Most AI projects look great in the demo and quietly die three months later. AIGenic designs, builds, and operates AI agents and automation for small and midsize companies, with the baseline measurement, governance, and support model that keeps them running long after the pilot.
The problem
Three failure modes account for most of the AI and automation work that gets written off. All three are avoidable, and none of them are technical.
Demos run on clean data and the happy path. Production runs on the exception. If nobody measured the process before you started, there is no honest way to say whether the thing worked. Cycle time, error rate, cost per transaction, hours consumed: without those numbers you have an opinion, not a result.
The build team leaves. A vendor changes a screen, a policy changes, a form gets a new field. There is no runbook, no monitoring, no named owner. Confidence erodes, someone quietly goes back to the spreadsheet, and the automation becomes shelfware nobody wants to admit to.
"We need AI" is not an objective. Reducing quote turnaround from three days to four hours is. Without a number attached to the outcome, the project has no way to prove its value and no defense the first time budget gets tight.
What we build
Six things AIGenic does. Each one is scoped to produce something operational you can run, measure, and support.
Agents that read, decide, and act inside your real systems, with tool access, guardrails, human approval where the risk warrants it, and a full audit trail.
How it worksA ranked, costed pipeline of what to automate and in what order, with the business case, the data and access dependencies, and the honest list of what isn't worth doing.
How it worksExisting bots that break weekly, cost more than they save, or came with a license you can't justify. We stabilize, re-platform, or retire them, whichever the numbers support.
How it worksThe unglamorous work that pays: intake, quoting, invoice and document handling, reporting, and getting systems that were never meant to talk to each other to do exactly that.
How it worksWho can deploy an agent, what data it may touch, how output is reviewed, what gets logged, and what happens when it's wrong. Written to be followed, not framed.
How it worksMonitoring, exception handling, tuning, and change management once it's live, either as your ongoing operator or as backup for the person on your team who now owns it.
How it worksHow an engagement runs
Every engagement starts with a number and ends with documentation you could give to another engineer. The middle part is the easy part.
We map the process as it actually runs, including the workarounds nobody documented, then measure it. Volume, cycle time, error rate, touch time, cost. That number is what everything after gets judged against.
Target-state design, the integration and data dependencies, controls and review points, and a cost/benefit model with the assumptions written down where you can argue with them.
Built in increments against real data and real exceptions. Proven in a controlled run beside the current process, so the go/no-go decision rests on evidence rather than enthusiasm.
Monitoring, runbooks, named owners, and a documented handover. You get source, configuration, prompts, and architecture. Enough for anyone competent to pick it up without calling us.
Why AIGenic
The hard part was never the model. It's the exception queue, the access review, the person whose job just changed, and the question of who gets paged at 6am.
Proof, not promises
Most AI consultancies ask you to take the capability on faith. These are described in enough detail that you can judge the engineering before you spend anything.
Mines public GIS data to build off-market target lists, then models subdivision feasibility. Backtested against 33 closed transactions, which is how we found the first model was off by 3.35×.
Read the architectureWatches a defined market for the events that create an opening and returns four companies each morning, with who to call, why today, and every score opened up to its reasoning.
Read the architectureDaily discovery, deduplication, weighted scoring against a published model, and an auditable log for a regulatory requirement, with hard boundaries written into the spec.
Read the architecture610 verified sources in Postgres with provenance tagging, a single sanctioned write path, and integrity confirmed three independent ways. The foundation any trustworthy internal assistant needs.
Read the architectureWho this is for
Thirty minutes, no pitch. Describe the process that's costing you the most and I'll tell you whether automation is the right answer, including when it isn't.
