Cloud and SaaS adoption
Helped global enterprise customers reduce cloud transition costs by about 50% through championing and accelerating SaaS adoption strategies.
The pre-transformation intelligence platform
Diagnose before you buy.Priorities before platforms.
Most HR transformations fail long before go-live — in the gap where structural, leadership and governance causes are mistaken for software problems. MBT closes that gap: it traces each problem to its real causes, states honestly where technology helps and where it cannot, and turns that into priorities your board can defend.
The path MBT follows
- Signals
- Patterns
- Implications
- Priorities
- Decisions
Let's understand the problem before choosing the technology.
- Workforce
- Business Dynamics
- Technology Pressure
- Market & Industry
- Regulation & Compliance
- Organizational Realities
Why pre-transformation intelligence
The costliest HR decisions are made before anyone buys anything.MBT is the intelligence layer for that phase.
Before a business case is written, the evidence that should shape it sits scattered across owners, formats and systems — and the real causes are rarely the ones in the complaint.
- Geopolitics
- Economy
- Labour regulation
- Industry
- Technology & AI
- Local events
- Company-specific signals
- Your own secure workspace documents and private enterprise files
What MBT does with them
You choose which signals to scan. MBT then helps you group them, work through what they could mean for your organisation, and turn the ones you confirm into a prioritised transformation view.
- Signals
- Patterns
- Implications
- Priorities
- Decisions
The pre-transformation deficit
Software gets chosen before the cause is understood.
- Workforce data
- HR systems
- Market research
- Business strategy
- Technology roadmaps
- Regulatory changes
- Industry signals
- Consulting reports
- Too many signals.
- Too many possible initiatives.
- Too much uncertainty.
What actually needs to change — and can technology change it?
That is the question MBT answers before a platform is chosen — with evidence attached, scoped to your organisation, industry and geography, and honest about the parts no software can fix.
How MBT works
Six stages from organisational context to a decision leadership can take.
Each stage goes deeper than the complaint in front of you: what people experience day to day is traced back to what is really causing it, and forward to what it costs the business — so nothing is treated as a cause until it holds up against evidence. The depth of research is matched to the question, while the discipline stays consistent enough for outputs to remain comparable across geographies, business units and planning cycles. You stay in control at each stage — MBT proposes, you confirm.
- 01
Context
You set the organisation, industry classification, geography hierarchy and the situation you are looking at.
- 02
Signals
You choose which current-affairs categories to scan, then keep the sourced items you consider relevant.
- 03
Patterns
Possible scenarios and symptoms are proposed for you to confirm or reject, then grouped into problems.
- 04
Implications
Each confirmed problem is researched with sources attached, including likely root causes and their links.
- 05
Priorities
Transformation acts are scored and sequenced, with feasibility and alternatives set against cost and time.
- 06
Decisions
You export an executive presentation, the problem trees and an HR technology requirements workbook from the same run.
What you get
From intelligence to action
Three files come out of a single run — one to present, one to explain the thinking, one to hand to a vendor. Each is built only from what you confirmed, carries the run reference and review dates, and lists the sources behind every claim.
Executive presentation
What has changed, what is the problem, and what should we do?
One PowerPoint file covering the confirmed problems, what they cost, the business, HR and digital changes that address them, and the priorities — with sources behind each claim.
In the workspace: the export stage — downloads as a PowerPoint file
Explore an exampleProblem trees
How does each problem connect to what causes it?
A diagram for every confirmed problem, showing what sits underneath it and what it leads to, ready to drop into your own documents.
In the workspace: the export stage — downloads as an image file (SVG)
Explore an exampleHR technology requirements
What do we ask a vendor for, and in what order?
An Excel workbook turning every target outcome into line-by-line requirements, with a separate sheet of agentic AI scenarios — capabilities described, never brand names.
In the workspace: the export stage — downloads as an Excel workbook
Explore an exampleWhy MBT
Unconflicted, causal, evidence-led.
MBT sells no software and bills no implementation, so it can say plainly when technology is not the answer. It exists for one job: deciding what to change in HR, and in what order, before the money is committed.
- 01
Contextual
Not generic HR trends.
Intelligence scoped to your organisation, industry and the geographies your workforce actually sits in.
- 02
Evidence-backed
MBT does not treat AI output as evidence.
Evidence comes from identified sources. AI-generated analysis is labelled as interpretation, never as fact — findings carry their sources and an evidence-strength label, and say plainly where evidence is thin, contested or missing.
- 03
Vendor-neutral
Priorities before platforms.
Transformation priorities are established first, on the evidence. Technology and vendor choices are then assessed against those priorities — never the other way round, and never against a shortlist MBT has an interest in.
- 04
Decision-oriented
Not another research library.
Every output is designed to move a specific decision forward, in a format leadership can act on — with the reasoning, the sources and the open questions attached.
- 05
Hybrid Research Orchestration
Public evidence and your own files, together.
Simultaneously combines public web data with your own private, uploaded documents (PDFs, reports, benchmarks) with an automated AI Relevancy Guardrail to prevent noise — and your documents are cited by file name wherever they shape a conclusion.
The obvious question
Why MBT instead of generic AI?
General-purpose assistants are useful, and MBT uses AI too. The difference is what is done around it: HR transformation specialisation, a defined methodology, your organisational context, disciplined evidence handling where AI output is never counted as a source, and outputs built for a decision rather than a conversation.
Generic AI
MBT
- Generic AI
Answers questions
MBTStructures transformation decisions
- Generic AI
General-purpose
MBTPurpose-built for HR transformation
- Generic AI
Prompt-dependent
MBTBuilt around a defined transformation methodology
- Generic AI
Model output presented as the answer
MBTModel output treated as interpretation, evidence traced to identified sources
- Generic AI
Produces information
MBTConnects information to priorities
- Generic AI
Generic context
MBTOrganisation, industry and geographic context
- Generic AI
Confidence reads the same whether sources are strong or absent
MBTEvidence strength and open questions stated explicitly
- Generic AI
One-off interaction
MBTDesigned around an ongoing transformation workflow
Who it is for
Built for leaders making transformation decisions
Everyone works in the same workspace — there are no separate role-based views. Whoever has to defend the decision afterwards gets the reasoning, the sources and the evidence-strength labels with it.
- CHROUnderstand what matters most and make strategic transformation decisions with greater confidence.
- HR Transformation LeaderIdentify, prioritise and sequence transformation opportunities.
- COO / Transformation LeaderConnect workforce and HR transformation to broader business priorities.
- CIOUnderstand HR technology and transformation implications before major technology decisions.
Inside the platform
See the intelligence behind the decision.
MBT is a controlled-access platform, not a self-serve tool. Accounts are approved for verified business email addresses by an administrator, so the working product and its methodology are shown in a private walkthrough rather than published in full here.
- 01
Scope
Organisational context: industry classification, geography hierarchy and the situation in view.
- 02
Radar & research
Scans you trigger, then researched problems with sources and evidence-strength labels attached.
- 03
Transform
Outcomes and transformation acts drawn from the problems you confirmed.
- 04
Prioritise
Scored and sequenced acts, with feasibility and alternatives against cost, time and operations.
- 05
Export
Executive presentation, problem trees and the HR technology requirements workbook, with sources and open questions.
Walk through the real product
A private briefing walks through the live workspace and a worked example end to end, using your own organisational context rather than a canned demo.
Placeholder
Customer evidence / case study — replace with validated evidence
No customer names, logos, testimonials or outcome metrics are shown until they are verified and approved for publication.
Trust
Enterprise intelligence requires enterprise trust.
What follows is a summary. The published notices are the authoritative statements — no certification is claimed unless it has been formally obtained.
Data ownership
Your data is yours. It never trains our models.
Nothing you enter, upload or generate is used to train AI models, and nothing is sold or shared for marketing. Your research stays inside your account, is visible only to you and your approved colleagues, and can be exported or deleted at any time.
For sensitive work you can switch on a zero-retention run: the research run itself is not written to your browser or our database, no result is cached, and the session ends when you close the tab — you keep only what you export. Files you have uploaded to your secure workspace are held separately and stay until you remove them.
Secure workspace uploads: your files are private to your organisation, separated at the database level from every other customer, checked for relevance before they are used, and never sent to the public research engines. They are kept so you can reuse them across runs, and deleting a file or purging the workspace permanently removes it along with everything derived from it.
Business-email access, isolated per organisation, encrypted in transit, deletion on request. Read how we handle data.
Data handling
Your inputs and research outputs are stored against your own approved account and are not readable by other accounts, enforced by database-level access rules.
Access control
Access is granted by an administrator to verified business email addresses; there is no public sign-up, and access can be withdrawn at any time.
Sign-in codes
Sign-in uses single-use codes, valid for a limited time, sent to the approved business mailbox.
AI and data usage
How AI is used in research and what is sent to model providers is described in the data and security notice.
Privacy, retention and deletion
Retention and deletion practices, and applicable data-protection commitments, are set out in the privacy notice.
Founder perspective
Structure the question. Test the evidence. Make the decision defensible.
Meet the founder
Satrajit Sengupta
Founder, MBT
Helping organisations modernise HR operations through integrated digital platforms that simplify work and strengthen decision-making.
Satrajit brings more than 26 years of experience across HR, enterprise technology and transformation. His work combines HR domain understanding with HCM technology consulting, executive advisory and complex, multi-country delivery — with a consistent focus on turning transformation intent into measurable operating outcomes.
- HCM transformation
- 25+ years in HCM Business Transformation and Technology consulting
- Customer delivery
- 20+ end-to-end customer transformation engagements
Selected achievements
KPI and CXO advisory
Partnered with customer executives to define KPI frameworks that connect enterprise transformation programmes to measurable business outcomes.
Delivery predictability
Improved post-implementation delivery predictability by about 25% across a global customer portfolio, strengthening confidence in timelines and reducing escalations.
HR technology leadership
Turned HR technology programmes into measurable outcomes through structured adoption, faster HR cycle times and stronger end-user adoption.
Experience and education
SAP, IBM, HCL, JKT Consulting, PwC and Road Builder Malaysia. Currently working with VISCAP Consulting Pvt. Ltd as a Vice President - Strategic HR Innovation and Customer Success, focusing on optimizing large-scale HR transformation delivery, modernizing workforce operations, and navigating SaaS & AI adoption curves. Post Graduated in Human Resource Management from XLRI Jamshedpur.
International delivery
India, ANZ, Southeast Asia, EMEA, Canada and the Americas
Leadership approach
IT-enabled HR digital transformation, technology adoption, CXO advisory and business networking. Structure-and-metrics driven, with an emphasis on visible proof, practical frameworks and capability transfer.
Know the cause before you commit the capital.
Move from competing priorities and vendor pitches to an evidence-backed view of what to change, what technology can genuinely carry, and what must change in the business first.
Administrator-approved access
Sign in or request access
Requests are reviewed by an administrator — there is no public sign-up. Want the full picture for your organisation? Request a private briefing.
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