing & conversion engine

A quarter we hear about every week

  1. Week 1Pipeline looks healthy: 120 open deals.
  2. Week 6Average discount creeps from 8% to 17%.
  3. Week 9Deals in the West region stall at “proposal sent”.
  4. Quarter endSales close at 82% of target. Again.
What the engine shows in week 4
!
11 deals are leaking marginWest region buyers are not objecting to price. Late deliveries are triggering the discount requests.
Discounts risingDeliveries 2+ days lateStalled at proposalSame 3 products
Revenue at risk

Suggested actionFix delivery dates for those 11 accounts before you cut the price.

The data we’d connect
CRM stages & notesQuotes & discountsOrder historyDelivery & ops dataCall notes & transcriptsProduct mixCompetitor pricingRep activity
The needle we’d move · Average discount to close
-17%-8%TODAY17%average discountWITH THE ENGINE8%average discount

Live KPI cockpit

A week in the leadership team

  1. MondayThree managers bring three different revenue numbers to the same meeting.
  2. TuesdayFinance rebuilds the board pack by hand. Again.
  3. ThursdayA stock-out in one warehouse shows up in a spreadsheet five days late.
  4. FridayThe decision is pushed to next week “until we have the numbers”.
What the cockpit shows on Monday at 9 am
!
One number, refreshed every hourAlert: Warehouse B has less than 3 days of stock on your top-selling item.
Sales vs targetCash positionStock coverOpen support issues
Time to spot a problem

Suggested actionThe meeting starts with the same numbers for everyone, and one thing to fix.

The data we’d connect
ERPCRMAccountingInventorySpreadsheetsAd platformsSupport deskHR system
The needle we’d move · Time to spot a stock-out
TODAY5daysWITH THE ENGINESameday

Churn early-warning engine

How a good customer slips away

  1. Month 1Acme Ltd logs in every day: 40 active users.
  2. Month 3Logins drop by 60%. Two support tickets stay open for nine days.
  3. Month 4Their survey score: 6 out of 10, “it’s fine”. Your main contact changes job.
  4. Month 5Cancellation email: “We’ve gone with someone else.”
What the engine shows in month 3
!
Acme Ltd is likely to leaveUsage down 60%, two tickets unresolved and your champion has just moved on.
Usage down 60%Tickets open 9 daysChampion leftRenewal in 8 weeks
Churn risk

Suggested actionAccount manager calls this week, fixes the tickets and meets the new contact.

The data we’d connect
Product usageSupport ticketsNPS & CSAT surveysBilling & renewalsEmail & chat toneContacts changing jobsOnboarding progressFeature adoption
The needle we’d move · Warning before a customer leaves
TODAYNoneno warningWITH THE ENGINE3months ahead

Attrition-risk engine

Priya, one of your best analysts

  1. JanuaryEngagement survey: “All fine.”
  2. FebruaryShe works 14 extra hours a week, six weeks in a row. Two of her 1:1s are cancelled.
  3. MarchHer leave request is declined twice. Her pay is now 12% below market for her role.
  4. AprilShe resigns. Exit interview: “Better offer, and I was burnt out.”
What the engine shows in February
!
Priya S. · high flight riskOverload, missed 1:1s and a widening pay gap: the same pattern as the last three people who left her team.
Overtime 6 weeks running1:1s cancelledLeave declinedPay 12% below marketApplied for an internal role
Flight risk

Suggested actionRebalance her workload, approve the leave and review her pay band this month.

The data we’d connect
HR system & org chartTimesheets & overtimeLeave & attendancePayroll vs market payPerformance & promotionsManager 1:1 recordsPulse & engagement surveysExit interviewsWorkload: meetings, email, Teams/SlackInternal job applications
The needle we’d move · Warning before a resignation
EXIT!8 wksTODAYNoneno warningWITH THE ENGINE8weeks ahead

Automation engine

Month-end for your reporting team

  1. Day 1Export from the ERP, paste into six spreadsheets.
  2. Day 2Fix the formulas that broke since last month.
  3. Day 3Chase three managers by email for their numbers.
  4. Day 4The report goes out. Nobody reads past page two.
What the engine does on the night of day 0
!
Report built overnightEverything reconciled. 3 exceptions need a human to check before it goes out.
ERP pulledSheets mergedManagers’ inputs collectedCommentary drafted
Hours back each month

Suggested actionYour team spends day 1 on the three exceptions, and the other days on decisions.

The data we’d connect
ERPEmailSpreadsheetsShared drivesFormsPDFs & invoicesCRM
The needle we’d move · Month-end report
1234DAYTODAY4daysWITH THE ENGINE½a day

Your problem, your engine

How a first call with us usually goes

  1. Minutes 0–10You tell us the number that’s hurting, in your own words.
  2. Minutes 10–20We ask where the data lives today: tools, sheets, people.
  3. Minutes 20–30We tell you honestly whether AI can move it, and roughly how.
  4. Weeks 1–2If it can, a fixed-price diagnostic proves it on your own data.
What you leave the call with
!
A one-page planThe number to move, the data to connect, and what AI would do about it.
The metric that mattersWhere the data livesThe first quick winA fixed price
Clarity after 30 minutes

Suggested actionIf AI can’t move your number, we’ll say so on the call.

The data we’d connect
Whatever you run todaySpreadsheetsCRMERPHR systemSupport deskMarketing tools
The needle we’d move
The number you name
TodayToday→With the engineTarget

Illustrative stories and names. Real results depend on your data — we’ll tell you honestly on the first call.

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