We build AI that handles repetitive work, finds opportunities in your data and helps your team move faster — using the tools and workflows you already have.
Every card below is a real production deployment, measured on that operation's own before-and-after data — a sample of the live systems we build and run.
Automate repetitive work from end to end.
The deployment featured in the Spotlight below: an agent reads inbound quote emails, prices each request from the client's own rate tables — never an AI guess — and replies in minutes, around the clock.
AI analyzed the desk's own throughput data, then automated the busywork — auto-closing self-service requests, routing spam, drafting the most common replies. Same team of four.
Find revenue hiding in your existing data.
AI analysis of payment-processor data found the top failure cause in minutes — work that took days by hand — and tuned the retry logic to recover revenue that was silently walking away.
Live recurring revenue, churn and cohort retention, built straight from live billing data — a job scoped at three to four developer-weeks, delivered in about 12 hours with ~75 passing tests.
Turn messy data into usable intelligence.
Every new data-provider integration took a developer one to two days, and the queue was months deep. An AI integration agent cleared the entire backlog in a single day.
A data-analysis capability that would have taken months with a dedicated specialist, delivered in two weeks — and now a reusable asset that compounds across every later project.
Build and modernize products faster.
A natural-language agent inside a national home-search platform helps buyers see what they can truly afford on live listings — and became the foundation for enterprise contracts and a per-market revenue model.
An AI design-to-code workflow — design files straight to working components — rebuilt eight customer-facing products, cut development time 90%, saved ~$70K in outside costs, and lifted usage 30%.
A company-wide agent that investigates bugs for non-technical staff, answers data questions in minutes that used to wait on a developer, and opens correctly prioritized tickets itself.
A sample of production deployments. Company identities withheld; metrics are each system's own measured before-and-after operating data. Every one of these expanded what the team could do — none was designed to cut jobs.
A Digital Worker works like an AI teammate: it handles repetitive requests from start to finish, using the tools and rules your team already relies on.
Quotes, intake, orders, updates and follow-ups — read, processed and answered end to end, 24/7.
Email, help desk, ERP and CRM — no rip-and-replace, nothing new for your customers to learn.
Routine work gets done automatically. Exceptions go to your team with full context already attached.
Language models are remarkable at reading and writing — and unreliable at exact numbers. So we split every job along that line.
If a process is repetitive, rules-driven and lives in email, tickets, spreadsheets or forms, it's a candidate for a Digital Worker. We learned the discipline in mortgage finance — where a wrong number is a liability — and it transfers everywhere.
Bring us the process your team dreads — we'll tell you honestly whether AI fits it, and what it's worth if it does.
A freight company was manually pricing more than 20,000 email quotes a year. We built a Digital Worker that reads each request, prices it from the company's own rate tables — never an AI guess — and responds automatically. Everything below was measured in the client's own operating data.
Routine quotes answered in minutes, around the clock — including the 21% of demand that used to wait overnight and through weekends.
Roughly the annual workload of an additional full-time hire — about $130K/yr of measured labor value — added back to the team without recruiting, hiring or training anyone.
Every automated quote is priced from the same deterministic rules — the same shipment gets the same price, every time, on every shift.
The team's senior quoters now spend their time on complex, custom shipments — work carrying roughly three times the value of a routine quote — instead of retyping the routine ones.
The goal of this system was never to eliminate employees. It was to make them more productive: the Digital Worker absorbs the routine volume, and the people absorb the growth — new accounts, complex quotes and new revenue — without the cost of hiring extra staff to keep up with the inbox.
Metrics measured in the engagement's initial data analysis of the client's historical email threads, quoting records and payroll figures. Client identity withheld. Results vary by process, volume and data.
Most tools focus on getting an AI agent to run. Our proprietary platform focuses on making it accountable: every agent we deploy is operated, governed and measured from one control layer — so you can audit it, budget it, pause it, and step in wherever it matters.
What the agent did, what it cost, where it hit trouble and what it decided — watch it live, and replay any past run for audits or troubleshooting.
Spend is tracked for every agent and every run, with budgets and hard limits — an agent can never quietly run up a bill.
A person can review, approve or correct the agent's work before it goes out — and you decide exactly where that checkpoint sits.
Long jobs pause and resume without tying up your systems. Failed steps retry on their own — and ask a human for help when they shouldn't.
Each agent's credentials are isolated — it can only reach the systems it was given. Even a manipulated agent can't take a sensitive action without approval, and every attempt is logged.
A plugin architecture connects the systems you already use, and nothing is locked to one AI provider — swap or mix models as pricing and quality change.
We analyze your workflow and your real data — mailboxes, tickets, systems — and quantify the time, cost and value before any build.
We build around your existing systems, then replay your own historical work through the agent until it clears the accuracy bar you set.
Your team reviews the work first. Autonomy grows only when the results prove it should — and you can always step in.
Grounded in your own payroll and volume figures, with the value dashboard as the single source of record. When the Digital Worker doesn't deliver, we don't get paid.
In 30 minutes, we'll tell you whether it's a good fit for AI — and what automating it could be worth. No sales pitch. No commitment. Just a measured fit assessment.