Support queues full of routine work
Agents spending most of their time classifying and routing tickets instead of resolving the ones that actually need a person.
Custom AI Agents Development
Custom AI agents that run the routine work
Taycon AI builds custom AI agents that handle repetitive work across support, sales, finance, and operations, deployed inside the tools your team already uses. Not chatbots, not standalone products. Working systems wired into your CRM, helpdesk, ERP, and data.
What it is
AI agents that work inside your operations
An AI agent perceives a situation, decides what to do, and acts on its own, across multiple steps, inside the tools your team already uses. A support triage agent doesn't just route a ticket. It reads it, judges intent and urgency, pulls account data from your CRM, drafts a response, and routes it, in seconds, without a person touching each case.
Every agent Taycon AI builds has a defined human oversight model. The agent carries the cognitive load. People make the calls that need judgment and approve outputs before anything reaches a customer.
Support triage agents
Classify tickets by intent and urgency, draft responses for routine cases from your knowledge base and account data, and route complex issues with full context, inside your helpdesk.
CRM and sales agents
Read call notes, emails, and meetings, update CRM records automatically, keep pipeline data current without reps touching a field, and surface deal insight from conversation patterns.
Financial reporting agents
Pull actuals from your ERP at period end, generate variance analysis across cost centers, flag anomalies, and produce a first-draft management commentary for review.
Operational monitoring agents
Watch operational metrics continuously, surface anomalies with context before they reach customers, and move data between systems that don't talk to each other.
What it solves
The operational problems custom AI agents address
CRM data that's always stale
Pipeline accuracy riding on reps remembering to log calls and update fields. Decisions made on data nobody trusts.
Manual work at every close
Finance teams rebuilding the same variance analysis and commentary by hand every period instead of reviewing a draft.
Problems found too late
Anomalies surfacing after they've already hit a customer or a number, because no one is watching the metrics in real time.
Systems that don't talk
People copy-pasting between tools that were never connected, moving data by hand from one system to the next.
Skilled people on low-value tasks
Expensive talent stuck on repetitive processing that follows a predictable pattern and needs no human judgment.
Where it applies
Teams that get the most from AI agents
Support teams
AI support agents triage and draft routine responses so your people focus on the cases that genuinely need them.
Revenue teams
Agents keep the CRM current from real conversations and surface the deal signals reps would otherwise miss.
Finance and operations
Autonomous AI agents handle reporting, reconciliation, and monitoring, flagging what needs a human and drafting the rest.
How we deploy it
From discovery to deployed agent
Discover
We map the workflow, every manual step, system, and handoff, and pinpoint where AI creates the most measurable impact, before a line of code is written.
Architect
We design the AI models, data flows, integration points, and the human oversight model. You review and approve the architecture before we build.
Deploy
We build and integrate directly into your tools, with parallel testing against real data before go-live, so there's no disruption to live work.
Measure
We track performance against the baseline from Discovery and refine on real output and feedback until it's running at full effectiveness.
Client engagement
A recent client engagement
Industrial manufacturer · Western Canada · CRM enrichment agent
A 150-person manufacturer's 12-person sales team handled complex enterprise deals, but CRM records were always out of date, updated by hand hours after calls with incomplete notes. Pipeline data was unreliable and forecasting ran on instinct.
Taycon AI deployed a CRM enrichment agent connected to their call recording, email, and calendar. After each interaction it reads the transcript, identifies the updates that matter (stage changes, next steps, stakeholders, objections), and updates the record automatically.
Reps stopped doing CRM admin, the pipeline reflected what actually happened in conversations, and the deployment returned about 45 minutes per rep per day.
The payoff
The operational payoff
- Routine, high-volume work runs without a person on each case.
- Skilled staff move to the judgment calls and exceptions that need them.
- Records and outputs stay current automatically, so data stays trustworthy.
- A human oversight model approves anything customer-facing or high-stakes.
FAQ
Questions about custom AI agents
What's the difference between an AI agent and a chatbot?
A chatbot waits for someone to type a question. An AI agent works on its own inside your systems: it watches for triggers, pulls data from multiple sources, decides, and acts, without a person starting each interaction. A support triage agent handles every incoming ticket automatically, not just the ones someone asks about.
Can AI agents integrate with Salesforce, HubSpot, or Zendesk?
Yes. We build agents that work inside your CRM (Salesforce, HubSpot), helpdesk (Zendesk, Intercom), ERP, data warehouse, and communication tools, so your team keeps the platforms they already use instead of adopting a new one.
Do AI agents replace human staff?
No. Every agent has a defined human oversight model. AI handles classification, drafting, data capture, and routing, and people review and approve anything customer-facing. Skilled staff spend their time on complex cases and judgment calls instead of routine processing.
How accurate are AI agents?
Accuracy is set against a baseline during parallel testing, before the agent touches live workflows. It is validated on your real operational data first, and it improves over the first 30 to 60 days as it learns from corrections and feedback.
How long does it take to build a custom AI agent?
A focused agent typically runs 4 to 8 weeks from discovery to production, including two weeks of parallel testing before go-live. Larger multi-agent deployments take longer and are scoped individually.
Keep exploring
Related work
Ready to put an AI agent to work?
A 20-minute call with the Taycon AI team to find the first workflow worth automating and what a working agent would do for it. No deck, no obligation.
Book a free intro call