Case Studies
We don't talk about AI potential. Every case study below is a real business, with before-and-after numbers — hours saved, missed calls captured, no-shows eliminated, and leads that would have been lost converted into revenue.
Finance Agent for a Pune Fintech Company
The Problem
A fast-growing fintech company in Pune had a finance team of four people spending the majority of their working month on a single task: processing invoices. Every month, the company received over 200 vendor invoices in various formats — PDF, Excel, scanned images, and email text. Each invoice had to be manually read, verified against purchase orders, categorised in the ERP, flagged if anomalous, and entered into the accounts payable system. This process took the team 15 working days every month — more than half the month dedicated to a single administrative function. Errors were common. The team was exhausted. And the CFO had no real-time visibility into the company's payables position.
The Solution
Symagentic built a Finance AI Agent that integrates with the company's email inbox and cloud storage. When an invoice arrives — in any format — the agent reads it, extracts all structured data (vendor name, invoice number, line items, amounts, due dates, and tax details), matches it against the corresponding purchase order in the ERP, and posts the validated entry to the accounts payable system. For invoices that don't match or contain anomalies, the agent flags them in a prioritised exception queue for human review, along with an explanation of the discrepancy. At the end of each day, the agent generates a payables summary report for the CFO.
The Outcome
Processing time reduced from 15 working days to under 1 working day. Manual data entry errors eliminated entirely. The finance team of four now manages the exception queue (roughly 15–20 invoices per month) instead of processing all 200+. The CFO has real-time payables visibility for the first time. The agent was deployed in 5 weeks from kick-off. ROI was visible in the first month.
Recruitment Agent for a GCC Company in Hinjewadi The Problem
The Problem
A large GCC company in Hinjewadi, Pune ran a quarterly hiring drive for a technology team. Within 48 hours of posting the job descriptions, they received 500 applications. Their three-person HR team faced an impossible task: read 500 CVs, score each candidate against the job criteria, identify the top candidates, schedule preliminary calls, and present a shortlist to the hiring manager — all within one week. In previous hiring rounds, this process had taken three weeks, resulted in good candidates being missed, and left HR team members working evenings and weekends. There had to be a better way.
The Solution
Symagentic built a Recruitment AI Agent specifically configured for this company's hiring criteria. The agent was trained on the job descriptions, the company's ideal candidate profile, and the specific technical and soft skill requirements for each role. When connected to the applicant tracking system, the agent read every CV, scored each candidate on a weighted criteria matrix, wrote a two-paragraph assessment of each candidate's fit, ranked the full applicant pool, and delivered the top 10 candidates per role as a formatted shortlist — complete with individual summary notes — to the hiring manager's inbox.
The Outcome
500 CVs screened and shortlisted in 4 minutes. The HR team's involvement in the initial screening phase dropped to zero. Hiring manager review time reduced from 3 days to 2 hours. The quality of shortlisted candidates improved — because the AI applied the criteria consistently, without the fatigue-driven inconsistency of manual review. The agent was deployed in 3 weeks. It is now used for every hiring drive the company runs.
Customer Support Agent for a Real Estate Company
The Problem
A commercial real estate company in Pune was receiving between 80 and 120 customer enquiries per day across their website chat, WhatsApp business number, and email. Their support team of five was overwhelmed. Average response time to a new enquiry was 6–8 hours — and in the real estate business, a slow response is a lost lead. Most enquiries were repetitive: questions about property availability, pricing ranges, location details, amenity specifications, and the buying process. The team was spending 80% of their time answering the same questions, leaving little time for the complex enquiries that actually required human expertise.
Document Processing for a CA Firm
The Problem
A mid-sized chartered accountancy firm in Pune manages financial records for over 150 client companies. Each month, these clients send hundreds of documents — bank statements, purchase invoices, sales invoices, salary slips, and tax documents — in varying formats and quality. The firm's team of eight accountants was spending 40% of their working time just extracting and organising data from client documents before they could begin actual accounting work. This created a bottleneck that limited how many clients the firm could serve and pushed processing deadlines dangerously close to regulatory filing dates.
Dental Practice — AI Missed-Call Text-Back & Online Booking
The Problem
A Pune dental practice was receiving an estimated 50 calls per week after the clinic closed at 6pm. Without a receptionist on evenings and weekends, every one of those calls went to voicemail. Most callers never called back. The practice was losing new patient enquiries to competitors who either answered the phone or had an online booking system that worked.
HVAC Company — AI Missed-Call Capture & Lead Follow-Up
The Problem
A pune HVAC company was running paid ads during peak season — summer air conditioning emergencies and winter heating callouts. The campaigns were generating inbound calls, but the owner and engineers were frequently out on jobs and couldn't answer the phone. An estimated 60% of calls went unanswered. When callers tried competitors, they often booked there instead.
