Validated · Case study
AI code review quietly added ~$49K of technical debt in 4 months.
ProblemAI assistants fire off thousands of code suggestions — only 2.6% ever get human feedback, and the redundant ones quietly pile up as maintenance debt.
SolutionWe built an evaluation layer that scores every suggestion against your team’s standards and rejects the redundant ones before they ship.
ResultsUp to 87.5% agreement with senior engineers and +47% more consistent than ad‑hoc prompting.
~$49K
technical debt AI added · 4 months
Validated · Peer‑reviewed
Too little data to train a model — too much nuance for a generic one.
ProblemSpecialized, sparse text — niche domains, non‑standard language — is too small to train classical ML on, and general‑purpose models miss the nuance.
SolutionWe engineer a prompt‑and‑context layer built on our ICI framework — no training data required — that reads your language, not generic English.
ResultsWith GPT‑4 and our prompt engineering, we matched expert human coders and beat classical ML by 32% on the rare cases it misses.
+32%
higher F1 than classical ML on the cases it misses
Validated · Client engagement
People analytics — proof your program built relationships across your groups.
ProblemA statewide education program needed to prove to funders that its work created relationships among educators — not just gathered people who already knew each other.
SolutionUsing people analytics, we mapped who connects with whom and traced where every working relationship came from — and which ones the program created.
Results99% of traceable relationships were created by the program — only 2 pre‑existed. We also pinpointed the few people acting as connectors who hold the network together.
99%
of traceable relationships created by the program
Ongoing since 2019
"Our team isn't up to speed on AI."
Every session is designed for your use case, team, and level — data science, engineering workflows, AI transformation, or wherever you're headed.
In progress
"Our data can't leave the building."
Runs correctly and securely behind your own firewall — on‑prem and edge, firmware‑tested with our partners.
Our product · Early access
Network surveys — without the 35+ hours of wrangling.
ProblemTraditional survey platforms take heavy custom logic to build a relational (network) survey — then 35+ hours of wrangling to turn the export into correct, analysis-ready data.
SolutionArs Network builds the relational logic for you and delivers analysis-ready data on study close — nodes, edges, responses, no wrangling.
~$8–10K
estimated saved per study · build + wrangling
Ongoing · Virtual AI Assistant · Bespoke
Every missed call after hours is a booking gone.
ProblemSolo owners and small practices — plastic surgeons, clinics — can't answer every call after hours or on weekends. Each missed call is a lost booking, and generic answering services don't clear the HIPAA or governance bar.
SolutionWe build each assistant for the individual clinician's practice — your intake, your rules, your voice — on our Ars Hygieia base. HIPAA-compliant, governance-grade, answering and booking around the clock.
24/7
answered — after hours + weekends
In progress
"When our experts retire, the knowledge leaves with them."
ProblemThe know‑how that keeps your floor running is tacit — a veteran hears a machine and knows to tighten the left bolt before it fails. It's never written down, and it walks out the door at retirement.
SolutionWe capture that hands‑on knowledge right on the floor and turn it into something your team owns and can verify — processed in weeks, not months, through a proprietary engagement. The backbone of succession planning.
In progress
"This workflow is slow and full of busywork."
We redesign the workflow with your team and add AI only where it saves hours — sometimes the fix isn't AI.
In progress
"Adoption's stalling — and people fear for their jobs."
We find the champions your people already learn from to speed adoption — and show AI augments them, not replaces them.