Custom AI
Agents.

Agentic AI
We build standalone AI applications that handle specific business tasks autonomously. Not chatbots. Not copilots. Purpose-built software trained on your data, integrated with your systems via MCP, and designed to run without manual intervention.
Built for operations leaders, department heads, and business owners who have workflows that are manual, repetitive, or inconsistent — and teams too valuable to spend time on tasks a machine can handle.

The Problem

Your Team is Doing Work A Machine Should Handle.

Scoring leads by hand. Compiling reports from five different tools. Processing the same documents over and over. These are tasks that follow rules, not judgment. An AI agent automates the entire workflow — consistent output, zero manual input.

01

40%

Of knowledge worker time is spent on repetitive, automatable tasks

02

$12K

Average monthly cost of one full-time employee doing a task an agent can handle

03

3x

Faster output when manual workflows are automated with AI agents

04

0

Human intervention required once an agent is deployed and running

What You Get

Custom AI Agent Development, End to End.

Every agent we build is a standalone application — not a plugin, not a prompt, not a chatbot. It is purpose-built software that performs a specific task autonomously and delivers measurable results.

Task-specific Applications

Each agent is designed around one task and does it well. Lead scoring, document extraction, content drafting, data enrichment — built for precision, not generality.

Trained on Your Data

Every agent learns from your documents, processes, and business rules. It works the way your team works — using your terminology, your criteria, your standards.

Autonomous Execution

Agents operate on schedules or triggers. Set the rules once and the agent runs independently — processing data, generating outputs, and routing results without human input.

Measurable Output

Every agent produces trackable deliverables. Scored leads, processed documents, drafted content, structured reports — you see exactly what it does and how well it performs.

Full Code Ownership

You own the application, the source code, all configurations, and every output. No licensing fees. No vendor lock-in. Standard API costs only.

Production-grade Security

Encrypted API connections, role-based access controls, audit logging, and governance controls. On-premise deployment available for sensitive data that cannot leave your servers.

Model Flexibility

GPT-4o for complex reasoning. Claude for long-document processing. Gemini for multimodal tasks. Llama and DeepSeek for on-premise. We select the right model for your task and budget.

Built-in Observability

Every agent ships with monitoring dashboards that track accuracy rates, processing times, failure points, and cost per task. You know exactly how the agent is performing at all times.

Agents We Build

Real-world Examples of AI Agents in Action.

Every agent starts with a specific business problem. Here are six agents we commonly build — each one eliminates hours of manual work and delivers consistent, measurable output.

Sales Operations

Lead Qualification Agent

Pulls form submissions and CRM data, scores each lead against your qualification criteria, and routes qualified prospects to the right sales rep automatically. No more manual lead review or missed follow-ups.

Saves 10+ hours/week for a 5-person sales team

Business Development

Proposal Writer Agent

Takes project briefs and generates first-draft proposals with scope, timeline, and pricing pulled from your templates and past projects. Produces consistent, professional proposals in minutes instead of days.

Cuts proposal turnaround from 2 days to 2 hours

Team Productivity

Meeting Summary Agent

Processes call transcripts, extracts action items, decisions, and deadlines, then pushes a structured summary to Slack, email, or your project management tool. Every meeting produces a clear record automatically.

Eliminates 30 minutes of note-writing per meeting

Client Operations

Client Onboarding Agent

Triggers when a new deal closes and generates onboarding documents, sends welcome email sequences, creates project tasks in your PM tool, and schedules kickoff calls. Turns a 3-day manual process into a 3-hour automated workflow.

Reduces onboarding time from 3 days to 3 hours

Strategy and Research

Research Brief Creator

Monitors industry news, competitor activity, and market data on a schedule you define. Delivers structured intelligence briefings to your inbox — replacing hours of manual scanning across dozens of sources.

Replaces 5+ hours of weekly manual research

Internal Operations

HR and Policy Agent

Answers employee questions using your internal policies, handbooks, and SOPs. Trained on your documentation and updated automatically when policies change. Handles routine HR queries without human involvement.

Handles 80% of routine HR queries autonomously

What We Solve

If You Are Saying Any of These, You Need an Agent.

Most businesses do not realize a workflow can be automated until someone points it out. If any of these sound familiar, you are describing a problem an AI agent solves.

Lead Scoring and Document Processing

“We spend hours qualifying leads manually.” “Our team copies data from PDFs into spreadsheets every week.” Lead scoring agents pull data from forms, CRM, or email — score each lead against your criteria — and route qualified prospects automatically. Document processing agents extract structured data from PDFs, invoices, contracts, and forms.

  • Automated lead qualification and routing
  • CRM and email data integration
  • Invoice and contract data extraction
  • Structured output to your database
  • Custom scoring criteria and rules
  • Multi-source data normalization

Content Generation and Reporting

“We need someone to compile a weekly report from five different tools.” “Our team writes the same types of emails over and over.” Content agents generate first drafts based on your brand voice and guidelines. Reporting agents pull data from multiple sources, generate formatted reports, and deliver them on schedule.

  • Brand-voice trained content drafting
  • Multi-source report compilation
  • Scheduled and on-demand delivery
  • Template-based formatting
  • Meeting summary and action item extraction
  • Automated proposal generation

Research, Monitoring, and Data Enrichment

“I wish someone could monitor our competitors and give me a weekly summary.” “Our CRM data is incomplete — someone has to research every contact manually.” Research agents monitor industry news, competitor activity, and market trends. Data enrichment agents take partial records and fill gaps using verified sources.

  • Competitor and market monitoring
  • Regulatory change tracking
  • Contact and company data enrichment
  • Structured intelligence briefings
  • Multi-source data validation
  • Scheduled delivery to Slack or email

Technology Stack

Models and Frameworks We Build On.

We select the model based on your task requirements, cost considerations, and data sensitivity. The orchestration layer is chosen for the workflow complexity.

OpenAI Gpt-4o and GPT-4.1

Complex reasoning, structured output, and function calling. The default choice for agents that need to process nuanced language, follow multi-step instructions, and produce precise outputs.

Anthropic Claude

Long-document processing, careful instruction following, and extended context windows. Ideal for agents that work with large knowledge bases, legal documents, or detailed technical content.

Google Gemini 2.0

Multimodal processing — text, images, audio, and video in a single pipeline. The choice for agents that need to analyze visual content, process mixed media, or handle multi-format inputs.

Llama and DeepSeek

Open-source models for on-premise deployments where data cannot leave your infrastructure. Full control over the model, no external API calls, and complete data privacy.

LangGraph and CrewAI

Orchestration frameworks for stateful workflows and multi-agent collaboration. LangGraph manages complex state machines. CrewAI coordinates specialized agents working together on a single pipeline.

Model Context Protocol

The open protocol that connects AI agents to any tool with a single interface. CRMs, databases, APIs, and internal systems integrate through one standardized layer — no custom connectors required.

The Process

How a Custom AI Agent Gets Built.

Every agent project follows a structured process from discovery to deployment. You stay involved at every decision point. We handle the build.

01

Discovery

You describe the task. We identify the best approach — model selection, data sources, integration points, and output format. This takes 2 to 3 days and defines the entire project scope.

02

Architecture

We define the agent’s processing logic, integration layer, deployment model, and monitoring strategy. You approve the architecture and fixed-price quote before we write any code. This takes 3 to 5 days.

03

Build and Training

We develop and refine the agent iteratively. Prompt engineering, data pipeline construction, integration setup, and knowledge base configuration happen in structured sprints with milestone reviews. This is 1 to 3 weeks depending on complexity.

04

Testing and Validation

We run the agent against real scenarios using your data. You validate the output meets your quality standards. Edge cases, error handling, and failure recovery are tested before deployment.

05

Deployment

We deploy to your environment — cloud, on-premise, or hybrid. Full documentation, team walkthrough, and observability dashboards are configured before handover. This takes 2 to 3 days.

06

Post-launch Support

30 days of monitoring, optimization, and adjustments included with every project. Most refinements happen in the first two weeks as the agent handles real production data.

Scope and Complexity

Three Tiers Based on Workflow Complexity.

Most projects start as a single-task agent. As confidence grows and needs expand, agents scale to multi-step workflows or full multi-agent systems. We evaluate your use case and provide a fixed-price quote after discovery.

Tier 1

Single-task Agent

Typically 4 weeks from discovery to deployment

  • One defined workflow or task
  • One or two data source integrations
  • Single model deployment
  • Standard monitoring dashboard
  • 30-day post-launch support

Tier 2

Multi-step Agent

Typically 6 to 8 weeks

  • Multiple data sources and integrations
  • Conditional logic and branching
  • Advanced prompt chains
  • Error handling and fallback routing
  • 30-day post-launch support

Tier 3

Multi-agent System

Typically 8 to 12 weeks

  • Two or more specialized agents
  • Orchestration layer with state management
  • Enterprise-grade compliance controls
  • Advanced observability and audit logging
  • 30-day post-launch support

What We Need

Three Things to Get Started.

To build agents that actually solve your problem, we need a clear picture of the workflows, tools, and outcomes you are targeting.

01

Use Case Brief

A clear description of what the agent should do, who will use it, and what a successful outcome looks like. A one-page overview of the current manual workflow is enough to start discovery.

  • Task description and goals
  • Current manual process overview
  • Expected input and output formats
  • Success criteria and metrics
02

System and Tool Access

Credentials or documentation for the platforms, APIs, and data sources the agent connects with. We use MCP as our standard integration layer, plus REST APIs, webhooks, and direct database connections.

  • API keys and documentation
  • CRM and database access
  • MCP-compatible tool endpoints
  • Authentication requirements
03

Sample Data

Representative examples of the inputs the agent will process and the outputs you expect. Sample data accelerates development and ensures the agent is calibrated to your actual data patterns from day one.

  • Example inputs and documents
  • Expected output formats
  • Edge cases and exceptions
  • Volume and frequency estimates

In Practice

Real Agents. Real Decisions.

AI agents go beyond answering questions — they take action. Here are three that replaced entire manual workflows.

Insurance — Claims Processing Agent

The problem: An insurance brokerage processed 200+ claims per week. Each claim required pulling policy details, cross-referencing coverage limits, extracting information from adjuster reports, and generating status letters. A single claim took an analyst 45 minutes to process.

What we built: Multi-step AI agent that ingested claim submissions, extracted structured data from adjuster reports using document AI, cross-referenced policy databases, validated coverage, and generated templated correspondence. The agent flagged anomalies for human review instead of processing blindly.

The outcome: Average processing time dropped from 45 minutes to 7 minutes. Analysts shifted from data entry to exception handling and client communication. Error rate in coverage validation decreased 82%. The brokerage processed 35% more claims without adding headcount.

Logistics — Route Optimization Agent

The problem: A regional delivery company with 40 trucks planned routes manually every morning. The dispatcher spent 3 hours creating routes based on experience and spreadsheets. Last-minute order changes meant re-planning mid-day. Fuel costs were climbing because routes were not optimized for distance or time windows.

What we built: AI agent that pulled orders from their OMS, optimized routes based on delivery windows, truck capacity, traffic patterns, and driver availability. The agent re-optimized in real-time when orders were added or cancelled. Drivers received updated routes via a mobile app integration.

The outcome: Daily route planning dropped from 3 hours to 12 minutes. Average delivery distance per truck decreased 18%. Fuel costs dropped $4,200 per month. On-time delivery rate improved from 84% to 96%. The dispatcher now manages exceptions instead of building routes.

Recruiting — Candidate Screening Agent

The problem: A staffing agency received 3,000 applications per month across 80 open positions. Recruiters spent 60% of their time on initial resume screening — reading applications, matching qualifications to job requirements, and sending status emails. By the time they reached qualified candidates, top talent had already accepted other offers.

What we built: AI agent that parsed resumes, extracted qualifications, scored candidates against job requirement matrices, and generated shortlists with reasoning. The agent sent personalized status emails to applicants, scheduled phone screens for top candidates, and flagged edge cases for recruiter review.

The outcome: Time-to-first-contact with qualified candidates dropped from 5 days to 6 hours. Recruiters spent 80% of their time on relationship building instead of screening. Placement rate increased 22% because candidates were contacted before competitors reached them.

Build a Custom AI Agent.

Describe the workflow you want to automate. We will evaluate your use case and provide a fixed-price scope estimate within 48 hours — no commitment, no sales pitch.

01 Describe your workflow and automation goals
02 We evaluate and provide a fixed-price scope estimate
03 Discovery begins and your agent is in production within weeks

FAQ.

A custom AI agent is a standalone software application built to handle one specific task or workflow autonomously. Unlike chatbots that wait for user input or copilots that assist alongside humans, an AI agent operates independently — processing data, making decisions based on defined rules, executing actions, and delivering output without human intervention. Each agent is trained on your business data, integrated with your existing platforms via the Model Context Protocol and APIs, and designed for production-grade reliability.

ChatGPT and Claude are general-purpose AI assistants designed for open-ended conversations. A custom AI agent is purpose-built software — trained on your specific data, integrated with your systems via API, and designed to execute one defined task reliably and repeatedly. General-purpose assistants require a human to prompt them every time. A custom agent runs autonomously on triggers and schedules.

If a team member follows the same steps repeatedly, pulls data from one system to enter it into another, compiles information from multiple sources into a report, or makes decisions using a consistent set of criteria — that workflow is a strong candidate for an AI agent. The best starting point is any task that is manual, repetitive, and rule-based. If you are unsure, describe the workflow to us and we will tell you whether an agent is the right solution or if a simpler automation would be more appropriate.

Any platform with an API, database connection, or MCP-compatible interface. Common integrations include CRMs like Salesforce and HubSpot, email platforms, Google Workspace, Slack, project management tools like Asana and Monday.com, document storage systems, spreadsheets, and custom internal applications.

Yes. Most clients start with a single-task agent to validate the approach and measure results before expanding. A single agent can be upgraded to a multi-step agent or become part of a multi-agent system as your needs evolve. Starting small reduces risk and builds internal confidence in AI-powered automation. There is no penalty for starting with a focused scope — in fact, we recommend it.

4 to 8 weeks from discovery to deployment. A single-task agent with one integration typically takes 4 weeks. Multi-step agents with several data sources and conditional logic take 6 to 8 weeks. Multi-agent systems with orchestration layers may extend to 10 to 12 weeks. Timeline depends on workflow complexity, number of integrations, data volume, and testing requirements.

Every project starts with a discovery phase where we evaluate your workflow, data sources, integration requirements, and complexity. Based on this evaluation, we provide a fixed-price quote before any development begins. Pricing depends on the number of integrations, processing logic complexity, and deployment requirements. There are no surprises — you approve the scope and cost before we write any code.

We select the model based on your task requirements, cost considerations, and data sensitivity. Common choices include OpenAI GPT-4o and GPT-4.1 for complex reasoning, Anthropic Claude for long-document processing, Google Gemini 2.0 for multimodal tasks, and open-source models like Llama and DeepSeek for on-premise deployments. For orchestration, we build on LangGraph for stateful workflows, CrewAI for multi-agent collaboration, and the Model Context Protocol for standardized tool integration.

A multi-agent system uses two or more specialized AI agents that collaborate to complete a complex workflow. Instead of one agent handling everything, each agent focuses on a single task — one extracts data, another validates it, a third routes exceptions. An orchestration layer coordinates the handoffs and manages state across the entire pipeline. Multi-agent systems are ideal for workflows that involve multiple data sources, conditional branching, and different types of processing at each stage.

Yes. Every agent is designed for iteration from day one. We monitor performance metrics through built-in observability dashboards after launch, and adjust processing logic, prompts, model selection, and integrations as your needs evolve. Most refinements happen within the first 30 days as the agent handles real production data. We can also swap underlying models as newer, more capable options become available.

Yes. You own the application, the source code, all configurations, and every output the agent produces. We build it, deploy it, document it, and hand over full ownership. There are no licensing fees or usage-based charges for the agent itself — only standard API costs for the underlying AI model provider.

ROI varies by use case, but most businesses see payback within 2 to 4 months. A lead scoring agent that saves a 5-person sales team 10 hours per week delivers significant annual labor savings. A document processing agent handling hundreds of invoices per month can reduce processing costs by 70 to 80 percent. We define ROI metrics during the discovery phase so results are measurable from day one.

Data security is built into every layer. We use encrypted API connections, role-based access controls, and secure cloud infrastructure. Every agent includes audit logging and governance controls so you can track exactly what data the agent accesses, processes, and outputs. For sensitive data, we deploy agents using on-premise models that process data entirely within your environment — nothing leaves your servers. We also support SOC 2 compliant hosting and can sign BAAs for healthcare data.