How We Build It
From Discovery to Live
Most custom AI agents go from discovery to production in two to four weeks. Here is what each phase looks like.
Your Client
Has a Person Doing What Software Should
That person is not a system. They are a bottleneck pretending to be a system. They know how to score leads because they have done it 10,000 times. They know which documents matter because they remember. They know what falls through the cracks because they are the one patching the cracks every Tuesday at 4 PM.
Custom AI agents replace that dependency. Each agent handles one job completely — trained on the same decisions that person makes, running continuously, scaling without headcount. Your client’s team gets the hours back. Your agency gets a recurring revenue stream that grows with every deployment.
What a Custom
AI Agent Actually Is
A custom AI agent is a purpose-built software system that performs a specific business task autonomously — without waiting for someone to ask it a question. Unlike chatbots that respond when prompted, agents watch for work and complete it on their own.
The distinction matters because the two solve fundamentally different problems:
Chatbot
- Reactive — waits for input
- Answers questions from a knowledge base
- General-purpose
- Customer-facing
AI Agent
- Proactive — monitors for triggers
- Makes decisions based on real data patterns
- Purpose-built for one job
- Operates behind the scenes
We build agents that replace the person everyone depends on — the one who knows every workaround, patches every crack, and is always one vacation day away from disaster.
Why
Most AI Agent Projects Fail
Before you sell AI agents to your clients, understand why most builds never make it to production. Knowing these pitfalls is the difference between a revenue stream and a write-off.
Scope Creep
Started as a lead scorer, ended as “can it also write emails and manage our CRM?” Agents do one job. When you try to make them do five, they do none well. We scope ruthlessly in discovery so the agent ships and delivers results.
No Real Data
Training on hypothetical scenarios produces hypothetical results. Agents need actual historical decisions, actual documents, actual patterns. We audit your client’s data in the first three days. If the data is insufficient, we say so before a dollar is wasted.
No Error Handling
What happens when the agent encounters something it has never seen before? Most builds skip this entirely. We make error handling the foundation — confidence scoring, human escalation paths, and fallback rules are built in from day one.
Wrong Problem
Some problems need a human. Some need a Zapier workflow. Some need an agent. Picking the wrong tool is the most expensive mistake. We help you identify which problems actually warrant an agent and which ones have simpler solutions.
Five Agent
Types We Build
Each agent type solves a specific category of business problem. Here is what they do, what they replace, and the real-world impact we have seen.
AI Lead Qualification
What it does: Evaluates every inbound lead in seconds using historical conversion data.
Real-world impact: Companies using AI lead scoring report 25–30% increases in sales productivity and 15–40% improvements in conversion rates.
Example: A services firm scoring 200 leads weekly reduced evaluation time from 15 minutes per lead to 3 seconds, allowing senior reps to focus only on qualified opportunities.
Automated Document Intelligence
What it does: Reads contracts, forms, and applications, extracts key data, and updates systems automatically.
Real-world impact: AI document processing reduces end-to-end processing time by over 80% while lowering error rates from 10% to under 2%.
Example: An insurance brokerage processing 500 applications monthly reduced turnaround time from 48 hours to 4 hours through automated extraction and system updates.
Scaled Content Creation
What it does: Generates on-brand marketing and business content trained on company voice and guidelines.
Real-world impact: Agencies replace thousands in monthly freelance costs while producing 10–15 publish-ready pieces weekly.
Example: A real estate marketing firm trained an agent on 200 listings to generate 40 property descriptions weekly matching brand tone and quality.
Automated Business Reporting
What it does: Pulls data from multiple systems, compiles reports, and delivers them automatically on schedule.
Real-world impact: Eliminates manual reporting effort and ensures leadership receives accurate insights on time.
Example: A logistics company eliminated a weekly 4-hour reporting task by automating data collection and delivering finalized reports every Friday morning.
Market Intelligence Automation
What it does: Monitors competitor activity, websites, job boards, and news sources while summarizing key updates.
Real-world impact: Teams identify market shifts early without hiring dedicated analysts.
Example: A SaaS company tracks competitor pricing pages and hiring signals daily, receiving summarized Slack digests highlighting meaningful changes.
The Technology
Behind It
We are model-agnostic. The right model depends on the task, not on marketing.
Natural Language Tasks
GPT-4, Claude, or open-source models depending on client data sensitivity requirements. Content, email, summarization.
Document Processing
OCR pipelines combined with LLM extraction. Reads scanned documents, handwritten forms, structured and unstructured data.
Lead Scoring
Traditional ML models (gradient boosting, random forests) combined with LLM reasoning where judgment matters.
Integrations
REST APIs, webhooks, direct database connections. Deploy on client infrastructure, cloud (AWS, GCP), or managed hosting.
Data stays where the client wants it. On-premise, single-tenant cloud, or encrypted pipelines. We never train foundation models on client data.
Agent vs. Chatbot vs.
Zapier —
When to Use What
Your clients do not need all three. They need the right tool for each problem. Here is how to guide them.
Use a Chatbot When
- You need to answer customer questions from a knowledge base.
- The interaction is reactive — the customer initiates.
- FAQ, product info, basic support triage.
- answers already exist somewhere and just need to be surfaced.
Use Zapier or Make When
- You need to move data between tools based on simple triggers.
- If X happens, do Y — linear, rule-based logic.
- No judgment required — just data transfer and formatting.
- The workflow is predictable with no exceptions.
Use a Custom AI Agent When
- The task requires judgment — not just rules.
- Scoring a lead based on 500 past conversions, not just form completion.
- Processing a document means knowing which fields matter and handling edge cases.
- The 10% exceptions are where the real cost hides.
The honest answer: Most businesses need all three. The expensive mistake is using the wrong tool for the job.
Days 1–3
Discovery
We document what actually happens. Not the process your client wishes they had. The real one. Every workaround, every tool, every decision rule. We identify exactly where an agent earns its place and where it does not.
Days 4–7
Architecture and Data
We design the agent. What data feeds in. What systems it connects to. How it handles edge cases. What we test. You get a project plan, a data requirements list, and a timeline you can lock in. If data is insufficient, we define the fallback rules.
Weeks 2–3
Build and Test
Development happens in parallel with testing. Weekly updates. No surprises because we show working code, not decks. Your client runs real data through the agent. We tune accuracy until it handles their actual edge cases reliably.
Week 4+
Deploy and Support
Agent goes live. Integrated into their systems. Running on schedule. First 30 days of support included. After that, ongoing monitoring and improvements on a monthly retainer. The agent needs a caretaker. That is us.
Three Real
Examples
These are agents we have built. Real businesses. Real problems. Measurable results.
Professional Services Firm
Meeting Follow-Up Agent
Problem: 40+ meetings weekly. 15–20 minutes per meeting writing summaries and updating Asana. Tasks got missed. PMs were always behind.
Solution: Agent listens via Fireflies.ai, extracts summary, pulls action items, and updates Asana automatically.
Result: 20 minutes became 2 minutes per meeting. 12 hours saved weekly. PM tool now current instead of three days behind reality.
Marketing Agency
Lead Qualification Agent
Problem: Sales team reviewing 300 inbound leads per month manually. Each took 10–15 minutes of research. Good leads buried under bad fits.
Solution: Agent scores leads based on company size, role, industry, website traffic, and behavioral signals. Routes qualified leads to sales with context. Sends nurture sequence to everyone else.
Result: Sales team focuses on top 20% of leads. Qualification time dropped from 75 hours per month to zero manual effort. Conversion rate improved by 35%.
Accounting Firm
Contract Data Extraction Agent
Problem: Staff extracting key terms from 80–100 client contracts per quarter. Each contract took 30–45 minutes of manual review.
Solution: Agent reads each contract, extracts 18 key fields (effective dates, renewal terms, payment schedules, liability caps), and populates a structured database.
Result: 45 minutes became 90 seconds per contract. Quarterly savings of 60+ hours. Error rate dropped from 8% to under 1%.
What You Can
Charge Your Clients
Your pricing is yours to set. We give you our cost and a scope document. You mark it up. Agencies typically see 60–80% margins on AI agent projects.
For context: custom AI development from enterprise vendors runs $100,000 to $500,000. We deliver production-grade agents at a fraction of that because we focus on solving one problem completely.
Single-Purpose Agent
$5,000 – $15,000
Lead scoring, document processing, content generation.
Multi-System Agent
$15,000 – $35,000
Pulls from CRM, updates PM tool, sends notifications, branching logic.
Ongoing Monitoring & Improvement
$500 – $2,000/month
Performance tuning, accuracy adjustments, integration updates.
The White-Label
Model
Your client thinks you have a dedicated AI team. You do. We just do not sit in your office.
Path A
Project-Based
Build agents for your clients. You sell the project. We build it. They pay you. We disappear. One-time engagement with clear deliverables and timelines.
Path B
Recurring Revenue
Sell AI agent automation as an ongoing service. Monthly retainer. Recurring revenue. We handle delivery, monitoring, and improvements behind the scenes.
You set the price. You own the client. You control the scope and timeline. We operate as your backend delivery team — on your tools, in your project management system, following your communication preferences.
What Happens
After Deployment
An AI agent is not software you install and forget. Business rules change. Data patterns shift. New edge cases appear. The agent needs someone watching it. That is us. You bill your client for the retainer. We deliver the work.
1
First 30 Days — Included
Bug fixes, adjustments, questions — all covered. We monitor performance, track accuracy, and tune the agent based on real production data.
2
Monthly Retainer ($500–$2,000/month)
Performance monitoring, accuracy tuning as data patterns change, integration updates when connected systems change, edge case handling as new scenarios emerge, and quarterly performance reviews.
3
Why This Matters
The agent that works perfectly in month one will drift without oversight. Data changes. APIs update. Business rules evolve. Ongoing support is not optional — it is what separates a working agent from an expensive experiment.
Industries Where Agents
Deliver the Highest ROI
AI agents work best where there is high volume, repeatable knowledge work. These four verticals consistently produce the strongest results.
1
Professional Services
Lead qualification, proposal generation, meeting follow-ups, time tracking reconciliation. High volume of repeatable knowledge work where agents eliminate hours of manual effort per week.
2
Financial Services and Insurance
Document processing, compliance checks, claims triage, client reporting. AI document processing cuts processing time by 80% with error rates dropping below 2%.
3
Marketing and Advertising Agencies
Content at scale, campaign reporting, competitive monitoring, client onboarding automation. Replace freelance budgets with consistent, on-brand output that scales without headcount.
4
Healthcare and Legal
Patient intake processing, contract review, appointment scheduling, regulatory document extraction. High accuracy requirements where agents outperform manual processes consistently.
FAQ
What exactly is a custom AI agent vs. a chatbot?
A chatbot waits for someone to ask a question and responds from a knowledge base. An AI agent watches for work and processes it automatically. A chatbot finds your invoice when you ask. An agent generates invoices before customers need to ask. One is reactive. One is proactive. Different tools for different problems.
How is this different from building with Zapier or Make?
Zapier connects tools and moves data based on simple rules. When X happens, do Y. An AI agent evaluates context, applies judgment, and decides what to do based on patterns in your data. Sometimes you need both. We help you determine which tool fits which problem.
What tasks can AI agents actually handle?
If someone on your client’s team does it the same way every time, it can likely be automated with an agent. Lead scoring, document review, report generation, content creation within brand guidelines, data extraction, meeting follow-ups. The edge cases that happen 10% of the time usually still need a human. But automating the 90% removes enormous friction.
How long does it take to build a custom AI agent?
Two to four weeks depending on complexity. A single-purpose lead scoring agent takes about two weeks. A multi-system agent with branching logic and multiple integrations takes three to four weeks. This is faster than most expect because we start with your real data and real decision rules, not theoretical requirements.
What does it cost?
Single-purpose agents range from $5,000 to $15,000. Multi-system agents with complex integrations range from $15,000 to $35,000. Ongoing monitoring runs $500 to $2,000 per month. Enterprise AI development firms charge $100,000 to $500,000. We focus on solving one problem completely, which keeps costs practical.
Does it connect to our existing tools?
Usually yes. Salesforce, HubSpot, Slack, Asana, Monday, Zapier, Gmail, Stripe, QuickBooks — if it has an API, we can connect it. Legacy software without APIs is harder but usually still possible through workarounds like email parsing or file monitoring. We scope integration feasibility during discovery.
What LLMs and AI models do you use?
We are model-agnostic. GPT-4 and Claude for natural language tasks. Traditional ML models for structured scoring. OCR pipelines for document processing. The right model depends on the task, the data sensitivity, and the accuracy requirements. We do not pick a model because it is trending. We pick it because it solves the problem.
What happens if the agent makes a mistake?
Every agent we build includes error handling, confidence scoring, and human escalation paths. When the agent encounters something outside its training, it flags it for human review instead of guessing. We set confidence thresholds during testing. Below the threshold, a human reviews. Above it, the agent proceeds. You control where that line sits.
You Already Know
Which Client Needs an Agent First.
The one where someone’s calendar fills up because they’re processing leads manually. Where documents stack up waiting for data entry. Where reports get built on Thursday night for Friday morning and they’re always slightly wrong. The ops manager sighs a lot. It’s fixable.
Tell us about that client. Walk us through what happens now. The process. The tools. The steps that make people sigh. Don’t polish it. Messy is better than polished. Tell us how long it takes and what breaks regularly.
We’ll scope it. Build it. If it makes sense for your business we’ll move forward. If it doesn’t, we’ll tell you that too. That’s how partnerships actually last.
Your client thinks they hired you. You own the credit. You own the revenue. You own the relationship. We disappear into the delivery.
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