AI chatbots built for production — not platform demos that break on the first edge case.
Madgeek builds production AI chatbots — customer service agents, internal workflow automation bots, AI SDRs, enterprise knowledge assistants, and contact centre AI — for companies in the US, UK, and Canada. Custom LLM integration. Not Zendesk AI. Not Intercom. Not a no-code builder. A production AI system built for your specific escalation logic, your data, and your compliance requirements.
Building enterprise software since 2017
Enterprise systems shipped to production
Production AI systems deployed in operations
Clutch rating from verified reviews
Standard chatbot platforms hit their ceiling the moment your logic gets complex.
Zendesk AI, Intercom, Freshdesk — these platforms handle FAQ deflection well. They don't handle complex escalation logic, multi-system data lookups, or compliance-governed conversation flows. When a customer asks a question that requires pulling data from three internal systems, applying eligibility rules, and following a regulatory disclosure script — the platform's rule builder doesn't support it. Your support team handles it manually. The platform is overhead.
The "no-code AI chatbot" products target the same ceiling. Drag-and-drop conversation builders work for simple decision trees. They don't work when the conversation flow depends on real-time data from your CRM, your ERP, or your customer account system. The integrations either don't exist or require the kind of middleware that costs more than a custom build.
LLM-powered chatbots built on top of ChatGPT or Claude without proper guardrails produce confident wrong answers. A customer service AI that fabricates policy details, quotes incorrect pricing, or gives compliance-incorrect responses causes more operational damage than no AI at all. Production AI requires grounding — accurate retrieval from your knowledge base, constrained output, escalation paths when confidence is low, and audit trails of every response.
The cost isn't the platform licence. It's the support ticket volume that the platform was supposed to reduce but didn't. It's the compliance exposure from AI responses that weren't reviewed. It's the engineering time maintaining integrations that break every time the platform updates.
Building a customer service AI or internal workflow bot that needs to work reliably in production? Let's talk.
Book a 30-minute callCustom AI chatbots grounded in your data — with the escalation logic your operation actually runs.
We build on the LLM that fits your use case — GPT-4o, Claude 3.5, Gemini, or open-source models for data sovereignty requirements. The chatbot is grounded in your knowledge base, your product data, and your customer records through RAG (Retrieval-Augmented Generation) — so it answers from your content, not from general training data.
Production AI requires more than a prompt. It requires a retrieval system that finds the right information, a confidence threshold that triggers escalation before the AI makes a mistake, a guardrail system that prevents out-of-scope responses, and an audit trail of every interaction for compliance review. We build all of it.
We've deployed AI in production for a contact centre operations platform — monitoring call quality across 50+ agents in real time. The same engineering discipline applies to customer service chatbots, internal knowledge assistants, and AI SDRs. AI is included on every engagement. No separate AI module. No extra charge.
What we build for AI chatbot deployments.
We build AI systems that run in production — not proof-of-concepts that don't survive real data.
AI in production requires integration depth, guardrail engineering, and the kind of operational resilience that only comes from shipping real systems — not demo environments.
AI in production operations: 50 to 80+ agents in 3 months
Situation: A growing operations team needed to scale quality assurance from 50 to 80+ agents without adding management headcount. Manual monitoring couldn't keep pace with growth, and quality consistency was slipping across the contact centre.
What we built: Custom AI-powered call quality monitoring with automated scoring, performance dashboards, and coaching workflows. The AI system processes every interaction, flags quality issues, and surfaces patterns — replacing manual review that would have required dedicated QA staff.
50 → 80+ agents scaled in 3 months
Tejas Networks: enterprise platform with strict audit requirements
Situation: A publicly listed telecom equipment manufacturer ran multi-level purchase requisition approvals on paper forms. Finance and operations had no visibility into pending, approved, or blocked requests. Strict compliance requirements for audit trails and access control.
What we built: We built a purchase requisition platform with role-based approval chains, configurable escalation rules, real-time dashboards, and full audit trail on every transaction. Compliance-grade access control with department-level permissions and multi-tier approval workflows.
90% reduction in paper-based approvals
Manufacturing ERP: AI cost estimation integrated into operations
Situation: A manufacturer needed production cost estimation that pulled live data from inventory, procurement, and production schedules. Manual estimation was slow, inconsistent, and blocked sales quoting cycles.
What we built: Custom AI-assisted cost estimator integrated into a purpose-built ERP. The AI processes real-time materials data and production constraints to generate estimates — with full audit trail and human review step before quote release.
Complex AI + ERP integration in production
What we build for companies that need production AI chatbots.
Every AI chatbot engagement is scoped to your specific workflows, data sources, and compliance requirements — not assembled from a template.
Handles support queries, account lookups, order status, returns, and complaint triage. Grounded in your knowledge base and customer data. Escalates to a human agent when confidence is below threshold. Built for your support workflow, not a generic template.
HR policy assistant, IT helpdesk, procurement approvals, finance query handling, onboarding support. Deployed in Slack, Teams, or as a standalone web interface. Accesses your internal systems securely.
Lead qualification, meeting booking, CRM data enrichment, objection handling, and follow-up sequence automation. Knows your product, your ICP, and your qualification criteria.
Real-time call monitoring, quality scoring, agent coaching prompts, call summarisation, and performance analytics. Deployed on top of your existing telephony stack.
Three concerns companies raise about AI chatbots.
"We tried a platform AI chatbot and it gave wrong answers. How is custom different?"
Platform AI chatbots answer from their training data or from a generic knowledge base — not from your specific policies, your current pricing, or your actual account data. Custom AI chatbots are grounded through RAG: every response is retrieved from your content before being generated. When retrieval confidence is low, the system escalates rather than guessing. The wrong-answer problem is an architecture problem, and the custom architecture solves it.
"Our chatbot needs to access live customer data from our CRM and ERP. Is that possible?"
Yes. The AI agent connects to your CRM, your order management system, your knowledge base, and any other data source via API or direct database connection. When a customer asks about their account status, the agent retrieves the current data in real time — not from a cached snapshot. The integration layer handles authentication, rate limits, and error recovery.
"How do you handle sensitive customer data in an AI system?"
Conversation data is logged with access controls and encryption. PII is handled according to GDPR and CCPA requirements — we design the data retention and deletion workflow into the system from the start. LLM API calls can be routed through your own Azure OpenAI or AWS Bedrock deployment for data sovereignty if your compliance requirements prevent data leaving your infrastructure.
How AI chatbot engagements work.
We show you a working prototype against your actual data before you commit to the full build.
AI systems and enterprise software we've shipped.
Production platforms for complex operations, AI-powered workflows, and enterprise environments — built and maintained long-term.
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 chatbot development.
Have a question we didn't cover?
Talk to us directly — no forms, no sales reps.
Start with a working prototype — not a vendor pitch.
We build a prototype against your actual data before you commit to the full build. Discovery calls are 30 minutes. You see real responses to real queries within 4 weeks.
Book a discovery call