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AI Agent Agency That Works Around the Clock
An AI agent is not a chatbot that regurgitates your FAQ page. Tkist Digital AI agent development services build production-grade agents that read context, make decisions within a defined boundary, take actions across multiple systems, and hand off to a human at exactly the right moment, our agency has built this in production, not in a demo environment.
0/7
Agent availability
0 min
Avg lead qualification time down from 4 hours
0%
Avg support tier-one deflection rate
The Challenge
The gulf between what chatbots promise and what a real AI agent delivers
Most chat widgets are still static scripts running on yes/no decision trees. They frustrate visitors, break on anything outside the script, and convert almost nobody. A properly built AI agent actually understands your product, knows your pricing and common objections, qualifies leads against your ICP, books discovery calls, and only hands off to a human at exactly the right moment. The gap between a scripted bot and a trained agent isn't a small percentage, it's a multiple.
We train agents directly on your product documentation, pricing, past sales conversations, and competitor positioning. What comes out the other end responds like your best rep at 3am on a Sunday, across every channel simultaneously.
24/7
Agent availability
Package Contents
Everything bundled into your ai agents & gpt builds engagement
View Full ScopeLead Qualification Agents
Agents that read, score, and route inbound leads via email or chat without human review.
Customer Support Agents
First-line support agents trained on your knowledge base, escalating to humans outside scope.
Internal Knowledge Agents
Ask questions against your documentation, SOPs, and data via a natural language interface.
Multi-Step Workflow Agents
Agents with tool use, CRM updates, calendar booking, email send, Slack notifications.
RAG Pipelines
Retrieval-Augmented Generation pipelines for grounded, document-cited responses.
Monitoring & Audit
Every conversation logged. Decision points auditable. Anomaly alerts included.
Our Process
From the first conversation to results you can measure
Use Case Scoping
We define the agent's exact scope, decision boundaries, tool access, and escalation rules before any build begins.
Prompt & Memory Design
We engineer the system prompt, design the memory layer, and define the retrieval strategy for knowledge-base agents.
Build & Red Team
We build the agent, then red-team it, testing edge cases, adversarial inputs, and out-of-scope requests to find failure modes before deployment.
Deploy & Monitor
We deploy to your environment with a monitoring dashboard showing every conversation, decision point, and tool call. Full handover and training included.
Agent Types We Build
Four categories of AI agent we build for real production use
Every agent gets scoped, tested, and monitored before it goes live. These are the deployments that reliably produce results you can put a number on.
Lead Qualification Agent
Watches every inbound channel, web forms, email, live chat, and evaluates leads against your ICP the moment they arrive. Scores, routes, and responds within seconds around the clock, with no human needed for tier-1 triage.
Real-world example
A B2B software company fielding 80+ weekly enquiries had their Tkist Digital agent pre-qualify each one, book ICP-matched leads straight into the sales calendar, and route mismatches into a nurture sequence, all without rep involvement.
94% of leads pre-qualified. Response time: 47 seconds average. Reps only touch qualified leads.
Customer Support Agent
Trained on your product docs, pricing, onboarding guides, and top 200 support tickets. Resolves tier-1 questions instantly. When something falls outside its knowledge, it escalates with a full transcript and a recommended next step, never a blank handoff.
Real-world example
A SaaS company with 3,000 users cut first-response time from 4 hours to 22 seconds. 73% of tickets resolved with no human involved. CSAT climbed from 3.8 to 4.9.
73% self-resolved. 22-second first response. CSAT from 3.8 to 4.9.
Internal Knowledge Agent
Wired into your Notion, Confluence, SharePoint, Slack history, and internal wikis. Employees ask questions in plain language and get precise, sourced answers from your actual knowledge base instead of hunting for 25 minutes or guessing.
Real-world example
A 200-person professional services firm deployed an internal agent across HR policy, the proposal library, and project methodology docs. New-hire questions got resolved instantly, and senior consultant time on internal queries dropped 60%.
60% reduction in internal queries to senior staff. Answers sourced from 47,000 internal documents.
Workflow Orchestration Agent
Watches for external events, emails, CRM updates, calendar events, payment notifications, and kicks off multi-step processes automatically. Not a single API call, but a multi-tool agent that reasons through what needs to happen next.
Real-world example
A logistics company runs an orchestration agent that catches shipment exception emails, checks them against customer SLAs, drafts and sends proactive notifications, opens internal escalation tasks, and updates the CRM, without a human ever seeing the original email.
14 hours per week returned per operations manager. Zero missed SLA notification triggers.
By The Numbers
Five things separating a production-ready AI agent from a demo that falls apart in the real world
73%
of tier-1 enquiries resolved by a trained AI agent, versus 18% for scripted chatbots
The difference between a scripted bot and a trained agent is structural, not incremental. A scripted bot can only handle the questions it was explicitly built for. An AI agent reads intent, pulls context, checks your knowledge base, asks clarifying questions, and handles the variations no script writer ever anticipated. The resolution gap is measured in multiples.
21×
more likely to qualify a lead when the first reply lands within 5 minutes
An AI agent doesn't sleep, doesn't get stuck in back-to-back calls, and never lets a form sit unread until Monday. Every inbound lead gets a qualified response within seconds. Improving speed-to-lead is one of the most reliable, measurable levers available to any sales team.
90%
lower cost per interaction for AI agents versus equivalent human support at scale
A trained agent handling 500 tier-1 enquiries a day costs a fraction of the equivalent headcount, with no training time, no sick days, no knowledge gaps between shifts, and consistent quality every time. This isn't a marginal saving, it's a structural shift in the cost model.
34%
higher CSAT scores where the agent has proper escalation logic built in
Bad AI experiences usually come from agents fumbling questions outside their scope instead of handing off cleanly. Escalation logic isn't optional, it's what determines whether the agent builds trust or destroys it. We design the human handoff with as much care as the agent's core capability.
3 min
average lead qualification time with our agents, against a 4-hour industry norm
Our qualification agents read inbound data, check it against your ICP, ask follow-up questions, score the lead, and route it to the right person in under three minutes, any time of day. The commercial upside is simple: you respond to high-value leads before competitors even know they exist.
A Real Outcome
Software / Technology, Enterprise Sales
AI agent pre-qualifies 94% of inbound leads. Cost per qualified lead cut by 88%.
Leads pre-qualified by AI
Before
0%
After
94%
Cost per qualified lead
Before
$180
After
$22
Out-of-hours response time
Before
Next day
After
Instant
We turned the agent on a Thursday evening. By Monday it had pre-qualified 34 leads and booked 8 demo calls. Our old chatbot hadn't booked a single call in six months.
Ravi S.
Head of Growth
Mid-Market B2B SaaS Company
Want to see numbers like these on your own project?
Browse case studiesThe Comparison
What you actually get with Tkist Digital versus the alternatives
| Feature | Tkist Digital | Basic Chatbot (Drift / Intercom) | In-house IT Build |
|---|---|---|---|
| Trained on your actual product data | ✓ | ✗ script-based | varies |
| Multi-system tool use (CRM, calendar) | ✓ | ✗ | varies |
| Escalation logic engineered in | ✓ | basic | varies |
| Red-team tested before deployment | ✓ | ✗ | rarely |
| Monitoring dashboard included | ✓ | basic | extra build |
| Privacy / on-prem model option | ✓ | ✗ | possible |
| Lead qualification built in | ✓ | ✗ | extra build |
| Ongoing iteration and tuning | ✓ | self-managed | extra resource |
Our Toolkit
What's in the stack behind your ai agents & gpt builds project
Industries We Work With
AI Agents & GPT Builds tailored to the sectors we know best
Recent Work
Recent ai agents & gpt builds projects
Frequently Asked
Questions people ask us about ai agents & gpt builds
What is the difference between an AI chatbot and an AI agent?
What AI models do you use?
How do you prevent the agent from saying incorrect things?
Can the agent connect to our CRM, calendar, or other software?
How long does it take to build an AI agent?
What data do you need to train the agent?
Content last reviewed: July 2026
Zero obligation
Book a free AI agent scoping call
Tell us which part of your sales or support operation you want to automate. We define the agent's exact scope, tool access, and expected deflection rate, and give you a fixed build cost before you commit to anything.