Enterprise AI that runs in production — not a demo that impressed the board once.
Madgeek builds enterprise AI solutions that operate inside real business processes — custom AI agents, ML models, and workflow automation integrated into your existing ERP, CRM, and operations systems. Production-grade. Compliant. Auditable. AI that your operations team uses every day, not a proof of concept collecting dust.
Agents scaled in 3 months with AI quality monitoring
Reduction in manual approval steps — Tejas Networks
ML cost estimation replacing 3-day manual process
Shipping production AI systems since
Most enterprise AI projects fail after the demo.
The vendor showed a demo. The board approved a budget. A team built a proof of concept that worked on clean data. Then it hit production — messy data, edge cases, compliance requirements, integration with a 15-year-old ERP — and the whole thing stalled.
This happens because most AI vendors build for the demo, not for the operating environment. Production AI needs error handling for the 5% of cases where the model is wrong. It needs audit trails for compliance. It needs to degrade gracefully when data quality drops. It needs to integrate with SAP, not sit next to it.
The gap between "AI can do this" and "AI is doing this in our business" is engineering, not models. That's what we build. Not sure if your use case needs custom AI or if an off-the-shelf tool can handle it? Try the build vs buy assessment — it takes two minutes.
AI built for your actual business process.
We don't sell AI as a product. We build AI into the process that generates revenue, reduces cost, or eliminates manual work in your specific operation.
Every engagement starts with an AI Readiness Assessment — 5–7 days where we evaluate your data, map the process, identify where AI adds measurable value, and produce a build specification. If AI isn't the right answer, we say so.
Production architecture from day one: data residency, audit trails, confidence scoring, human-in-the-loop escalation, model monitoring, and integration with your existing systems — whether that's custom ERP, enterprise workflow software, or a CRM. These are not features we add at the end.
What we build.
Enterprise AI systems running in production today.
AI call quality monitoring — contact centre operations
Situation: A contact centre scaling from 50 to 80+ agents couldn't scale QA through manual call reviews. Every new agent meant more QA overhead.
What we built: AI agent that listens to call recordings, scores against quality criteria, flags issues, and generates coaching summaries. Fully automated. Runs continuously across every call.
Result: Scaled the operation without adding QA headcount. Quality scores improved because every call gets reviewed, not a sample.
50 → 80+ agents in 3 months
Manufacturing cost estimation — ML replacing spreadsheets
Situation: A coatings manufacturer spent 2–3 days per quote, calculating costs manually from product specifications in spreadsheets. Sales cycle was bottlenecked by engineering time.
What we built: ML model trained on historical cost data that generates accurate quotes from product specifications in real time. Deployed into their existing operations workflow.
Result: Sales team can quote immediately. Engineering time freed for production work instead of spreadsheet calculations.
3-day process → real-time output
Enterprise procurement automation — Tejas Networks
Situation: A publicly listed telecom equipment manufacturer ran multi-level purchase approvals on paper. No visibility into what was pending. Approvals took days.
What we built: Procurement workflow system with role-based approval chains, escalation rules, and real-time dashboards. AI-assisted categorisation and routing of purchase requests.
Result: Procurement cycle from days to hours. Full audit trail. Finance has real-time visibility into pending approvals.
90% reduction in paper-based approvals
Proof of concept vs production AI — what actually changes.
Most enterprise AI projects stall between PoC and production. Here's what production requires that a demo doesn't.
| Dimension | Proof of Concept | Production AI (Madgeek) |
|---|---|---|
| Data quality | Clean, curated dataset. Hand-picked examples. | Real production data with gaps, noise, and edge cases. Data pipeline handles quality issues. |
| Error handling | Crashes or gives wrong answers on edge cases. | Confidence scoring. Graceful degradation. Human escalation for low-confidence decisions. |
| Compliance | No audit trail. Data may leave your infrastructure. | Full audit trail. Data residency. Compliance pathway for SOC 2, HIPAA, GDPR. |
| Integration | Standalone demo. Manual data input. | Integrated with your ERP, CRM, operations systems. Real-time data flows. |
| Monitoring | None. If accuracy drifts, nobody knows. | Continuous model monitoring. Accuracy tracking. Automated retraining triggers. |
| Scale | Works on 10 examples. May not work on 10,000. | Architected for production load. Tested against real volumes. |
Have an AI use case stuck between demo and production?
Three concerns enterprise AI buyers always have.
"We can't trust AI to make decisions in our operations."
Production AI doesn't replace human judgment — it augments it. Every system we build includes confidence scoring. High-confidence decisions execute automatically. Low-confidence decisions escalate to a human. You define the thresholds. The AI handles the 80% that's routine; humans handle the 20% that requires judgment.
"Our data isn't ready for AI."
No company's data is "AI ready" out of the box. The AI Readiness Assessment evaluates your data, identifies gaps, and defines what needs to happen before the AI layer can work. Sometimes that's data cleanup. Sometimes it's building a data pipeline. Sometimes it's scoping the AI to a subset of data that is ready. We don't need perfect data — we need enough data with a clear path to more.
"We don't have in-house AI expertise to maintain this."
That's what the monitoring retainer is for. We maintain the models, monitor accuracy, handle retraining, and manage the infrastructure. Most enterprise AI clients stay on a monthly retainer. You don't need to hire an ML team — you need a production AI system that works and a team that keeps it working.
How an enterprise AI engagement works.
Every engagement starts with the AI Readiness Assessment. If the use case doesn't justify the build, we tell you before you spend $60K.
Common questions about enterprise AI solutions.
Have a specific AI use case?
Describe it. We'll tell you if it's worth building — honestly.
AI that runs your actual process. Not a demo.
Tell us the process, the data, and the outcome you're looking for. We'll tell you whether AI is the right answer and what a realistic build looks like.
Discuss your AI use case