AI application development that ships to production — not a proof of concept that impressed once.
Madgeek builds custom AI applications that run inside real business operations — ML models, LLM integrations, agent systems, and computer vision deployed into your existing workflow. We have three production AI systems running today. The gap between "AI can do this" and "AI is doing this in our company" is engineering. That is what we build.
Production AI systems running in client operations today
Agents scaled in 3 months with AI quality monitoring
Clutch rating across 50+ projects
Building production software since 2017
Most AI projects die between proof-of-concept and production.
The demo worked. The model hit 92% accuracy on test data. The team was excited. Then production happened — messy real-world data, edge cases the training set never saw, an integration with a system that was never designed for real-time ML calls — and the project stalled. Six months later the prototype is still a prototype.
Consultancies deliver architecture diagrams and "AI strategy" decks. They scope beautifully. They don't ship. When you need an AI application that actually runs inside your operations — that handles the 5% of cases where the model is wrong, that degrades gracefully when data quality drops, that retrains itself as your business changes — you need an engineering team that has done it before.
Internal teams often have the domain knowledge but lack ML infrastructure experience — model serving, monitoring, retraining pipelines, fallback handling. Building that infrastructure from scratch adds 6-12 months before the AI application is operational. That is time your competitors are using.
Have an AI application you need built for production?
Describe your use caseHow Madgeek builds AI applications.
We don't sell AI as a technology layer. We build AI into the specific business process that generates revenue, reduces cost, or eliminates manual work in your operation. The model is one component. The system around it — data pipelines, monitoring, error handling, retraining, integration — is what makes it production-grade.
Every engagement starts with an Agent Design Sprint — 5 to 7 days where we evaluate your data, map the process, and produce a build specification with architecture, model selection, and realistic cost. If AI is not the right answer for your problem, we say so before you spend $50K.
AI is included in every Madgeek engagement — it is not charged as a separate line item. We build with the assumption that AI software development is a standard part of modern applications, not a premium add-on. Production architecture from day one: monitoring, fallback handling, confidence scoring, and model-portable design so you are never locked to one vendor.
What we build.
AI applications running in production today.
AI call quality monitoring platform
Situation: A contact centre scaling from 50 to 80+ agents needed to review every call for quality — not a sample. Manual QA could not keep pace with hiring.
What we built: AI application that listens to call recordings, scores against quality criteria, flags compliance issues, and generates coaching summaries. Fully automated. Runs continuously across every call.
Agents scaled in 3 months without adding QA headcount
Manufacturing cost estimation engine
Situation: A coatings manufacturer spent 2-3 days per quote, manually calculating costs 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 cost estimates from product specifications in real time. Deployed into the existing operations workflow.
Manual quote process replaced with real-time ML estimation
Custom AI application vs AI SaaS vs internal ML team.
Three paths to AI in your business. Each fits a different situation.
| Dimension | Custom AI Application | AI SaaS Platform | Internal ML Team |
|---|---|---|---|
| Fit to your process | Built around your exact workflow, data, and rules | You adapt your process to fit the tool's assumptions | Can be exact fit, but takes 12-18 months to staff and ship |
| Time to production | 8-14 weeks after sprint | Days to weeks (if your process fits) | 6-18 months to hire, build infrastructure, then build |
| Data ownership | Your infrastructure. Your data. Full control. | Their infrastructure. Your data on their servers. | Full control, but you build and maintain everything |
| Accuracy on your domain | Trained on your data. Retrains continuously. | General-purpose model. May not handle your edge cases. | Can be high, but depends on team capability |
| Ongoing cost | $2K-$5K/month monitoring retainer | Per-seat or per-usage pricing. Scales with volume. | $300K-$600K/year for a 2-3 person ML team |
| Monitoring and retraining | Built in. Continuous. Automated alerts. | Vendor-managed. You have no visibility. | You build and maintain it yourself |
Concerns we hear from every AI buyer.
“Our last AI project never made it to production.”
That is the most common outcome with AI vendors who build for the demo. We start every engagement with a 5-7 day Agent Design Sprint that produces a production architecture — not a slide deck. The sprint identifies data gaps, integration complexity, and deployment requirements before the build begins. If the use case does not justify the build, we say so during the sprint, not six months and $80K later.
“We don’t have enough data.”
No company has perfectly clean, perfectly labelled data on day one. The Agent Design Sprint evaluates what you have, identifies what is missing, and scopes the AI application to work with the data that exists today — with a pipeline that improves over time. Some AI applications need 50 examples to start. Others need 50,000. We define the minimum viable dataset during the sprint and build the data pipeline as part of the application, not as a separate project.
“How do you handle model accuracy and monitoring?”
Every AI application ships with continuous monitoring — accuracy tracking, drift detection, confidence scoring, and automated alerts when performance drops below defined thresholds. Low-confidence decisions escalate to a human. The monitoring retainer covers retraining, model updates, and infrastructure management. Production AI without monitoring is a system waiting to break silently. We do not ship without it.
How an AI application engagement works.
Every engagement starts with the Agent Design Sprint. If the use case does not justify the build, we tell you before you commit to a full project.
Production AI systems we've shipped.
AI platforms for operations, manufacturing, and enterprise workflow — built and maintained in production.
AI Call Center Software for Quality Monitoring
Enterprise client
A contact centre operation was scaling rapidly but had no automated way to monitor agent call quality at volume. Madgeek built custom AI call center software that scored agent calls against domain-specific criteria, surfaced coaching opportunities, and tracked performance trends in real time. The result: the operations team scaled from 50 to 80+ agents in 3 months without adding QA headcount.
agents scaled in 3 months
Custom Purchase Requisition Software for Enterprise
Tejas Networks Ltd.
A publicly listed telecommunications manufacturer was running procurement entirely on paper forms and manual approval chains. Madgeek built custom purchase requisition software with automated approval routing, purchase order generation, real-time inventory tracking, and document management. The result was a 90% reduction in paper-based approvals and full visibility across the procurement lifecycle.
reduction in paper-based approvals
Call Center SaaS Platform — ODC Partnership
Lead Tact
Lead Tact, a US-based call center SaaS company, needed a dedicated engineering team to build their platform from scratch — not a one-time vendor build, but an ongoing ODC partnership. Madgeek's dedicated team built and maintains the full product: dynamic call scripts, automated QA monitoring, real-time client portals, analytics dashboards, and lead management workflows. The same engineers from month one are still on the product, shipping features on Lead Tact's sprint cadence.
reduction in admin time for call center operations
Common questions about AI application development.
Have a specific AI application you need built?
Describe the process, the data, and the outcome. We will tell you if it is worth building.
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Multi-step AI workflows that handle complex business processes autonomously.
Your AI application, running in production. Not in a slide deck.
Tell us the process, the data, and the outcome you need. We will tell you whether AI is the right answer and what a realistic build looks like.
Describe your AI use case