AI for small
businesses

Tennessee, United States

Evidence before deployment.

We help small businesses put AI to work safely and cheaply. We pick the right tool, wire it into what you already run, and build something custom when nothing else fits.

Every engagement ends in a written recommendation. Sometimes the recommendation is not to build.

Services

Three ways we help. Most engagements need only one.

Tool selection

Most small businesses don't need custom AI. They need someone with no product to sell, who looks at what's already on the market, tells you what it actually costs to run, and flags what it does with your data before you sign up.

Integration

We wire the tool into what you already run, your inbox, your spreadsheets, your point of sale, without leaking customer data to a service that was never asked to protect it, and without running up a bill nobody budgeted for.

Custom build

When nothing off the shelf does the job, we build it, using the same feasibility-first approach as our regulated work below. We prove it holds up on your data before either of us commits to it.

Fig. 1 Catheter tip position estimated from intracavitary ECG during a simulated advance

20% advanced

Proximal

0.41

60% advanced

Approaching

0.68

85% advanced

Cavoatrial junction

0.94

Motion artifact

No call

—
P-wave amplitude rises as the tip nears the cavoatrial junction, and the estimate reads that feature. Confidence is calibrated against held-out recordings rather than reported raw, so 0.94 has to mean what it says. In the fourth panel, motion artifact leaves the P wave unresolvable, and the model declines to call it. That's the behavior that matters most once a device is in someone's hands. Signals are synthetic and illustrative. This is not the output of a validated system.

Studies

Where the work is regulated or the stakes are high enough to earn a formal study first.

Feasibility study

Four to six weeks against your data, not a demo set, answering one question. Does the approach hold up? You receive a written report covering data assessment, baseline results, failure modes, what it costs to run in production, and a recommendation you can act on either way.

Implementation

Design, build, and hand over the working system, together with the evaluation harness that proves it still behaves after we leave. Your engineers own the code and the tests. We document what we did and why.

Validation support

Test protocols, model documentation, and traceable evaluation records that fit the quality system you already maintain. We support your regulatory and quality teams with engineering evidence. We do not replace them, and we do not make submissions on your behalf.

Domains

Some of the work we take on is regulated. Here's what that's looked like.

Medical devices

We work on software in and around regulated products, on-device signal and image processing, algorithm performance testing, retrospective evaluation against labeled data, drift monitoring after release, and the documentation that has to survive review. We work alongside manufacturers' engineering and quality teams, most often at companies small enough that the algorithm team is one or two people.

Natural language processing

We work on clinical and technical documents, information extraction, span annotation and annotation-guideline design, de-identification, retrieval over internal corpora, and evaluation of language-model output where a wrong answer is expensive. The work is usually less about the model than about defining the label, and that's where we start.

PICCdevice tip confirmed at the cavoatrial junctionfinding; no evidence of pneumothoraxnegated on post-procedure film. Continued on apixaban 5 mg BIDdrug.

Fig. 2 The note written after the procedure in Fig. 1. Negation is drawn dashed because a negated span is not an extraction. It records what was ruled out, and a system that misses it reads the note backwards. Synthetic text.

Method

In order. Each step is a gate, not a formality.

  1. 01

    Intake

    One conversation, a look at what data exists, and a question narrow enough to answer. No cost, no deck.

  2. 02

    Study

    Four to six weeks of hands-on work with weekly written notes, so nothing in the final report is a surprise.

  3. 03

    Report

    Specific, reproducible, and yours to keep, including the case against building, stated as plainly as the case for it.

  4. 04

    Build

    Only when the report supports it. Scoped from the study's findings, not from an estimate made before the data was seen.

Fit

Said early to save both sides a month.

A good fit

  • Small businesses, roughly one to two hundred people
  • Willing to start small and prove a tool works before scaling spend
  • Regulated or high-stakes work, where being wrong has a cost you can name
  • A person on your side who will read the report and make the call

Not a fit

  • Strategy decks with no implementation behind them
  • Projects that need a vendor to say yes
  • Work that depends on data you do not have permission to use
  • No one accountable for what the system decides

Contact

Replies come from a person, usually within a day.

Tell us what you're trying to do with AI and what's stopped you so far.