Your CFO has a finance degree, fifteen years of experience, and spends half the month closing books manually.
We build AI systems that automate reconciliation, sharpen forecasting, catch fraud in real time, and reduce compliance overhead. Your finance team gets to do the work they were actually hired for.
Your finance team is the most expensive data entry department in the company.
Manual reconciliation is eating your team alive
Your finance team spends 15 hours a week on manual reconciliation across 23 data sources: bank statements, payment gateways, partner files, GST returns, internal ledgers. They export CSVs, paste into Excel, and run VLOOKUP formulas that break every time a vendor changes their invoice format. Cash reconciliation alone takes 30+ hours each month. If one source is delayed, the entire monthly close slips.
Your fraud detection is a quarterly audit
Most mid-market companies discover fraud during the quarterly audit, weeks or months after the damage is done. Your team reviews transactions in batches, not in real time. Anomalies that should be flagged in seconds sit unnoticed in spreadsheets. Attacks have gotten more sophisticated than the manual controls most finance teams rely on.
Cash flow forecasting is guesswork with a spreadsheet
Your finance team builds cash flow forecasts using historical averages and gut feel, ignoring seasonality, payment term variations, and customer payment behaviour. The result: surprise cash crunches, unnecessary short-term borrowing at 12-18% interest, and missed early payment discounts. Poor cash flow visibility for even a single month can cost tens of thousands in unnecessary interest.
Compliance is a fire drill every quarter
GST reconciliation, TDS verification, RBI reporting for NBFCs, transfer pricing documentation. Your team spends days cross-checking GSTR-2A with purchase registers, manually verifying TDS credits, and preparing regulatory filings that could be automated. Every manual touchpoint is a compliance risk. When the auditor finds a mismatch, it costs ten times more to fix than it would have to prevent.
From diagnosis to deployed system in six weeks
We do not sell you a platform. We study your finance workflows, identify the highest-ROI automation opportunities, and build custom AI systems that integrate with your existing stack.
Finance Workflow Audit
Days 1-3
We sit with your finance team. Not the CFO alone, but the analysts and accountants doing the actual work. We map every manual process: reconciliation workflows, data sources, reporting cadences, compliance touchpoints. We measure time spent per task, error rates, and downstream costs. Most companies find they have far more automatable work than they expected.
Deliverable: Finance Process Map with time-cost analysis and automation opportunity scoring
Data Readiness & Architecture
Days 4-7
We assess your financial data across every source: ERP, banking portals, payment gateways, GST systems, and internal tools. How clean is it? How complete? Can we actually access it? We design the data pipeline architecture that will feed your AI systems. This is where most AI projects fail. They skip data assessment and build models on unreliable inputs. We do not make that mistake.
Deliverable: Data readiness report, integration architecture blueprint, and API mapping document
System Build & Integration
Days 8-30
We build the AI system, whether it is a reconciliation engine, a forecasting model, a fraud detection layer, or a compliance automation pipeline. Every system is integrated with your existing tools: Tally, Zoho Books, SAP, custom ERPs, banking APIs, and GST portals. We test against your real data, not sample datasets. The system handles your edge cases, not textbook scenarios.
Deliverable: Working AI system deployed in your environment with integration to existing tools
Validation, Training & Handover
Days 31-42
We run the AI system in parallel with your existing process for two weeks. Your team validates outputs, flags exceptions, and builds confidence in the system. We train your finance team on using, monitoring, and interpreting the system. By handover, your team owns it. They are not dependent on us to keep it running.
Deliverable: Parallel run report, team training completion, monitoring dashboard, and operations playbook
Finance Workflow Audit
Days 1-3
We sit with your finance team. Not the CFO alone, but the analysts and accountants doing the actual work. We map every manual process: reconciliation workflows, data sources, reporting cadences, compliance touchpoints. We measure time spent per task, error rates, and downstream costs. Most companies find they have far more automatable work than they expected.
Deliverable: Finance Process Map with time-cost analysis and automation opportunity scoring
Data Readiness & Architecture
Days 4-7
We assess your financial data across every source: ERP, banking portals, payment gateways, GST systems, and internal tools. How clean is it? How complete? Can we actually access it? We design the data pipeline architecture that will feed your AI systems. This is where most AI projects fail. They skip data assessment and build models on unreliable inputs. We do not make that mistake.
Deliverable: Data readiness report, integration architecture blueprint, and API mapping document
System Build & Integration
Days 8-30
We build the AI system, whether it is a reconciliation engine, a forecasting model, a fraud detection layer, or a compliance automation pipeline. Every system is integrated with your existing tools: Tally, Zoho Books, SAP, custom ERPs, banking APIs, and GST portals. We test against your real data, not sample datasets. The system handles your edge cases, not textbook scenarios.
Deliverable: Working AI system deployed in your environment with integration to existing tools
Validation, Training & Handover
Days 31-42
We run the AI system in parallel with your existing process for two weeks. Your team validates outputs, flags exceptions, and builds confidence in the system. We train your finance team on using, monitoring, and interpreting the system. By handover, your team owns it. They are not dependent on us to keep it running.
Deliverable: Parallel run report, team training completion, monitoring dashboard, and operations playbook
Systems, not slide decks
Discovery Phase (Week 1)
- Finance process map with time-cost analysis for every manual workflow
- Automation opportunity matrix scored by ROI, feasibility, and data readiness
- Data quality assessment across all financial data sources
- Integration architecture blueprint for your specific tech stack
Build Phase (Weeks 2-4)
- Custom AI system built and deployed in your environment
- Integration with your ERP, banking systems, payment gateways, and GST portals
- Exception handling logic tuned to your specific business rules
- Real-time monitoring dashboard for system performance and anomalies
Validation Phase (Weeks 5-6)
- Two-week parallel run with documented accuracy metrics
- Finance team training and adoption support
- Operations playbook for ongoing system management
- Performance baseline for measuring ongoing ROI
Scope boundaries
This engagement focuses on AI systems for your finance function. For broader automation, analytics, or governance needs, see our related services.
End-to-end business process automation
If you need automation beyond finance (procurement, HR, operations) that falls under our broader Automation practice, which covers cross-functional workflow design.
AutomationBI dashboards and enterprise analytics
We build AI systems that generate financial insights, but if you need full-scale business intelligence, data warehousing, or cross-departmental analytics, that is our Data Analytics engagement.
Data AnalyticsAI policy, ethics frameworks, and regulatory compliance programmes
We build finance AI systems that are auditable and explainable. But if you need a full AI governance framework (policies, risk assessments, bias auditing) that is a separate engagement.
AI GovernanceIs this right for you?
Right for you if
- Your finance team spends more time on data processing than analysis and decision-making
- You have at least $1.2M in annual revenue and the transaction volume to justify AI investment
- Your monthly close takes longer than five business days and involves significant manual reconciliation
- You are dealing with multiple data sources (ERPs, banking portals, gateways, GST systems) that do not talk to each other
- You want a working system in six weeks, not a twelve-month transformation programme
Not right if
- Your finance function is one person with a Tally licence and straightforward books. You do not need AI; you need a good accountant.
- You are looking for off-the-shelf accounting software. We build custom systems, not resell SaaS products.
- You are not willing to share financial data access during the build process. We cannot build what we cannot see.
What these engagements look like in practice
Problem
A mid-sized NBFC processing 12,000 loan disbursements monthly was reconciling payments across 8 banking partners, 3 payment gateways, and their core lending platform manually. Their five-person operations team spent 18 hours per week on reconciliation alone, with a 6% error rate that triggered RBI audit observations.
What we did
Built an AI-powered reconciliation engine that ingests data from all 12 sources, handles format variations automatically, and uses probabilistic matching for fuzzy transactions. Integrated directly with their LOS and core banking system via API.
Outcome
Reconciliation time dropped from 18 hours to 50 minutes per week. Error rate fell from 6% to 0.3%. Monthly close accelerated by 4 business days. Annual cost saving of $22,000 in operational overhead.
Problem
A D2C brand doing $10M in annual revenue across Amazon, Flipkart, Shopify, and their own website had no unified cash flow visibility. Their finance team was building forecasts manually from marketplace settlement reports, payment gateway statements, and bank feeds, always two weeks behind reality. They had taken $250,000 in unnecessary short-term borrowing the previous year.
What we did
Built a real-time cash flow forecasting system that pulls settlement data from all four sales channels, maps payment terms and refund patterns, and generates 30/60/90-day cash flow projections updated daily. Layered in seasonality models trained on two years of their sales data.
Outcome
Cash flow forecast accuracy improved from 62% to 91%. Short-term borrowing reduced by $175,000 annually. Finance team reclaimed 12 hours per week previously spent on manual forecast preparation.
Problem
A SaaS company with 340 enterprise clients was processing revenue recognition manually across multiple contract types: annual, quarterly, usage-based, and hybrid. Their two-person finance team spent the first ten days of every month on revenue recognition and GST reconciliation, leaving no bandwidth for financial planning or investor reporting.
What we did
Built an automated revenue recognition engine that parses contract terms, applies ASC 606 rules, and generates GST-compliant invoicing automatically. Integrated with their billing system and Zoho Books. Added anomaly detection to flag unusual revenue patterns.
Outcome
Revenue recognition cycle reduced from 10 days to 1.5 days. GST reconciliation became same-day instead of a five-day exercise. Finance team reallocated 60% of recovered time to FP&A and investor relations.