01. AI workflow automation

AI workflow automation that survives Monday morning.

We build production AI workflows for mid-market operations teams. Not templates. Not demos. Workflows your team uses daily, with the integrations, guardrails, and observability to keep them running.

Schedule a call →Start a diagnosticReplies within 1 business day
02. The problem

Connectors are not the judgment layer.

Most workflow automation tools, Zapier, n8n, Power Automate, handle the connectors but stop at the judgment layer. Tools that route between systems aren't the same as tools that make decisions inside operations.

Real operational automation needs a model that reasons, a substrate that integrates with your existing stack, and the engineering discipline to run it in production. The connectors are the easy 20%.

We build the other 80%. Eval suites in CI, HITL gates on destructive paths, audit logs you can search in under a minute, and integrations to the platforms your team already runs on, SAP, Salesforce, NetSuite, Procore, ServiceNow.

03. What we do

Map. Build. Integrate. Operate.

01

Map

We map every workflow in scope. Score each one for AI fit. Sequence the bets.

02

Build

Production workflows shipping by sprint one. Real data from day five.

03

Integrate

Into SAP, Salesforce, NetSuite, Procore, ServiceNow, and whatever else your team already runs on.

04

Operate

HITL gates on destructive paths. Eval suites in CI. Audit logs you can search in under a minute.

04. Why us

Sixteen years of production operations software.

15+
YEARS OPERATING
Phoenix & Los Angeles studio. Software for operations since 2010.
200+
ORGANIZATIONS
Enterprise teams running platforms we built, day after day. DocuPaint alone.
4yr+
AVG TENURE IN PRODUCTION
How long our platforms stay in active use after launch. The actual business.
They automated our entire dispatch. Load inputting, in-transit, carrier payout. The kind of quiet, durable softwarewe'd been looking for for years.
Ron Bagramyan, Founder, LoadQuest.net
05. Case study

LoadQuest. National freight, automated.

3×
DISPATCH THROUGHPUT, NO ADDED HEADCOUNT
FREIGHT · DISPATCH AUTOMATION · 2020–2024

LoadQuest

Multi-year build for a national freight brokerage. Dispatchers running load matching, in-transit tracking, and carrier payouts. All automated. The system ingested load requests across email, EDI, and broker portals, scored each one against carrier capacity and route economics, and routed to the right desk with enough context to take a decision in under a minute.

The AI layer made the decisions a human dispatcher used to make at 6 a.m. on a stack of paper, route fit, rate viability, carrier history. The judgment work. Dispatchers moved from data entry to exception handling. Throughput tripled without adding headcount.

Read the LoadQuest case study →
06. How we engage

Diagnose. Build. Embed.

01 · Diagnose

Map your workflows. Find where AI pays.

Six weeks. We map every workflow in scope, score each one for AI fit, and hand you a sequenced 90-day build plan you can fund.

Start a diagnostic →
02 · Build

Ship workflows in production from week one.

Twelve-week sprints. Real users on real data by day five. Public Jira board, weekly demo, integrations live before launch.

Scope a build →
03 · Embed

Long-term operation as your AI engineering function.

Quarterly partnership. Our team works inside yours. AI engineers on tap to evolve, monitor, and govern every workflow we shipped.

Talk about embedding →
07. FAQ

Common questions, straight answers.

How is this different from Zapier or n8n?

Zapier and n8n are excellent for connecting systems. They don't handle the judgment-layer work, the parts where a model needs to read context, make decisions, and pass structured output to the next step. We build the layer around them. We deploy on n8n where it fits; we go beyond it where it doesn't.

What if we already have a workflow automation platform?

Better. We build on what you have. We integrate Claude or another model layer into your existing platform, add the governance and eval substrate, and ship workflows in production without forcing a tool migration.

How long until we see something in production?

A Build sprint ships software in production by week one, with real users on real data by day five. Diagnostic engagements ship a written plan in six weeks.

Do you work with companies that don't have engineering teams?

That's most of our clients. Mid-market operations teams typically don't have AI engineers in-house. We operate as yours.

What if our operations are too messy for AI to touch?

That's the Diagnostic engagement. Six weeks to map what you have, score what's AI-fit, and tell you the truth about what's ready and what isn't. More on the diagnostic →

What industries do you focus on?

Logistics and freight, construction and general contracting, industrial services, field services, and property management. Mid-market companies with $50M to $500M revenue where operations leaders own the AI agenda.

08. Begin
Replies within 1 business day

Workflows that survive past month three.

Schedule a call →Start a diagnostic+1.602.815.5600 · hello@sytepoint.com