9 domains · 36 use cases
Pharma
Revenue, trade settlement, and statutory reconciliation in one AI-ready finance layer.
1. Pharma Landscape Framing
Current State of Indian Pharma
A domestic formulations company is one of the hardest commercial operations to run in India. Product leaves the plant, sits in a CFA depot in each state, ships to a stockist, and finally reaches the chemist who fills the prescription. Demand is not created at the chemist counter. It is created in the doctor's chamber, where a Medical Representative details a product and the doctor decides whether to write it. Between the molecule and the rupee sit four parties, two distribution layers, a field force, and a prescriber who never appears on any invoice.
A ₹600 Cr domestic formulations company might run 28 CFA depots, 2,400 stockists, and a field force of 900 MRs across 8 therapy divisions — cardiology, diabetology, gastro, derma, gynaec, and more — each with its own brand portfolio, its own doctor universe, and its own incentive plan. Primary billing runs on SAP. The field force punches calls into a pharma SFA app. Stockist secondary trickles in through a DMS that is half-implemented. Schemes, credit notes, and GST live in three more places.
The distribution has scaled. The decision infrastructure behind it has not.
The Data, Analytics & Decision-Making Gaps
Three gaps define where Indian pharma companies are making expensive decisions on bad information:
Gap 1: Primary is visible. Secondary and prescription are a guess.
SAP captures what the company bills to stockists. What actually sells from the chemist — and which doctor's prescription drove it — is either self-reported through the SFA app, pulled from a partial stockist DMS feed, or bought from a third-party Rx audit that arrives a month late and covers a sample.
The result: divisions chase primary targets by pushing stock onto stockists at month-end, while the prescription that was supposed to pull that stock through never materialised. The company books the sale, the stockist holds the inventory, and the gap surfaces as expiry returns two quarters later.
Gap 2: Trade schemes and credit notes are the largest unmonitored cost line in the P&L.
Bonus offers, breakage allowances, expiry returns, and quarterly stockist schemes collectively run at 6-12% of net revenue. Credit notes are raised manually against claims that no one matches to actual secondary. Reconciliation is quarterly. Leakage — schemes settled on inflated billing, expiry credited twice, breakage claimed without proof — is systematic and largely invisible.
The sales team knows it is happening. Finance cannot prove the quantum. No one has stopped it.
Gap 3: MR productivity and prescription causality are aggregated into invisibility.
The SFA app reports call averages, coverage percentages, and doctor-meeting counts. It does not report whether the call changed a prescription. A division head sees that an MR is doing 12 calls a day at 92% coverage and assumes the territory is healthy — while Rx share for the flagship brand has been sliding for three months in exactly that territory.
The same blindness applies to launches. A new product is "launched" across 900 MRs. Which territories adopted it, which doctors switched, and which MRs never detailed it is never disaggregated at the speed the launch window requires. The first 90 days decide the brand. The data to manage them arrives on day 120.
Why Fire AI Is Relevant Now
Three structural pressures make this the right moment for Indian pharma:
The SFA rollout has created activity data without commercial intelligence — companies now know how many calls every MR makes and still cannot tell which calls move prescriptions or which divisions are subsidising dead territories.
Scheme and expiry cost is becoming a board-level question — as trade margins compress and the regulator tightens, the 6-12% of revenue going into schemes, breakage, and expiry credit is under pressure to prove it generated secondary. Fire AI makes that proof possible for the first time.
The India-specific pharma stack — SAP, Tally, a field-force SFA app, CFA and stockist DMS systems, GSTR-2B, the CDSCO portal — has no unified intelligence layer. Fire AI, with 700+ connectors, is built precisely for this operating reality.
2. User Personas
Six personas drive decision-making inside a pharma organisation. Fire AI enters through the National Sales Head or CFO, but compounds across every layer of the commercial, supply chain, and compliance function.
Persona 1 — The National Sales Head / VP Sales
| Role | National Sales Head or VP Sales — ₹100 Cr to ₹1,000 Cr+ domestic formulations company |
|---|---|
| Core Responsibilities | Owns national revenue, divisional targets, stockist network health, MR productivity, and Rx share across therapy areas. Sets territory targets, approves scheme calendars, and answers to the MD on primary, secondary, and prescription growth. |
| Pain Points | Primary billing is available daily. Secondary sell-out and Rx movement are unreliable or a month late. Divisional and territory variation is invisible at the national level until the quarterly cycle meeting. Cannot tell in real time whether a soft territory is a prescription problem, a stockist inventory problem, or an MR coverage problem. |
| Current Tools / Workarounds | SAP for primary, an SFA app ( Subhash, MR PRO, or a custom build) for call and coverage data, a third-party Rx audit for a sample of doctors, and a monthly MIS deck built by analysts from SAP exports and ABM inputs. |
| Where Decision-Making Breaks | Scheme approvals, territory restructuring, and MR deployment are decided on monthly aggregates that hide divisional and territory variation. A territory where Rx share has been falling for six weeks looks like a soft market in aggregate — not an MR execution gap a deployment change could fix. |
Persona 2 — The CFO / Finance Head
| Role | CFO or Finance Head — typically present at ₹75 Cr+ |
|---|---|
| Core Responsibilities | Closes monthly P&L across divisions and depots, validates scheme and credit-note claims, manages GST reconciliation across multi-state CFA operations, controls expiry and breakage provisioning, and produces board reporting. |
| Pain Points | Trade-scheme settlement is manual and quarterly — by the time over-settlement is found, the credit note has been issued. Expiry returns are credited against stockist claims no one reconciles to original billing. GSTR-2B mismatches across 20+ CFA state registrations pile up until filing week. Stockist deductions taken without approval are absorbed as breakage. |
| Current Tools / Workarounds | SAP or Tally for books, Excel for scheme and credit-note reconciliation, a GST tool, and a 3-5 person finance team running a monthly close that takes 18-22 days. |
| Where Decision-Making Breaks | Cannot close books in real time. Cannot prove whether the ₹40 Cr scheme-and-expiry budget generated proportional secondary. Expiry write-off lands as a year-end surprise. Board reporting runs on data that is already three weeks old. |
Persona 3 — The Field Force Excellence (SFE) Head
| Role | Head of Sales Force Excellence / Commercial Excellence — present at ₹150 Cr+ |
|---|---|
| Core Responsibilities | Owns MR productivity, call quality, doctor coverage, territory design, and incentive scheme effectiveness. Translates national targets into territory norms and manages the link between field activity and prescription outcomes. |
| Pain Points | The SFA app reports calls and coverage, not whether calls changed prescriptions. Cannot connect doctor-wise Rx trend to the MR who details that doctor. New-product detailing compliance is self-reported. Incentive payouts are computed on primary, which rewards stocking the stockist, not generating Rx. |
| Current Tools / Workarounds | The SFA app for call and coverage data, a separate Rx audit file, an Excel incentive model, and monthly reviews with Area Business Managers whose feedback is optimistic by design. |
| Where Decision-Making Breaks | Territory restructuring, coverage norms, and incentive design are set on activity metrics that have never been linked to Rx outcomes. The 20% of doctor calls that move prescriptions are indistinguishable from the 80% that are GPS pings against a target. |
Persona 4 — The Supply Chain Head (CFA & Stockist)
| Role | Supply Chain or Distribution Head — present at ₹200 Cr+ |
|---|---|
| Core Responsibilities | Manages plant-to-CFA dispatch, CFA-to-stockist fill rates, batch and expiry management, cold-chain compliance, and inventory health across the network. Owns stockout avoidance and expiry write-off. |
| Pain Points | Forecasting runs on primary billing history, not secondary offtake — so slow divisions over-stock and fast ones run short. Batch expiry risk surfaces only when the stockist files a return. Cold-chain breaches for thermo-sensitive products are logged after the fact. Fill rates to stockists are tracked monthly, not at dispatch. |
| Current Tools / Workarounds | SAP for inventory and dispatch, a batch-tracking module, cold-chain data-logger exports reviewed periodically, and weekly calls with CFA agents and depot managers. |
| Where Decision-Making Breaks | Production and depot allocation are set on lagged primary, not on chemist-level secondary. Batch expiry is managed reactively — a batch flagged 60 days from expiry could have been redeployed to a high-velocity territory; instead it is credited as a return at full cost. |
Persona 5 — The Marketing / Brand Head
| Role | Marketing or Brand Head — owns one or more therapy divisions, present at ₹100 Cr+ |
|---|---|
| Core Responsibilities | Owns brand growth versus market, detailing strategy, doctor segmentation, new-launch performance, and the marketing spend behind each brand. Designs the detailing message and the input plan the field force executes. |
| Pain Points | Cannot see whether detailing spend on a specialty actually moved Rx for that brand. Doctor segmentation (high/medium/low prescribers) is static and rarely refreshed. New launches are tracked on primary dispatch, not on territory-wise adoption. Competitive brand share is estimated from a sample audit, never triangulated against the company's own secondary. |
| Current Tools / Workarounds | An Rx audit subscription, the SFA app for input deployment, brand-team Excel trackers, and quarterly therapy-area reviews built from aggregated data. |
| Where Decision-Making Breaks | Input allocation — samples, gifts, CME spend, MR effort — is spread across the doctor universe by habit, not by causal Rx response. The brand head cannot tell which detailing investment generated incremental prescriptions and which subsidised doctors who would have written anyway. |
Persona 6 — The Compliance & Regulatory Head
| Role | Head of Regulatory Affairs & Compliance — present at ₹150 Cr+ |
|---|---|
| Core Responsibilities | Owns CDSCO filings and licence tracking, batch recall readiness, pharmacovigilance and adverse-event reporting, and audit preparedness across plants and depots. Accountable to the regulator and the board for compliance exposure. |
| Pain Points | Filing status, licence renewals, and batch records live in separate systems and spreadsheets. A recall requires tracing a batch across CFA, stockist, and chemist manually. Adverse-event signals are buried in field reports and pharmacovigilance inboxes until a quarterly review. Audit prep is a fire drill, not a state. |
| Current Tools / Workarounds | The CDSCO portal, a quality-management system, Excel licence trackers, a pharmacovigilance mailbox, and a small RA team that reconciles filings manually. |
| Where Decision-Making Breaks | Recall response and adverse-event escalation are reactive. A batch that needs to be pulled cannot be located across the distribution chain fast enough to limit exposure. Filing lapses and AE frequency spikes surface in audits, not before them. |
3. Problem → Fire AI Mapping
Each row below represents a real, high-frequency decision failure in an Indian pharma business — and the precise Fire AI capability that resolves it. Every problem, feature, and outcome is grounded in how pharma distribution, the field force, and the prescription engine actually operate.
Sales & Field Force: Primary vs. Secondary vs. Rx Gaps
| Problem | Visibility Gap | Fire AI Feature | Outcome |
|---|---|---|---|
| Primary targets are met by pushing stock to stockists at month-end while secondary and Rx never pull it through — the gap shows up later as expiry returns | Primary billing is daily; secondary offtake and Rx movement are absent or a month stale. No system connects billing to chemist sell-out to prescription | Causal Chain Intelligence + Schedulers & Alerts | "Cardiology division billed ₹4.2 Cr primary in March, 31% above secondary. 14 stockists are now at 70+ days cover on the flagship brand. Projected expiry write-off if not redeployed: ₹46L. Reallocation list generated." |
| MR coverage and call averages look healthy while Rx share for the flagship brand quietly slides in the same territory | The SFA app shows calls and coverage; it does not show whether the call changed a prescription. Call data and Rx audit live in separate systems | Causal Chain Intelligence + Deep Drill-Down on Dashboards | "MR Suresh runs 92% coverage and 12 calls a day in Pune North. Flagship Rx share there fell from 24% to 17% over the quarter. 9 high-value cardiologists he details have switched to a competitor. Estimated recoverable secondary from corrected detailing: ₹11L/quarter." |
| A new product is launched across 900 MRs but territory-wise adoption is invisible until the launch window has closed | Launch performance is read off primary dispatch; doctor switch and MR-level detailing compliance are never disaggregated at launch speed | Deep Drill-Down on Dashboards + Ask Fire AI | "New molecule adoption at week 6: Maharashtra at 38% of doctor target, Gujarat at 11%. 140 MRs in the West have not logged a single detailing call on the launch brand. The launch is failing in Gujarat — not nationally. Corrective deployment recovers an estimated ₹62L of first-year secondary." |
Trade Schemes & Credit Notes: Leakage and Settlement Gaps
| Problem | Visibility Gap | Fire AI Feature | Outcome |
|---|---|---|---|
| Stockist scheme and bonus claims exceed actual secondary — the over-claim is known but never quantified before the credit note is raised | Scheme claims are matched to billing, not to chemist-level secondary, and reconciled quarterly. By the time a mismatch is found, the credit note is issued | Auxiliary Reports — Trade Discount Settlement Reconciliation + Causal Chain Intelligence | "Stockist GR Pharma claimed bonus schemes on ₹58L of billing. DMS secondary shows ₹34L of actual chemist sell-out. Scheme over-claim: ₹4.8L. 11 stockists show the same pattern this quarter. Total deniable before settlement: ₹41L." |
| Expiry and breakage credit notes are raised against claims no one ties back to original billing — the same stock gets credited twice | Credit notes are processed against stockist claims manually; there is no automated match to the batch and invoice the return is claimed against | Auxiliary Reports — Stockist Credit Note Reconciliation + Deep Drill-Down on Dashboards | "₹2.3 Cr in expiry credit notes raised this half-year. ₹37L of it cannot be matched to an original billing batch. 6 stockists account for 71% of the unmatched credit. Dispute window open for 14 days." |
| GST input on schemes and the GSTR-2B position across multi-state CFA registrations never reconcile before the filing date | Scheme GST treatment varies by state CFA registration; 2B mismatches are found at filing week, not before | Auxiliary Reports — GST Reconciliation on Pharma Schemes + Schedulers & Alerts | "GSTR-2B mismatch across 22 state registrations: ₹19L of ITC at risk on scheme and freight invoices. 8 CFA registrations account for the bulk. Filing date in 9 days. Mismatch list and supplier follow-up generated." |
Supply Chain & Expiry: Forecast and Working Capital Gaps
| Problem | Visibility Gap | Fire AI Feature | Outcome |
|---|---|---|---|
| Demand forecasts run on primary billing, not chemist secondary — production and depot stock are systematically misaligned with real consumption | Secondary offtake is not fed into the planning model; the forecast uses lagged, target-driven primary billing | Causal Chain Intelligence + Schedulers & Alerts | "Secondary for the diabetology flagship in Tamil Nadu has grown 29% for three months. The production plan has not moved. At current run rate you will be 16 days short at 9 key stockists before the season — estimated lost secondary: ₹33L." |
| Batches drift toward expiry at slow stockists while fast territories run short of the same SKU — the mismatch surfaces only as a return | No view connecting batch expiry dates at stockist level with territory-wise secondary velocity and redeployment options | Auxiliary Reports — Batch Expiry Risk Flagging + Deep Drill-Down on Dashboards | "₹1.1 Cr of stock is within 90 days of expiry at 18 low-velocity stockists. The same SKUs are below 12 days cover at 7 high-velocity stockists. Redeployment now prevents an estimated ₹68L expiry write-off and recovers ₹24L of secondary." |
4. Entry Points
Every entry point must answer one question for the pharma commercial leader in under 90 seconds: "Where is my scheme and expiry spend leaking, which territories are losing Rx share they should be winning, and what does my field force need to detail differently this week?" Not a report. A verdict with a number.
Entry Point 1 — The Scheme & Credit-Note Leakage Scan
The finance or sales head connects SAP billing, the scheme master, and the stockist DMS export. In 90 seconds, Fire AI quantifies scheme and credit-note leakage by stockist, identifies over-claim and double-credit patterns, and generates a deniable amount before the next settlement cycle. This is the first meeting trigger and the activation hook.
Why it gets the first meeting: scheme and expiry leakage is known at every pharma company. It has never been quantified fast enough to deny the claim. Fire AI does it before the credit note is raised.
Entry Point 2 — The Primary vs. Secondary vs. Rx Mismatch Report
For sales heads and division heads, this is the signal they have never had in real time: which stockists are sitting on billing that never sold through, which territories are losing Rx share, and where the primary push has outrun real demand. The output is a division-and-territory action list, not a dashboard to explore.
Entry Point 3 — The MR Productivity & Rx Causality Scan
For SFE heads and division heads preparing territory and incentive decisions, this surfaces the gap that no SFA report shows: the difference between an MR who makes the call and an MR whose calls move prescriptions. The output ranks MRs by Rx-effective coverage, not by call average, and names the doctors who switched.
Entry Point 4 — The New-Launch Adoption Diagnostic
For brand and marketing heads inside the launch window, this is the input that has always arrived too late: territory-wise adoption, doctor switch, and MR detailing compliance while the window is still open. The output redirects field effort before the launch is decided.
Entry Point 5 — The Batch Expiry & GST Reconciliation Report
For the CFO and supply chain head, this is a direct working-capital and compliance recovery tool. The output surfaces batches drifting to expiry at slow stockists with redeployment options, GSTR-2B mismatches across CFA registrations, and stockist deductions taken without approval — each with the amount at risk before the next filing or write-off date.
The reconciliation pays for the subscription in the first run. The CFO becomes the internal champion who drives org-wide adoption.
Parallel Retention Layer — The Monday Commercial Brief
Every Monday, the sales head receives three decisions ranked by rupee impact: which stockist intervention is most urgent, which territory has the highest recoverable secondary or Rx this week, and which scheme or expiry exposure is the worst in the current cycle. No MIS to chase. No ABM WhatsApp to decode. Delivered before the weekly cycle call.
| What Gets the First Meeting | What Gets Adoption |
|---|---|
| Scheme & Credit-Note Leakage Scan — free, connects SAP + scheme master + DMS, verdict in 90 seconds | First scheme over-claim denied or first expiry batch redeployed from a Fire AI verdict |
| Primary vs. Secondary vs. Rx Mismatch Report — shows immediate secondary and Rx recovery potential | Monday Commercial Brief becomes the cycle-call agenda — expansion from Sales Head to SFE to Finance |
| MR Productivity & Rx Causality Scan — answers the detailing-effectiveness question every SFE head is wrestling with | Ask Fire AI used by division heads for territory and incentive reviews — analyst and ABM reporting dependency removed |
5. Aha Moments — By Persona
An Aha Moment is not a feature discovery. It is the exact moment where a specific person says: "This is what I have been trying to get out of my SFA app and my Rx audit for two years and never could." Design for these moments. Everything else is secondary.
National Sales Head — The Territory Rx Recovery
Trigger: Primary vs. Secondary vs. Rx Mismatch Report, within 10 minutes of connecting SAP and SFA data.
What must appear: Territory-level primary-secondary-Rx alignment map, territories flagged as Rx-loss vs. inventory-push, doctors who switched, estimated recoverable secondary per territory, action required per stockist and MR.
CFO / Finance Head — The Scheme & Expiry Leakage Number
Trigger: Scheme & Credit-Note Leakage Scan, typically the entry point for the Finance persona.
What must appear: Stockist-wise claim vs. secondary comparison, scheme over-claim and unmatched expiry credit per stockist, total deniable amount this cycle, denial-ready summary with supporting billing and DMS data.
Field Force Excellence Head — The Rx-Effective Coverage Breakdown
Trigger: MR Productivity & Rx Causality Scan, typically in the first week of use.
What must appear: MR-level Rx-effective coverage vs. call coverage, doctor-wise prescription trend mapped to the detailing MR, list of high-value doctors who switched, estimated secondary gap attributable to detailing failure.
Supply Chain Head — The Expiry Redeployment View
Trigger: Batch Expiry Risk Flagging + Deep Drill-Down on stockist cover vs. secondary velocity, typically onboarded after the Sales Head activates.
What must appear: Batch-level days-to-expiry by stockist, redeployment options to high-velocity stockists, projected write-off avoided, estimated secondary recovered.
Marketing / Brand Head — The Launch Adoption Correction
Trigger: New-Launch Adoption Diagnostic, typically surfaced inside the first 90 days of a launch after the Sales Head activates.
What must appear: Territory-wise adoption vs. doctor target, doctor switch and trial data, MR-level detailing compliance on the launch brand, estimated first-year secondary at risk and the corrective deployment.
Compliance & Regulatory Head — The Recall Trace & AE Signal
Trigger: Batch Recall Monitoring + adverse-event frequency tracking, typically activated after the commercial team is live and distribution data is flowing.
What must appear: Full batch distribution trace across CFA, stockist, and chemist; quantity sold through vs. recoverable; adverse-event frequency by product with trend; CDSCO filing and licence-renewal status.
6. Red Flags & Risks
These are the specific ways this GTM loses in pharma, in order of likelihood. Each one reflects a real pattern in how pharma technology adoptions fail in India.
| Risk | What It Looks Like / How to Prevent It |
|---|---|
| Getting positioned against the SFA vendor | Pharma companies will frame Fire AI as a competitor to their SFA app and route the evaluation to the SFA admin. It is not an SFA tool. Fire AI is the decision layer above the SFA, the ERP, and the DMS — it reads their data and tells the leader what to do. The moment it is scoped as "another field-force app", the buying committee shrinks to IT and the pricing collapses. Keep the sponsor at the Sales Head or CFO level. |
| MR and field resistance to Rx-effectiveness visibility | ABMs and MRs will resist any product that surfaces real Rx-effective coverage instead of call averages. The buyer is the National Sales Head, SFE Head, and CFO — not the field force. Frame Fire AI to the buyer as a secondary-and-Rx recovery tool, not a surveillance tool. The field conversation comes after the ROI is established at the top. |
| Stockist and CFA data access as a blocking objection | Companies will say stockist DMS coverage is partial and CFA data is messy, so the analysis cannot be trusted. This is true and irrelevant in week one. Fire AI's value at the start is surfacing the direction of the problem — which stockists, which territories, which schemes — not audited precision. Coverage improves as the product embeds. Do not let this delay the first verdict. |
| Getting trapped in a DMS or SAP integration scope | IT will insist on full SAP and DMS integration before any evaluation, creating a 3-6 month dependency that kills velocity. The counter: Fire AI works with SAP and DMS exports on day one. Full integration is a phase-2 enhancement, not a precondition. Show the verdict first, negotiate the integration second. |
| Compliance scope creep pulling the deal into RA | Regulatory will want the batch-recall and pharmacovigilance features scoped to a full validated compliance system with CDSCO documentation. That is an 18-month qualification project. Lead with the commercial verdict, keep compliance as a high-value secondary use case, and do not let RA's validation requirements gate the commercial deal. |
| Underpricing the scheme and expiry protection value | A ₹600 Cr company with 8% scheme-and-expiry cost carries roughly ₹48 Cr in annual scheme, breakage, and expiry spend. Fire AI protecting 5% of that is a ₹2.4 Cr annual value. A subscription priced below ₹30-50L/year for this company leaves the value on the table and sets a price anchor that is impossible to reset. |
| Getting typecast as a scheme reconciliation tool | Reconciliation is the wedge, not the product. If marketing leads with "Fire AI catches scheme fraud", it gets scoped as a compliance audit tool and priced accordingly. Always pair the reconciliation output with the commercial decision it enables: not just "here is the leakage", but "here is how to redesign this scheme so it pulls real secondary and the leakage becomes structurally impossible." |
7. Website & Distribution Requirements
What the Website Must Enable
The pharma website is not a product walkthrough. It is a commercial pain recognition engine. Every page must speak the language of the pharma operator — primary, secondary, Rx share, stockists, schemes, expiry, MR coverage, detailing — and end with a scan or a demo request. No page should leave the visitor with a feature list. Every page ends with a verdict prompt or a recoverable rupee number.
Hero Page — Role-Gated and Scale-Gated Headlines
The homepage must speak to commercial function, revenue band, and division. Segment by role, scale, and therapy structure:
₹100-500 Cr formulations companies: Find out how much of your scheme and expiry spend pulled real secondary — and how much was claimed without it.
Multi-division companies (5+ therapy divisions): Your cardiology division is winning. Your derma division is leaking. Fire AI shows you which, by territory, this week.
National Sales Heads: Your secondary and Rx data is a month late. Fire AI tells you which territories are losing prescription share right now.
CFOs: ₹9 Cr in scheme claims that don't match stockist secondary, and expiry credit with no matching batch. Fire AI finds it before the credit note runs.
SFE Heads: Your MRs are at 91% coverage. Fire AI shows you which calls actually moved a prescription — and which doctors switched.
SEO Comparison Pages (Hidden Pages)
These pages capture pharma operators evaluating their options after a bad quarter, an expiry write-off, an audit finding, or a board question they could not answer.
Fire AI vs. SFA Dashboards — Why your field-force app shows calls, not whether they moved a prescription
Fire AI vs. SAP Analytics — Built for pharma commercial teams, not data engineers
Fire AI vs. Hiring a Sales-Ops Analyst — Territory and Rx diagnostics on demand vs. a function that takes 3 months to build
Fire AI vs. Manual Trade-Scheme Reconciliation — The cost of quarterly scheme settlement in a business where the credit note has already gone out
Fire AI for Stockist & CFA Reconciliation — From primary billing visibility to real secondary and credit-note intelligence
Fire AI for Field-Force Effectiveness — From call averages to Rx-effective coverage by MR and territory
Persona-Specific Landing Pages
For National Sales Heads: "Which of your territories is losing Rx share right now — and which stockist is sitting on billing that never sold through?"
For CFOs: "Your quarter scheme settlement is ₹72 Cr. Fire AI finds the ₹9 Cr that does not match stockist secondary before the credit note runs."
For SFE Heads: "Coverage is 91%. Rx-effective coverage is 58%. Fire AI shows you which MR calls are not moving a prescription."
For Supply Chain Heads: "₹1.1 Cr of stock is 90 days from expiry at slow stockists while fast ones run short. Fire AI sees both and gives you the redeployment list."
For Brand Heads: "Your launch is at 38% adoption in Maharashtra and 11% in Gujarat. Fire AI shows you the gap while the window is still open."
Use-Case Entry Points (High-Conversion Pages)
Scheme & Credit-Note Leakage Scanner — upload SAP billing, scheme master, and DMS secondary; get a stockist-wise leakage and unmatched-credit report with a deniable amount in 60 seconds
Primary vs. Secondary vs. Rx Mismatch Finder — connect SAP billing, DMS secondary, and Rx audit; get a division-and-territory imbalance map with recoverable secondary
MR Productivity & Rx Effectiveness Tool — upload SFA call data and Rx audit; get MR-level Rx-effective coverage vs. call coverage and the secondary gap
Batch Expiry Redeployment Scan — connect stockist inventory and batch data; get an expiry-risk vs. velocity map with the redeployment list and write-off avoided
Supporting GTM Assets
| Asset | Purpose / Owner |
|---|---|
| Monday Commercial Brief — weekly email digest | Retention and top-of-funnel awareness; keeps Fire AI in the pre-cycle-call decision rhythm of sales and SFE teams |
| Pharma Case Studies — ₹ outcomes, named companies | Social proof for mid-funnel; must lead with scheme rupees denied, expiry write-off avoided, or secondary recovered — not with features |
| The India Pharma Commercial Benchmark Report (annual) — stockist health norms, scheme-and-expiry leakage benchmarks, MR Rx-effectiveness by therapy area | SEO anchor + PR trigger + the document every SFE head and sales VP shares at the annual industry conference |
| Demo video — 90 seconds, scheme leakage scan, no setup narrative | Website hero section + outbound follow-up; must open with a deniable rupee number, not a feature tour |
| Shareable Scheme & Expiry Leakage Report — branded PDF output | Viral loop within pharma commercial networks; one SFE head shares with a peer at another company over an industry roundtable |
| CA & Pharma Distribution Consultant Partner Kit | Channel enablement; equips advisors to run the scheme-and-expiry leakage scan on behalf of their pharma clients in the first meeting |
8. Closing Note
Indian pharma leaders are not looking for better SFA reports.
They have SAP, SFA apps, stockist DMS feeds, Rx audits, and MIS decks produced 20 days after the decisions needed to be made. What they want — and what no existing tool gives them — is a system that looks across their stockist network, their scheme calendar, their field force, their batch inventory, and their prescription engine simultaneously, and tells them what is leaking and what to do about it before the next cycle closes.
The pharma opportunity in India is structural and urgent. A generation of formulations companies is scaling to thousands of stockists and hundreds of MRs across multiple therapy divisions while running schemes, breakage, and expiry that represent 6-12% of net revenue — spend that has never been proven to pull proportional secondary. The scheme leakage is systemic. The signal from secondary and Rx is broken. The field force is measured on calls, not prescriptions. And the decision infrastructure behind it is a combination of SAP, an SFA app, Excel, and WhatsApp that was not designed for the commercial complexity these companies now face.
Fire AI's causal AI, conversational interface, and India-native connector stack — SAP, Tally, pharma SFA apps, stockist and CFA DMS systems, GSTR-2B, the CDSCO portal — make it the only product built precisely for this inflection point in Indian pharma. Not adapted from a global SFA suite. Not bolted onto a DMS. Built for the operating reality of a 2,400-stockist formulations company trying to understand where its scheme spend went and why its flagship Rx is sliding, for the first time.
The positioning is clear. The wedge is specific. The loops are structural.
Work the sales head and CFO. Protect the verdict positioning. Let the scheme and Rx numbers do the selling.