Thembian — Built on Trust. Measured on Impact.

The Gap We Address

A $6–15B market. Nobody serving the middle.

Mid-sized enterprises are the fastest-growing segment of AI demand — and the most systematically underserved. Three data points explain why Thembian exists.

91%

Demand is surging

91% of mid-market firms now use GenAI. 26% of African CEOs are allocating over 20% of budget to AI — nearly double the global average. MEA AI services will reach $14.6B by 2028 at 34% CAGR. The demand is concentrated precisely where supply is thinnest.

RSM 2025 · KPMG Africa CEO Outlook · IDC META

$2M+

Supply has a structural floor

Large SIs won’t touch engagements below $2M. Global consultancies charge $300–500/hr for frameworks. Generic SaaS lacks enterprise fit. The result: a ₹300–3,000 Cr enterprise has no credible specialist partner.

Competitive landscape · Published engagement minimums

70%

Investment without ROI

70% cannot show measurable board impact. Only 39% report EBIT contribution. 34% have no strategy beyond a pilot. The failure is not intent — it is execution, expertise, and accountability. Exactly what Thembian is built for.

McKinsey State of AI 2025 · RSM 2025 · MIT NANDA

Where the market leaves you — and where Thembian operates

Generic SaaS AI

Too shallow for enterprise complexity. No contextual fit.

▼ THE GAP

₹300 Cr – ₹3,000 Cr enterprises

⚡ Thembian operates here

Large IT Integrators

$2M+ minimum. Your engagement doesn’t qualify.

How Thembian closes the gap

✓

Engagement range: ₹50L – ₹5Cr. Designed for the project sizes that fall below SI thresholds and above SaaS capability.

✓

Outcome-linked pricing: Not time-and-materials. Every engagement is anchored to a quantified, agreed business metric.

✓

India delivery economics: 40–60% cost advantage over Western delivery firms — structural, not opportunistic.

✓

Proof-of-Value entry: 2-week PoV on your live data before any commitment. You see measurable results before you decide.

✓

Horizontal depth: Three specialist service lines — AI Strategy, Data Engineering, Applied AI — not a generalised IT offering.

$5.5–14.5B

Serviceable addressable market for India-based AI services providers targeting mid-market enterprises across India, Africa & the Middle East by 2028

Thembian market sizing · IDC · Fortune Business Insights · Mastercard Institute

The People Behind It

We have seen this problem from the inside.
That is why we built Thembian.

Suresh Satyamurthy

Co-Founder & CEO · Strategy, Commercial & CEO Advisory

~35 years P&L Leadership TMT · Pharma · Consumer Guest Faculty, IIM Bangalore

“Every organisation I’ve worked with has tried AI. Most are still waiting for it to show up in the P&L. The gap between investment and return is where Thembian lives.”

35 years across TMT, Pharma, and Consumer Products — India, the US, APAC, and Europe. Led an ~$80M P&L at Siemens. Co-founded Tarnea Technology Solutions, building one of India’s largest pharma retail platforms. Guest Faculty at IIM Bangalore and three other institutions. Every engagement, the same pattern: mid-sized enterprises knew what AI needed to do — and couldn’t find a firm willing to do it at their scale. Thembian is the answer to that pattern.

Madhav Sitaraman

Co-Founder & CTO · Technology Architecture & AI Delivery

25+ years Enterprise AI Healthcare IT · SaaS · EAI Agentic AI

“Organisations spend millions on AI that never reaches production — not because the models are wrong, but because the data foundations and workflows aren’t there. That is the problem I exist to solve.”

25 years making architecture work in production — EAI, SOA, Healthcare IT, SaaS, Agentic AI. Led 100+ person engineering teams. Architected a $30M EAI programme at CSC. Co-founded Enlightiks (acquired by Practo). Built Tarnea’s pharma retail platform from scratch. Most recently, designed Agentic AI for Credit & Invoice Management in pharma distribution. Every engagement built on the data foundations that most AI firms skip past.

60+ combined years. Two companies built. One conviction shared.

We have sat inside the enterprise. We have seen what gets funded and what doesn’t. We have watched pilots die in committee and models get deployed that nobody uses. We have also seen what happens when AI is done right — when the problem is the right size, the data foundation is honest, the team is aligned, and someone stays accountable through to the outcome.

Thembian exists because we believe mid-sized enterprises deserve that experience — not as the exception, but as the standard.

2companies co-founded and scaled in India

3continents of enterprise delivery experience

₹660CrP&L led at Siemens TMT

$30MEAI programme architected for US healthcare

AI Use Cases by Leadership Role

What AI looks like for your C-suite.

Four high-value use cases for each member of your leadership team—with real outcome metrics and a 2-week Proof-of-Value entry point.

CEO

Chief Executive Officer

“How do I grow faster, compete smarter, and show my board measurable AI value?”

Use Case 01

AI-Powered Competitive Intelligence

4–6 wks

earlier competitive signal detection

Problem

You find out about competitor price moves weeks after the market does. By then the damage is done.

Solution

Always-on AI engine monitors competitor pricing, job postings, and filings—surfacing signals weeks before they reach your sales team.

⚡ Proof-of-Value: Train on 3 competitors. Deliver first weekly intelligence brief.

Retail · BFSI · Telecom

Use Case 02

Strategic Scenario Modelling

50×

more scenarios modelled vs. manual

Problem

Major capital decisions are made on gut instinct and spreadsheets built by junior analysts at 2am.

Solution

AI-driven scenario engine models 50+ what-if scenarios in hours, with probability-weighted board outcomes.

⚡ Proof-of-Value: Build 3 scenarios on one strategic decision currently on your table.

All mid-sized sectors

Use Case 03

Customer Lifetime Value Prioritisation

15–25%

revenue uplift from value-based prioritisation

Problem

Your top 20% of customers generate 80% of revenue—but your teams treat all customers the same.

Solution

ML model scores every customer by predicted 3-year LTV, churn risk, and cross-sell potential—feeding directly into CRM.

⚡ Proof-of-Value: Score your existing customer base. Identify top 20% by predicted LTV.

Retail · BFSI · Telecom

Use Case 04

AI-Augmented Board Reporting

3 wks → 4 hrs

board pack production time reduction

Problem

Your team spends 3 weeks every quarter assembling board packs from 12 different systems. Half the data is stale when it lands.

Solution

Automated pipeline produces a live, narratively intelligent board pack in hours, not weeks.

⚡ Proof-of-Value: Automate one board report from your existing data sources.

All mid-sized sectors

CTO

Chief Technology Officer

“How do I build scalable AI infrastructure without breaking existing systems?”

Use Case 01

Agentic AI for Document & Approval Workflows

70–85%

reduction in manual document handling time

Problem

Procurement approvals, vendor onboarding, contract reviews—all stuck in email chains.

Solution

Agentic AI reads, classifies, extracts, and routes documents autonomously. Humans only touch exceptions.

⚡ Proof-of-Value: Automate one document type (e.g. invoice or PO) end-to-end.

Manufacturing · Logistics · BFSI

Use Case 02

Cloud-Native Data Lakehouse Architecture

1 platform

replacing 7+ fragmented data sources

Problem

Your data lives in 7 different systems—SAP, Salesforce, Excel, legacy warehouse, shadow IT. AI can’t work on this.

Solution

Unified cloud-native lakehouse on Databricks, Snowflake, or BigQuery with governance built in from day one.

⚡ Proof-of-Value: Connect 2 source systems. Build one unified dataset. Prove data quality.

All mid-sized sectors

Use Case 03

Predictive Maintenance for Operations

40–60%

reduction in unplanned downtime

Problem

Equipment failures are caught after they happen—causing unplanned downtime and emergency procurement.

Solution

IoT sensor data + ML model predicts failure 72–120 hours in advance, triggering maintenance orders automatically.

⚡ Proof-of-Value: Train model on 6 months of sensor data from one production line.

Manufacturing · Logistics · Energy

Use Case 04

AI Model Governance & Monitoring Platform

100%

visibility across all AI models in production

Problem

You have AI models in production. Nobody knows if they’re still accurate. One bad prediction could be a compliance event.

Solution

Model monitoring layer tracks drift and accuracy degradation with automated alerts and audit trails.

⚡ Proof-of-Value: Instrument one existing production model with drift monitoring.

BFSI · Healthcare · Regulated industries

CMO

Chief Marketing Officer

“How do I know which campaigns work, and find the next customer before my competitor does?”

Use Case 01

Customer Segmentation & Next-Best-Action

3–5×

improvement in campaign conversion rate

Problem

Your campaigns go to broad segments. Conversion rates are 1–3%. Everyone gets the same message.

Solution

ML-driven micro-segmentation groups customers by behaviour and intent signals—enabling hyper-personalised campaigns.

⚡ Proof-of-Value: Segment one product line’s customer base. Run A/B test on two segments.

Retail · BFSI · Telecom · D2C

Use Case 02

Marketing Mix Modelling & Attribution

20–35%

improvement in marketing ROI from reallocation

Problem

You spend ₹5Cr across 8 channels. You have no idea which ones are actually driving revenue.

Solution

Marketing mix model attributes revenue contribution to every channel, accounting for lag effects and seasonality.

⚡ Proof-of-Value: Build attribution model on last 12 months of spend and revenue data.

Retail · Consumer Products · D2C

Use Case 03

Churn Prediction & Retention Engine

25–40%

reduction in preventable churn

Problem

You know customers are churning. You find out when they cancel—6 months of warning signals wasted.

Solution

Churn model scores every customer weekly, triggering personalised retention at 60, 30, and 14 days before predicted exit.

⚡ Proof-of-Value: Train churn model on 24 months of customer behaviour data.

Telecom · Retail · SaaS · Financial Services

Use Case 04

Demand Forecasting for Campaign Planning

30–50%

reduction in stockouts during campaign periods

Problem

Marketing plans on last year’s numbers. Operations is blindsided by demand spikes. Stock runs out during promotions.

Solution

Demand model gives supply chain a 6–8 week forward view of demand by SKU and region.

⚡ Proof-of-Value: Build demand forecast for top 20 SKUs across next campaign cycle.

Retail · Consumer Products · FMCG

CSO

Chief Sales Officer

“How do I make my sales team hit quota more consistently and stop losing deals we should win?”

Use Case 01

AI-Powered Lead Scoring & Pipeline Intelligence

30–45%

improvement in qualified pipeline conversion

Problem

Your sales team has 400 leads in the CRM. They follow up with whoever emailed last.

Solution

ML lead scoring ranks every lead by conversion probability, deal size potential, and time-to-close.

⚡ Proof-of-Value: Score existing open pipeline. Identify top 20% by predicted close probability.

B2B across all sectors

Use Case 02

Sales Velocity Optimisation

20–35%

reduction in average sales cycle length

Problem

Your average deal takes 90 days. Some close in 30. You don’t know why. Sales managers coach by instinct.

Solution

AI analysis of CRM, email cadence, and deal velocity builds playbooks from what your best reps actually do.

⚡ Proof-of-Value: Analyse last 18 months closed-won vs. closed-lost. Surface top 5 velocity drivers.

B2B · Enterprise · Financial Services

Use Case 03

Dynamic Pricing & Deal Desk Intelligence

3–7%

gross margin recovery from pricing discipline

Problem

Sales reps discount to close. Sometimes 5%, sometimes 30%. Margin leaks on every deal with no model.

Solution

Dynamic pricing model recommends optimal discount by deal based on size, competitive pressure, and probability.

⚡ Proof-of-Value: Analyse discount patterns on last 12 months. Build pricing recommendation model.

B2B Manufacturing · Technology · Logistics

Use Case 04

Renewal Risk & Expansion Intelligence

90–120 days

early warning before renewal risk crystallises

Problem

You find out a renewal is at risk when the customer tells you. The signals were there 6 months ago.

Solution

Customer health scoring flags at-risk renewals 90–120 days early and identifies expansion-ready accounts.

⚡ Proof-of-Value: Score current renewal book by health. Identify top 10 at-risk accounts.

SaaS · Financial Services · Telecom

CFO

Chief Financial Officer

“How do I prove AI ROI to the board and protect margin while we scale?”

Use Case 01

Working Capital & Cash Flow Optimisation

10–20 days

reduction in cash conversion cycle

Problem

Your cash conversion cycle is 67 days. Industry best practice is 42. Every extra day costs working capital.

Solution

ML model identifies specific customers, suppliers, and SKUs creating cash flow drag with prescriptive actions.

⚡ Proof-of-Value: Analyse 24 months of AP/AR data. Identify top 10 working capital opportunities.

Manufacturing · Retail · Distribution

Use Case 02

AI-Driven Financial Planning & Forecasting

15–20% → 3–5%

forecast error reduction at quarter end

Problem

Your FP&A team spends 3 weeks on the annual plan. The forecast is wrong by 15–20% at quarter end.

Solution

AI forecasting model builds scenario plans in hours, updating automatically as actuals come in.

⚡ Proof-of-Value: Build AI forecast alongside your current Q-end forecast. Compare accuracy.

All mid-sized sectors

Use Case 03

Cost Intelligence & Margin Leak Detection

3–8%

gross margin recovery from identified leak points

Problem

The P&L says 34% gross margin. Some accounts are loss-making at segment level. You don’t know which ones.

Solution

AI cost allocation computes true profitability by customer, product, channel, and geography.

⚡ Proof-of-Value: Run true-cost model on top 50 accounts. Identify bottom-quartile profitability.

All mid-sized sectors

Use Case 04

Credit Risk & Collections Intelligence

12–18 days

reduction in Days Sales Outstanding

Problem

Your DSO is 72 days. Collections calls go to every overdue account equally. Large risky ones slip past 90 days.

Solution

Credit risk model predicts payment behaviour, enabling priority by risk-adjusted exposure with automated nudges.

⚡ Proof-of-Value: Score current AR book by payment probability. Reprioritise collections queue.

BFSI · Distribution · Manufacturing

Measurable AI outcomesfor mid-market enterprises.

A $6–15B market. Nobody serving the middle.

We have seen this problem from the inside.
That is why we built Thembian.

Why clients choose
Thembian.

India delivery economics

Outcome-linked pricing

Reusable accelerators

End-to-end ownership

Three horizontal service lines.

Foundation & Readiness

Predictive Analytics

Domain-Tuned Models

What AI looks like for your C-suite.

Proof of Value in under 2 weeks.

Free Assessment

Proof of Value

Full Deployment

Managed AI Service

Know your AI opportunity
in 48 hours.

Thank you — we’ve received your request.

Let’s start a conversation.

WhatsApp

Email

Registered Office

Measurable AI outcomesfor mid-market enterprises.

A $6–15B market. Nobody serving the middle.

We have seen this problem from the inside.That is why we built Thembian.

Why clients chooseThembian.

India delivery economics

Outcome-linked pricing

Reusable accelerators

End-to-end ownership

Three horizontal service lines.

Foundation & Readiness

Predictive Analytics

Domain-Tuned Models

What AI looks like for your C-suite.

Proof of Value in under 2 weeks.

Free Assessment

Proof of Value

Full Deployment

Managed AI Service

Know your AI opportunityin 48 hours.

Thank you — we’ve received your request.

Let’s start a conversation.

WhatsApp

Email

Registered Office

We have seen this problem from the inside.
That is why we built Thembian.

Why clients choose
Thembian.

Know your AI opportunity
in 48 hours.