AI agents for business — built and running in production.
Custom AI agents that automate multi-step business processes end-to-end. We scaled a contact centre from 50 to 80+ agents in 3 months using a custom AI call quality monitoring system. We built a procurement agent that eliminated 90% of paper-based approvals at a publicly listed enterprise. These are production systems, not demos.
Agents scaled in 3 months — AI call quality monitoring
Paper approval reduction — Tejas Networks procurement agent
Cost estimation — manufacturing ML agent replacing a 3-day spreadsheet process
Production AI agents running in live business operations today
What an AI agent actually does.
An AI agent is software that executes a business task autonomously — it receives an input, uses tools to gather or process information, makes a decision, and takes an action. Unlike a chatbot that waits for prompts, a custom AI agent completes a workflow end-to-end without a human at each step.
The difference between a working agent and a proof-of-concept isn't the model. It's reliable data access, failure recovery, audit trails, and human escalation design. Most AI agent development projects fail in production because those four things weren't designed from the start.
What separates production agents from demos:
Three types of custom AI agents we build.
Process automation agents
Multi-step approvals, compliance workflows, document processing — executed without a human at each step.
Example: Procurement approval agent — Tejas Networks, 90% reduction in paper-based steps
Intelligence agents
Score inputs, qualify records, analyse data, surface signals — replacing human analyst work.
Example: Call quality monitoring agent — scaled a contact centre from 50 to 80+ agents in 3 months
Integration agents
Connect data sources, update systems, trigger actions based on conditions across tools.
Example: CRM lead scoring agent — running in production for a B2B sales team
For large organisations with compliance and data residency requirements:
Enterprise AI AgentsWhat AI agents are automating in businesses today.
If your team is doing any of these manually, an AI agent can handle the execution while your people focus on the exceptions that actually need judgment.
Lead qualification
Scores inbound leads against ICP criteria and routes qualified ones to sales — before a rep manually reviews each one.
Call quality monitoring
Listens to calls, scores against your quality rubric, flags issues, and generates coaching notes. No manual spot-checking at scale.
Procurement approvals
Routes purchase requests through the right approvers based on spend limits and policy rules. Enforces policy programmatically.
Document extraction
Reads invoices, contracts, or intake forms and extracts structured data directly into your systems. No manual data entry.
CRM scoring and enrichment
Keeps pipeline records enriched with company signals and scored by win likelihood. Surfaces the deals worth working first.
Quote generation
Produces accurate quotes from product specs or customer inputs without a human building each one from a spreadsheet.
Inventory and supplier alerts
Monitors stock levels, triggers reorder workflows, and notifies the right people when thresholds are hit or suppliers are late.
AI virtual agent
Handles inbound customer service queries, qualifies requests, resolves common issues autonomously, and escalates to a human when context requires it — 24/7, without headcount.
After-hours intake
Qualifies incoming requests outside business hours, captures the context your team needs, and routes to the right person when they're back.
Quality control reporting
Monitors production or service data against thresholds, scores against criteria, and escalates anomalies before they become problems.
Document analysis and RAG
Indexes internal documents — policies, SOPs, contracts — and answers questions with cited sources. Replaces the person everyone asks because they know where things are.
Reporting and forecasting
Pulls data from multiple systems, builds reports automatically, and flags trends before they become problems. No more Monday-morning spreadsheet assembly.
Anomaly detection and alerts
Watches system metrics, transaction patterns, or operational data in real time. Flags deviations from normal before they escalate — not after.
Enterprise knowledge retrieval
Connects to your internal knowledge base — Confluence, Notion, SharePoint, custom databases — and retrieves accurate answers grounded in your actual documentation.
Not sure if your process is the right fit for an AI agent? The Design Sprint answers exactly that.
Book a Design SprintIndustries where we've shipped production AI agents.
The same agent architecture applies across sectors — but the data, integration patterns, and edge cases are different. These are the industries we have real delivery experience in.
AI call quality monitoring scaled a contact centre from 50 to 80+ agents in 3 months — no additional QA headcount. Every call scored automatically.
Four AI agents we've shipped to production.
As a custom AI agent development company, every claim we make is backed by a system running in a real business today. These aren't case studies we've written up — they're agents still in production.
Call Quality Monitoring Agent
Listens to call recordings, scores against quality criteria, flags issues, and generates coaching summaries automatically. Replaced a QA function that would have required dedicated headcount to operate at scale.
agents scaled in 3 months without adding QA headcount
Procurement Workflow Automation Agent
Multi-level approval routing with budget enforcement, policy rule application, and full audit trail. Integrated with existing procurement systems at Tejas Networks — a publicly listed enterprise.
reduction in paper-based approvals at Tejas Networks
Manufacturing Cost Estimation Engine
ML model that generates accurate cost quotes from product specifications in real time, replacing a manual spreadsheet process that took 2–3 days per quote.
quote turnaround — from spreadsheet to real-time ML output
CRM Lead Scoring Agent
Qualifies and prioritises deals based on historical deal data, company signals, and ICP criteria. Running in production for a B2B sales team, surfacing the deals worth working first.
pipeline qualification — running in production for a B2B sales team
Start with the Agent Design Sprint.
Before committing to a build, we spend 5–7 days mapping your use case to a working architecture. We scope the agent's tools, define data access requirements, design failure modes, and produce a full technical spec. If the use case isn't viable, we tell you before you commit to a build that won't work.
The Design Sprint produces an architecture document you own regardless of who builds it. Most clients who complete the sprint proceed to build with us. A few don't — and those are the right outcomes too.
Book a Design SprintHow an engagement works
Common questions about AI agent development.
Still have questions?
Talk to us directly — no forms, no waiting for a sales rep.
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Production conversational AI — customer service agents, internal workflow bots, and enterprise chat interfaces.
Have a process you want to automate?
Describe the workflow. We'll tell you whether a custom AI agent is the right tool, what it would need to access, and what a realistic build looks like. No pitch. Start with the Design Sprint.
Tell us what you need