Private, local AI systems that actually run your business — not another SaaS subscription.

Your data goes somewhere else

Every time you paste a customer list, a schedule, or a deal into a cloud AI tool, it leaves your control. It sits on someone else’s servers, under their rules, and you never get a clear answer about where it went or what they kept. It isn’t just stored somewhere else — it can be used to train the company’s models, and nothing in the terms says your data is never used to train.

Another monthly bill

Every SaaS tool is a per-use or per-seat fee, and the fees stack. The bills never stop, the price creeps up, and if you ever cancel, your files are still sitting inside the tool. You own nothing.

Nobody actually used it

Someone demos the tool, maybe sets it up once, and then it sits there. The calls still go to voicemail, the leads still wait, and the business runs the same way it did before — minus the monthly fee.

Private AI Setup

We install AI on private hardware ABM installs and runs, and set it up for the work it needs to do. It answers the calls, sorts the leads, handles the paperwork, and your data is never used to train any model. For businesses that need real automation but don’t want their data on someone else’s servers.

Agentic Workflow Automation

Agents that take a task all the way through, start to finish. It reads the voicemail, writes down who called and why, and has the follow-up in your inbox by 8am. For businesses where calls, follow-ups, and paperwork keep slipping through the cracks.

AI Strategy for Small Business

We look at how your business actually runs and tell you what’s worth automating and what isn’t. You get a short, plain-language plan with a fixed price before anything gets built. For owners who want to know what makes sense before they spend a dollar.

Ongoing Support

ABM keeps the system running — that is the flat monthly. When it breaks, we fix it. When your workflow changes, we update it, and you call the person who built it, not a ticket queue. For businesses that don’t have in-house technical staff.

Real-estate operations portal

Before. Property and owner research was manual. Finding who owned a parcel, tracking down a working phone number, and getting it in front of whoever made the calls meant hours of copy-paste across public-records sites and spreadsheets. Nothing was queued, nothing retried, and the work stopped whenever the person doing it stopped.

What was built. A private operations portal with an automated research pipeline behind it: owner-entity lookup against public business and property records, phone discovery with a confidence tier per number so callers work the good numbers first, a checkpointed queue that resumes a crashed run instead of losing the batch, and a deal board from lead through offer with an offer-document generator.

Outcome. Research that was manual and serial became a queue that runs unattended and hands the caller a ranked list. Records processed per batch: [NEEDS NUMBER]. Operator time recovered per week: [NEEDS NUMBER].

Nightly trading pipeline

The founder’s own system — not a client engagement. Shown as proof of capability.

Before. A nightly pipeline was producing signals, but the ML backtests were silently taking zero positions on every asset. The models looked like they ran; the strategy never actually traded. A shared risk-manager object carried a circuit breaker that latched partway through the asset list and never reset, blocking every entry after it.

What was built. Diagnosed and fixed the latched circuit breaker — from 0 of 25 assets trading to 25 of 25. A nightly orchestrated pipeline: price fetch, feature rebuild, retraining of any stale model, news-sentiment scoring, overfitting detection, multi-horizon backtest, and signal fusion. Further model improvements sit behind flags that are off by default, so the production path keeps running exactly as before.

Outcome. A pipeline that trains, validates, and produces signals unattended every night, on local GPUs, with no per-token or per-call API cost.

The agent fleet

Before. The claim: local models, on owned hardware, doing real recurring work under written procedures and hard guardrails.

What was built. A fleet of single-responsibility agents, each running a written runbook instead of improvising — a builder that implements one scoped change and validates it mechanically, a reviewer/release lane that gates and deploys, a reliability watcher on a timer that has opened and driven a real incident on its own, and a data-quality auditor. A small local task bus with atomic claiming keeps two agents from taking the same job. No single agent can push to production alone.

Outcome. [NEEDS NUMBER] tasks carried to done through the build → review → ship chain. Cost per token: zero. The models run on local GPUs.