A leading Indian broking house was carrying regulatory and operational risk on every voice order. BootLabs built an AI agent that transcribes calls in real-time, extracts structured order intent, and auto-reconciles against the OMS — with a complete audit trail for SEBI compliance.
At a large broking house, a significant share of orders — particularly from institutional and HNI clients — arrived via phone call rather than the digital trading platform. Relationship managers and dealers took these orders verbally and manually entered them into the Order Management System (OMS). The gap between what was communicated on the call and what was entered in the system created a persistent reconciliation problem: mismatched quantities, wrong scrips, incorrect prices, or missed orders entirely. Manual reconciliation was slow, error-prone, and retrospective — always done end-of-day, never in real-time. With SEBI trade surveillance requirements demanding documented evidence that every client order was executed as instructed, the firm was carrying significant regulatory risk on every voice trade. BootLabs built an AI agent that transcribes broker-client calls in real-time, extracts structured order intent using NLP, and auto-reconciles against OMS records — flagging every mismatch for immediate review with a complete, immutable audit trail.
Orders communicated verbally by clients were entered manually into the OMS by dealers. Transcription errors, mishearing, and time pressure during market hours created regular discrepancies between what was ordered and what was booked — with no automated mechanism to catch them before they became problems.
End-of-day reconciliation was done manually by a dedicated team cross-referencing call logs with OMS records. Each trade took 15 or more minutes to verify — unsustainable at scale and always retrospective, never real-time. By the time a mismatch was found, the financial impact had already crystallised.
SEBI regulations require documented evidence that client orders were executed as instructed. Without automated call-to-trade reconciliation, the firm carried significant regulatory and dispute risk on every voice order — each one a potential compliance gap with no reliable paper trail.
All broker-client calls are transcribed in real-time using a financial-domain-tuned speech-to-text model. The system handles trading terminology, stock names, scrip codes, and the fast-paced conversational style of market hours communication — delivering high accuracy on the language patterns unique to Indian capital markets.
An NLP layer extracts structured order parameters from the transcript: instrument (scrip name or ISIN), direction (buy/sell), quantity, price type (market/limit/SL), and price level. The model handles ambiguous natural-language expressions like "take 500 Reliance at market" or "sell half my HDFC Bank position" with robust intent resolution.
Extracted order parameters are matched against the corresponding OMS entry in real-time. The reconciliation engine checks scrip, quantity, direction, and price — flagging discrepancies with a confidence score and specific mismatch detail so the compliance team can act immediately, during market hours rather than after close.
Every call, transcript, extracted order, and reconciliation result is logged immutably with timestamps. A compliance dashboard gives supervisors real-time visibility into reconciliation status, exception queues, and resolution history — fully exportable for SEBI audits, with audio, transcript, and OMS record accessible in a single click.
The agent went live alongside existing dealer workflows — no disruption to operations during onboarding. From day one, every voice order entered the reconciliation pipeline automatically. The compliance team, previously occupied with routine cross-referencing, shifted immediately to exception-only investigation. Dealer errors that previously surfaced at end-of-day were now flagged within minutes of the call — within market hours, while positions could still be corrected.
Every voice order now has a documented, timestamped reconciliation record. The firm can demonstrate to SEBI that client instructions were captured accurately and executed as instructed — eliminating the compliance gap that existed on every voice trade.
The compliance team was freed from routine manual checking and now focuses entirely on genuine exception investigation. The same headcount handles a significantly larger order volume — with higher confidence and lower error rates than the manual process ever achieved.
When a client disputes a trade, the audio, transcript, and OMS record are available in one click — no more manual trawl through call recordings and system logs. Dispute resolution time dropped from days to hours, reducing both operational cost and client friction.
Mismatches are flagged in near-real-time during the trading session — not end-of-day. Dealers and supervisors can correct errors while positions are still open, reducing the financial impact of entry mistakes before they become confirmed trades.
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