A borrower who asks about a home loan may be better served by a loan against property. A borrower who asks about a personal loan for business may be better served by an MSME business loan. A borrower who wants ₹50 lakhs may be eligible for ₹65 lakhs and would be better served by knowing that before they sign for less than they need. The Account Executive AI does not pitch the product the borrower asked about — it pitches the product the borrower actually needs, derived in real time from their financial profile.
Why off-the-shelf product pitching fails borrowers and institutions
A standard product pitch presents what the institution offers, in a fixed order, for every borrower. It is efficient to script and consistent to deliver — and consistently wrong for most of the borrowers who receive it. The borrower who opened a home loan enquiry and has a property worth ₹1.2 crore but only needs ₹40 lakhs is a better candidate for LAP than a home loan — LAP gives them a higher sanction relative to their collateral value, a lower effective rate for their actual requirement, and more flexibility on end use. A home loan sales script does not tell them this.
The Account Executive AI generates a product match score for every product in the institution's portfolio, in real time, from the borrower's qualification data. The borrower receives a ranked recommendation — not the product they asked about first, but the product suite ordered by fit — with a specific, personalised rationale for each ranking. The first 30 seconds of the AE AI's pitch are never generic.
The product match engine: real-time scoring for every product
The personalised pitch: what the AE AI says in the first 60 seconds
AE AI Opening Pitch — Arjun Menon · MSME Business Loan (Primary) + LAP (Secondary)
Generated in real time from match output · Delivered via WhatsApp / voiceThe matching logic: how the AE AI scores each product
The product match score for each product is computed from four factors. Income compatibility (30%): does the borrower's income type, source, and amount satisfy the product's income assessment method? A GST-based income borrower scores highest on products with GST-based assessment, lower on products requiring ITR-based assessment, and lowest on salaried-only products. Purpose alignment (25%): does the loan purpose match the product's eligible use? An equipment purchase for a business is a 100% match for MSME business loan and a 60% match for personal loan (which permits business use with restrictions). Collateral eligibility (25%): for secured products, does the borrower have the required collateral, and does it meet the eligibility criteria? Ticket and FOIR fit (20%): does the requested ticket size fall within the product range, and does the preliminary FOIR at that ticket fall within the product ceiling?
The scores are computed in parallel for all six products within 4 seconds of the qualification data being received — so the AE AI opens the call with the recommendation already in hand, without needing to gather information the SDR AI already collected.
The pitch that converts is the one built on the borrower's numbers, not the institution's catalogue
A borrower who is told "here's what we offer" gets a brochure experience. A borrower who is told "here's what you qualify for, here's what it costs, here's what you save by taking this product instead of that one, and here's what else becomes possible for you" gets an advice experience. The first produces a transactional relationship where the borrower shops on rate. The second produces a trusted relationship where the borrower asks what the institution recommends. The Account Executive AI delivers the advice experience consistently, from the first 60 seconds of every call, for every borrower — not just the ones who get the most experienced RM.