AI that does
the work.
Not the pitch.
At some point, exploring AI stops being strategy and starts being an excuse. We've built 50+ agentic systems across sales, logistics, healthcare, and financial services. They run 24/7. They don't need managing.
Live agent reasoning simulation
Your team is
doing work that
shouldn't need a human.
The average knowledge worker spends 4.5 hours a day on tasks that follow a predictable pattern. Moving data between systems. Writing the same email in a different way. Checking if something happened so they can trigger the next thing. That's not work. That's operating a process. Agents do that instead.
Real agents.
Real tasks.
Real results.
An agent isn't a chatbot. It's software that perceives its environment, makes decisions, and takes action — inside your CRM, your inbox, your ops stack — without a human in the loop. These are the types we've built and deployed.
Identifies and qualifies leads from your CRM data, runs personalised outreach sequences, updates opportunity stages, and books discovery calls — without a rep touching it until a meeting lands in the calendar.
Reads incoming tickets, retrieves relevant account history, drafts and sends responses to common queries, escalates complex cases with full context pre-loaded. Handles 60–80% of ticket volume without human involvement.
Monitors deal health across your CRM, flags stalled opportunities, triggers re-engagement workflows, and surfaces risk signals to sales managers before deals go cold. Runs every night without being asked.
Pulls data from multiple ops systems, validates against rules, makes routing and allocation decisions, updates records across platforms, and flags exceptions that genuinely need human judgement.
Monitors search performance, identifies content gaps, drafts optimised pages for human review, publishes approved content, and tracks ranking changes — closing the loop between insight and execution.
Continuously monitors contact and account records for duplicates, stale data, missing fields, and enrichment opportunities. Applies fixes within defined rules. Escalates edge cases. Your CRM stays clean automatically.
We don't hand you
a model. We hand
you a working agent.
Every agentic system we build goes through the same process: define the trigger, design the decision tree, constrain the guardrails, test against edge cases, deploy, measure. No demos that never ship.
Six agents.
Running now.
These aren't proof-of-concept deployments. Every agent below is live, running in a production environment, and processing real decisions for a real business.
Mammoth Securities runs 200+ daily field deployments for camera and access control installations. Once trucks left the depot, dispatch was completely blind — and field techs were calling in for answers that a specialist already knew. Amroar built Mammoth.OPS: a custom AI platform with live GPS dispatch, an AI tech support layer trained on their full hardware catalogue, and a mobile app for field techs. Dispatch call volume dropped 40%. Fleet visibility went from zero to live.
Full Case StudyEnflytt manages freight across Australia and Asia-Pacific. Every shipment started with a coordinator assembling a quote manually — 24 to 48 hours for something that should take seconds. Dispatch was entirely manual with no routing logic. Amroar built a custom AI coordinator trained on Enflytt's shipping lanes and carrier contracts, a real-time rate builder, a visual shipment pipeline, and automated post-delivery follow-up. Dispatch turnaround dropped 35%. Manual coordination calls dropped 60%.
Full Case StudyIPQS sells fraud detection and threat intelligence APIs. Their sales team was manually reviewing thousands of freemium accounts, trying to identify which ones showed upgrade signals — a process that required reading usage data, firmographic signals, and engagement patterns simultaneously. Amroar built an AI summarisation engine embedded inside Agentforce that reads each lead in real time, scores upgrade probability, and surfaces the top conversion candidates directly in the agent's daily workflow. Lead qualification sped up by 50%. High-conversion leads found increased by 35%.
Full Case StudyHLW Travel Inc. conducts nightly drive-by inspections across commercial properties — identifying lighting outages, electrical faults, and signage failures. Detection was happening. But every step downstream was manual: customers weren't notified until someone drafted an email, findings lived in spreadsheets, recurring schedules required constant admin overhead. Amroar replaced every manual handoff with four autonomous agents: field detection and classification, customer notification within minutes, scheduling automation, and documentation. Admin overhead dropped 60%. Customer contact speed improved 85%.
Full Case StudyClariva Group processes over 14,000 insurance claims per month. Every claim was manually reviewed, routed, and responded to — experts buried in intake work rather than actual adjudication. Amroar built a five-agent OpenAI GPT-4o system: intake and routing agent, document extraction agent, fraud investigation agent, decision agent, and claimant communication agent. 73% of straightforward claims now resolve without human involvement. Average resolution time dropped from 3.8 days to 4.2 minutes.
Full Case StudyLexara Group advises on M&A transactions across financial services and technology. Every deal required weeks of associate time reading data rooms of 2,000–6,000 pages — contracts, employment agreements, IP assignments, regulatory filings — before a single strategic question could be asked. Amroar built a four-agent Claude system using Anthropic's 200K token context window. One agent ingests the full data room in a single pass, a second flags risk clauses, a third maps contractual obligations, and a fourth produces a structured legal brief. The process now completes before the first associate opens a document.
Full Case StudyThe stack behind
every agent we build.
We don't have a preferred stack — we use what's right for the agent and the client's existing infrastructure. Every build starts with an architecture decision, not a tool preference.
When agentic AI makes sense — and when it doesn't.
Stop exploring.
Start deploying.
Tell us the process that's costing your team the most time. We'll tell you whether an agent can fix it and what it would take to build one.
Shivam or Sonam will be on the call. Not a junior consultant.