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AI and Automation

Custom AI Agent Development

Purpose-built AI agents that complete real work inside your operations.

Overview

What we deliver

We design and build custom AI agents that handle research, decisions, and actions across your tools, with guardrails, logging, and human review.

We build custom AI agents that take on the work your team does not have time for. Each agent is scoped to a specific job, connected to the systems it needs, and tested against examples from your real workflows. We start by mapping the task, the inputs, the decision points, and the outputs you expect. From there, we choose the right model, design the prompts and tool calls, and add memory and retrieval where it improves accuracy. We add guardrails, logging, and fallback paths so the agent fails safely and you can audit every run. We deliver the agent with clear documentation, an evaluation harness, and a plan for monitoring and iteration. The result is a focused agent that completes defined work end to end and gives your team time back for higher value tasks.

Fit Check

Built for teams like yours

Who it's for

  • Operations leaders with repeatable knowledge work
  • Founders scaling without adding headcount
  • Product teams adding AI features
  • RevOps and CS teams drowning in tickets
  • Heads of data and analytics

Pain points we solve

  • Manual research and triage taking hours per day
  • Knowledge workers stuck on repetitive tasks
  • Inconsistent quality across team outputs
  • Slow turnaround on customer requests
  • Tools that do not talk to each other
What's included

Capabilities

Everything we cover in this engagement.

  • Agent scoping and task decomposition
  • Prompt and tool design
  • Retrieval and memory setup
  • Function and API tool integration
  • Guardrails and policy controls
  • Evaluation harness and test suites
  • Logging, tracing, and observability
  • Deployment and handoff documentation
How we work

Our process

A clear, predictable path from kickoff to outcomes.

01

Discover

Map the task, inputs, outputs, and success criteria with stakeholders.

02

Design

Define agent scope, model choice, tools, and guardrails.

03

Build

Implement the agent, tools, retrieval, and evaluation harness.

04

Test

Run evaluations on real cases and tune prompts and tools.

05

Deploy

Ship to production with monitoring, logging, and a review cadence.

What you get

Deliverables & outcomes

What you get

  • Working AI agent in your environment
  • Source code and prompt library
  • Tool and API integration layer
  • Evaluation dataset and test reports
  • Monitoring and logging dashboard
  • Operating playbook and handoff docs

Outcomes you can expect

  • Hours of manual work removed each week
  • Consistent quality across runs
  • Faster response on customer and internal requests
  • Clear audit trail for every agent action
  • A foundation to add more agents over time
Timeline

4 to 8 weeks

Engagement

Monthly retainer, Project, Sprint

Tools we use

OpenAI, Anthropic, LangChain, LangGraph, Pinecone

KPIs we track

Task completion rate, accuracy on golden set, average handle time saved, error rate, cost per task

Client stories

What clients say

"

We had 14 cornerstone pages stuck on page two for 18 months. Their SEO crew rewrote the internal linking, cleaned up our schema, and shipped 22 supporting briefs over a quarter. Eight of those pages broke top three by month five. Organic pipeline went from a trickle to our second-largest source. Felt like watching interest compound.

James T.
"

We had been prototyping an AI quoting agent for nine months and could not get it past demo quality. They came in, scoped a real eval set, swapped our retrieval layer, and added guardrails for the edge cases that kept burning us. Went live in seven weeks. It now handles 41 percent of inbound quote requests without a human touching them.

Kyle A.
FAQ

Frequently asked questions

Quick answers to the questions we hear most.

How is a custom agent different from a chatbot?
A chatbot answers questions. A custom agent completes defined work by calling tools, making decisions, and producing outputs you can use.
Which models do you use?
We pick the model that fits the task, cost, and latency needs. We work with frontier models from OpenAI, Anthropic, and others, plus open models when they fit.
Can the agent run on our infrastructure?
Yes. We can deploy in your cloud account or use managed services. We follow your data policies and security requirements.
How do you prevent the agent from making mistakes?
We use scoped tools, guardrails, evaluations on real cases, and human review steps for high stakes actions. Every run is logged.
What happens after launch?
We provide monitoring dashboards and a review cadence. Many clients keep us on a retainer to add features and improve performance.

Ready to put an AI agent on a real job?

We scope the work, build the agent, and ship it with the guardrails your team needs.