AI agents that do real work

Automate the work your team shouldn’t be doing manually

We design and build AI workflows that move work across your tools—reliably, with guardrails. Start with one high-ROI workflow, prove it end-to-end, then scale into a broader automation program.

Discuss your workflows See agent series

20+ hrs/wk

Typical time reclaimed

2–4 weeks

From kickoff to live workflow

Human oversight

Approvals, thresholds, auditability

Example agent workflow

This is how “agentic automation” actually runs.

STEP 1

Trigger

Event or schedule kicks off the workflow

STEP 2

Context

Fetch the right records, docs, and history

STEP 3

AI step

Extract, classify, draft, or decide with guardrails

STEP 4

Action

Update systems + notify the team

What we automate

The best candidates are repetitive workflows that touch multiple systems. We’ll map the process, pressure-test ROI, and design safe automation with fallbacks.

Sales

Meeting notes to CRM updates
Follow-up reminders
Deal stage tasks

Lead generation

Email & form intake
Lead enrichment
Qualification summaries

Marketing

Content briefs
Feedback summaries
Performance reports

Internal processes

Standup digests
Knowledge base Q&A
Work summaries

Administration

Folder organization
Invoice extraction
Audit-ready logs

Recruitment

Resume screening
Interview summaries
Candidate shortlists

ROI in weeks, not quarters

We start with the workflow, not a tool. You get an automation you can run, measure, and extend.

STEP 1

Discovery

Map the workflow, identify failure modes, define success metrics.

STEP 2

Design

Integrations, guardrails, approvals, and “what happens when…” scenarios.

STEP 3

Build

Implement the workflow end-to-end with logging, retries, and evaluation.

STEP 4

Launch + iterate

Go live, monitor, tighten edge cases, then expand to the next workflow.

Agent series we’ve shipped

Published examples. We can adapt any of these to your systems, approvals, and constraints.

PR Review Agent

AI-powered GitHub pull request reviewer that analyzes diffs, retrieves relevant code context (RAG), and posts production-grade feedback directly on PRs.

GitHub

Browse agent series

Built for production, not just demos

The difference between a cool prototype and a workflow you can rely on is engineering discipline: evaluation, observability, safe fallbacks, and human-in-the-loop controls.

Technical deep dive

Evaluation & testing

Benchmarks on real examples, regression tests, and measurable quality.

Guardrails

Approvals, thresholds, validation rules, and safe fallbacks.

Grounded context (RAG)

Pull the right documents/data so outputs match reality.

Observability

Logs, traces, retries, and monitoring to keep workflows stable.