AI SolutionsAI your operators will actually use
AI agents that know your business and answer to your people. Analysis and recommendations you can supervise, audit, and trust.
AI earns its keep when it's grounded in your real data and accountable to a human. We build agents and LLM-assisted analysis that read live business context, run the analysis your team repeats every week, and hand back recommendations in plain language, with the reasoning visible.
It's the pattern behind Glaukos, the AI analyst inside our Aegis BI platform (now in production), and the Oracle analysis engine in MyBudgetNerd, live on the App Store. Not a demo. Software people use.
You want AI capability but can't risk a black box making unsupervised calls
Analysts spend hours writing the same narrative summaries of the same reports
Expert knowledge is locked up with two people and doesn't scale
Off-the-shelf chatbots don't know your data, your metrics, or your risks
A Worked Example
Glaukos, the analyst inside Aegis BI
A production AI agent with a person in the loop: it reads the live dashboard context, runs risk-first analysis, and returns a plain-English briefing with its reasoning visible. It advises; the operator decides. Thera applies the same discipline to bid/no-bid scoring, where an unexplained recommendation is worthless.
Go and lookTwo ways in: a 2–3 week fixed-scope diagnostic on where an agent genuinely helps and where it would just add risk, or 4–6 weeks to an agent running in production with a person in the loop. Most start with the diagnostic.
Grounded in your numbers
Agents read live business context, so answers cite your actual figures instead of generic advice.
A person stays in charge
The agent recommends, your operator decides. Worst case is a bad suggestion, never a bad action.
Proven in our own products
The same architecture runs in Aegis BI, live in production, and MyBudgetNerd, live on the App Store.
Related Work
Aegis BI
AI-assisted financial intelligence that stays current: connect OneDrive or Google Sheets, then read the numbers anywhere through command-center dashboards, forecasting, what-if scenarios, and the Glaukos AI analyst.
Flagship · App Store
MyBudgetNerd
A shipped consumer finance product: PDF statement parsing, ML transaction categorization, and the Oracle engine for anomalies, category outlook, and plain-language explanation.
iOS · App Store
Aegis BI: Building an AI Financial Command Center for Small Business
How we designed and built a BI platform that reads the files a business already keeps, forecasts from them, and travels in a pocket, now live in production.
4 min read
MyBudgetNerd: Shipping Privacy-First ML Personal Finance to the App Store
From PDF parsing pipeline to 5.0-star iOS app: designing, building, and shipping a consumer ML product with privacy as the architecture, not the disclaimer.
2 min read
Thera: Scoring Federal Opportunities for a Contractor With Money on the Line
How we built capture intelligence for federal contractors around one hard rule, that a score has to be a gate rather than a ranking, with a design partner who loses work when the tool is wrong.
6 min read
AI Agents with a Human in the Loop: Trustworthy Automation
The AI agents that survive real operations share one principle: they recommend, humans decide. How we build agents that earn trust rather than demand it.
2 min read
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