Every engagement begins with a specific problem and ends with a documented result. Here's what we've built.
A fintech client processing 10,000+ loan applications monthly needed to automate document verification, credit analysis, and compliance checking — a workflow requiring coordination across 7 different systems.
Manual processing took 4–6 days per application with 12% error rate. The team of 40 analysts was overwhelmed, and regulatory scrutiny was increasing. No existing automation tool could handle the multi-system coordination and judgment calls required.
Built a 12-agent system using AutoAgent Studio. Specialised agents handle document OCR, KYC verification, credit bureau queries, fraud pattern matching, regulatory compliance checks, and final decision synthesis. The orchestrator manages exceptions and escalation to human reviewers.
Processing time reduced from 4–6 days to 8 hours. Error rate dropped from 12% to 1.4%. 85% of applications handled fully autonomously. Analyst team refocused on complex edge cases. ROI positive within 90 days of deployment.
A healthcare tech company building an AI assistant for clinical decision support needed rigorous validation before deployment. A hallucinated drug dosage or missed contraindication could be life-threatening.
No testing infrastructure existed. The team was manually reviewing AI outputs sporadically. The product was 6 weeks from launch with regulatory sign-off pending. They needed a systematic approach to validation, fast.
Deployed TestSentinel with a custom medical domain evaluation suite. Built 850 test cases covering drug interactions, dosage accuracy, contraindication detection, and source citation. Integrated adversarial testing for clinical edge cases. Wired into GitHub Actions to block any PR that degraded quality below thresholds.
3 critical hallucination scenarios caught in the final 2 weeks before launch. All were fixed before the product shipped. Post-launch: zero AI-related clinical incidents in 6 months of operation. The evaluation framework became part of the company's regulatory documentation package.
A 200-store retail chain across Maharashtra was managing inventory with spreadsheet-based forecasting, leading to chronic overstock in slow-moving lines and stockouts on high-demand products.
Existing forecasting was manual, took 3 days per planning cycle, and had 65% accuracy on weekly demand. Stockouts were costing 8% of potential revenue. Overstock was tying up working capital in 40,000+ SKUs.
Built a real-time demand forecasting pipeline using PredictFlow. Ingested 2M+ daily transactions via Kafka. Deployed Prophet + LSTM ensemble models per SKU category, with seasonality, promotional calendar, and local event features. Added anomaly detection for sudden demand spikes with auto-alerting to store managers.
Forecast accuracy improved from 65% to 94% on weekly demand. Stockouts reduced by 62%. Overstock inventory value reduced by 28%. Planning cycle reduced from 3 days to real-time. System paid for itself in 4 months through inventory savings alone.
A 120-engineer SaaS company was shipping 15–20 PRs per day. Security reviews were a bottleneck — their single AppSec engineer was reviewing only 10% of PRs before merge.
90% of PRs merged without security review. Two critical SQL injection vulnerabilities had shipped to production in Q3. Manual review was too slow and didn't scale with their sprint cadence. The AppSec team was burning out on repetitive pattern detection.
Deployed CodeGuardian into their GitHub workflow. The AI agent performs AST-level analysis plus LLM-powered semantic review on every PR. It comments directly on vulnerable lines with fix suggestions, creates Jira tickets for critical findings, and escalates to the AppSec engineer only for novel or complex issues.
100% of PRs now reviewed before merge. 340 vulnerabilities caught in the first 90 days — including 12 critical SQL injection patterns. Zero critical CVEs shipped to production since deployment. AppSec engineer freed to focus on architecture reviews and threat modelling.
A 60-person law firm was spending 30% of paralegal time on document retrieval — searching through 200,000+ contracts, judgments, and precedents to answer partner and client queries.
Document search was keyword-based and returned irrelevant results. Paralegals would spend 2–4 hours per complex query. Partners couldn't get quick answers to client questions without significant research time. Billing efficiency was suffering.
Built DataNexus on their document corpus. 200,000+ documents ingested, chunked, and indexed using Weaviate hybrid search. The system answers natural language queries with cited source documents, clause-level highlighting, and confidence scores. Access controls ensure each lawyer only sees documents within their practice area.
Average document research time reduced from 2–4 hours to 12 minutes. Paralegal capacity freed up by 30%. Partners now handle routine research queries directly. Document retrieval accuracy (precision@5) at 91%. Client response times improved by 60%.
