Your finance team was hired to think. Instead, they match rows in spreadsheets.

AI systems for finance teams stuck in manual reconciliation, bad forecasts, and compliance overhead. Solutions that cut processing time by 60-80% for mid-market companies.

The Problem

Your finance team is the most expensive data entry department in the company

Manual reconciliation eats 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, run VLOOKUP formulas that break every time a vendor changes their invoice format. Cash reconciliation alone takes 30+ hours monthly. One delayed source and the entire monthly close slips.

Your fraud detection is a quarterly audit: A lot of mid-market companies discover fraud during the quarterly audit, weeks or months after the damage is done. Your analysts review transactions in batches, not real time. Anomalies that should be flagged in seconds sit unnoticed in spreadsheets. The attacks have gotten more sophisticated than manual controls can handle.

Cash flow forecasts run on intuition and averages: Your finance team builds cash flow forecasts on historical averages and intuition, ignoring seasonality, payment term variations, and customer payment behaviour. That means surprise cash crunches, unnecessary short-term borrowing at 12-18% interest, and missed early payment discounts. Poor cash flow visibility for a single month can cost tens of thousands in avoidable interest.

Compliance turns into a fire drill every quarter: GST reconciliation, TDS verification, RBI reporting, transfer pricing documentation. The finance function spends days cross-checking GSTR-2A with purchase registers, 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 to prevent.

Our Approach

From diagnosis to deployed system in six weeks We do not sell you a platform. We study your finance workflows, find the highest-ROI automation opportunities, and build custom AI systems that plug into your existing stack.

Phase 1 — Finance workflow audit (Days 1-3): We work with your finance team directly, the analysts and accountants doing the actual work, not just the CFO. We map every manual process: reconciliation workflows, data sources, reporting cadences, compliance touchpoints. We measure time per task, error rates, and downstream costs. Nearly every team finds they have far more automatable work than they realised. Deliverable: Finance process map with time-cost analysis and automation opportunity scoring

Phase 2 — Data readiness & architecture (Days 4-7): We assess your financial data across every source: ERP, banking portals, payment gateways, GST systems, internal tools. How clean is it? How complete? Can we access it? We design the data pipeline architecture that feeds your AI systems. Most AI projects skip data assessment and build on unreliable inputs. We do not. Deliverable: Data readiness report, integration architecture blueprint, and API mapping document

Phase 3 — System build & integration (Days 8-30): The deliverable is a working AI system, whether that is a reconciliation engine, forecasting model, fraud detection layer, or compliance automation pipeline. It plugs into your existing tools: Tally, Zoho Books, SAP, custom ERPs, banking APIs, GST portals. Tested against your data, not samples. It handles your edge cases, not textbook scenarios. Deliverable: Working AI system deployed in your environment with integration to existing tools

Phase 4 — Validation, training & handover (Days 31-42): The AI system runs alongside your existing process for two weeks. Your team validates outputs, flags exceptions, and builds confidence in the results. We train your finance team to use, monitor, and interpret the system. By handover, you own it. No dependency on us to keep it running. Deliverable: Parallel run report, team training completion, monitoring dashboard, and operations playbook

Deliverables

Who This Is For

Right for you if: Your finance team spends more time on data processing than analysis and decisions. 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 with heavy manual reconciliation. You are dealing with multiple data sources (ERPs, banking portals, gateways, GST systems) that do not talk to each other. You need a working system in six weeks. Twelve-month transformation programmes are a non-starter..

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 develop 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..

Use Cases

NBFC / Fintech: A mid-sized NBFC processed 12,000 loan disbursements monthly and reconciled payments across 8 banking partners, 3 payment gateways, and their core lending platform by hand. Their five-person operations team spent 18 hours per week on reconciliation alone, with a 6% error rate that triggered RBI audit observations. — Built a reconciliation engine that pulls data from all 12 sources, handles format variations automatically, and uses probabilistic matching for fuzzy transactions. Plugged directly into 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 sped up by 4 business days. Annual cost saving of $22,000 in operational overhead.

E-commerce / D2C: A D2C brand with $10M in annual revenue across Amazon, Flipkart, Shopify, and their own website had no unified cash flow visibility. Their finance team built 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. — Built a real-time cash flow forecasting system that pulls settlement data from all four sales channels, maps payment terms and refund patterns, and produces 30/60/90-day cash flow projections updated daily. Added seasonality models trained on two years of their sales data.. Outcome: Cash flow forecast accuracy improved from 62% to 91%. Short-term borrowing fell by $175,000 annually. Finance team reclaimed 12 hours per week previously spent on manual forecast preparation.

B2B SaaS: A SaaS company with 340 enterprise clients processed 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, with no time left for financial planning or investor reporting. — Built an automated revenue recognition engine that parses contract terms, applies ASC 606 rules, and produces GST-compliant invoicing automatically. Plugged into their billing system and Zoho Books. Added anomaly detection to flag unusual revenue patterns.. Outcome: Revenue recognition cycle dropped from 10 days to 1.5 days. GST reconciliation became same-day instead of a five-day exercise. Finance team moved 60% of recovered time to FP&A and investor relations.

Results

What finance AI delivers

NBFC / Financial Services: 87% reduction in reconciliation time, $27,000 annual cost saving. An NBFC with $14M in AUM had a six-person finance operations team that spent most of their hours reconciling loan repayment data across eight banking partners, three payment gateways, a core banking system, and two collection platforms. Each source had its own format and settlement cycle. The team had built 47 Excel templates for format conversion alone. Monthly close took 12-14 business days. We built a reconciliation engine that pulls all sources, normalises formats automatically, and applies probabilistic matching for fuzzy transactions: partial payments, split settlements, reversed transactions, timing differences. Reconciliation time dropped from 18 person-hours per week to under 50 minutes. Error rates fell from 6% to 0.3%. Monthly close shortened from 14 days to 6. Three team members moved to financial planning and risk analysis. Total annual saving: $27,000, against a build cost recovered in under four months.

Frequently Asked Questions