A Apperdashery From AI to impact.

Use cases

AI agents we have built for real work.

Recent examples of multi-agent systems we have designed and developed. Each one takes a slow, manual process and turns it into a fast, reviewable workflow, with a person kept in charge of the final call.

Use case 01 · Mobile app modernization

Modernize the software your business runs on, one safe step at a time.

The challenge

AI coding tools can generate code quickly, but your team still needs to understand the existing application, make the right changes, and verify that everything works as expected. Without a structured process for reviewing and testing AI-generated changes, speed gains can turn into additional rework and technical debt.

What we built

A team of AI agents, each with one job. They map what you have, plan the work in small steps, add tests that protect what works today, make the changes, and check each one independently.

The result

Steady, visible progress in small, tested changes your team approves, without a risky big-bang rewrite.

InputYour current systems
Web appsMobile appsInternal toolsDatabases & APIsOutdated frameworks
AI agent team
  1. ScoutMaps how the system works today and where the risks are.
  2. PlannerBuilds a roadmap of small, ordered steps tied to your goals.
  3. TesterAdds tests that lock in today's behavior before anything changes.
  4. BuilderMakes one change at a time. Several can work in parallel.
  5. ReviewerChecks every change against the plan and your standards.

↺ Review findings go back to the builder

OutputOne improvement at a time

Small, tested changes

Each one is easy to read, easy to roll back, and arrives with its tests passing.

Decision

Your team approves every change

Nothing ships without a person's sign-off. The work stays in your own code repositories and tools.

Illustrative engagement.

Use case 02 · Safety & health regulations

Put the safety regulations in every worker's pocket, with the source on every answer.

The challenge

A construction crew on a job site needs to know what personal protective equipment is required for the work. The answer sits in federal regulations spread across hundreds of web pages, written in legal language and hard to search on a phone. A general-purpose chatbot might answer quickly, but nobody can tell where its answer came from.

What we built

A retrieval-augmented generation (RAG) system behind native iOS and Android apps. It indexes the OSHA regulations ahead of time, finds the sections that match a plain-language question, and provides answers with citations to those sections.

The result

A plain answer in seconds, tagged with the regulation number and linked to the original OSHA page, so anyone can check it.

Swift · SwiftUIKotlin · ComposeFastAPIChromaDBSentence Transformers
SourceIndexed ahead of time
OSHA regulation pagesConstruction · 29 CFR 1926PPE subpartsSection numbersSource URLs

Scraped, cleaned, split into sections and embedded. Each chunk keeps its citation and the link back to osha.gov.

Field questioniOS & Android app

When are safety nets required in construction?

Retrieval-augmented generation (RAG)API service
  1. ExpandTurns everyday wording like "do I need" into regulatory terms like "shall be provided".
  2. RetrieveFinds the five most relevant regulation sections in the vector store.
  3. GroundAnswers using only those sections, never from memory.
  4. CiteEvery requirement in the answer is tagged with its section number.

⚑ If the sections don't cover it, the answer says so

OutputAnswer + sources

Safety nets are required when the work surface is more than 25 feet above the ground or water and ladders, scaffolds, catch platforms, temporary floors, safety lines or safety belts are impractical (1926.105(a)).

1926.105(a) 1926.105 – Safety nets. osha.gov/laws-regs/regulations/standardnumber/1926/1926.105
Trust

Anyone can check the source

Each citation opens the original regulation, so the worker, the supervisor and the inspector are all reading the same text.

Demonstration project. Answers are informational and are not legal or compliance advice; always confirm requirements against the current regulation. OSHA is an agency of the U.S. Department of Labor. No endorsement or affiliation is implied.

Use case 03 · Investment research

Turn a watchlist into a researched shortlist.

The challenge

Checking every stock on a watchlist, spotting the real outliers and reading the news behind each one takes hours.

What we built

A pipeline of agents connected to Robinhood through MCP. They quote, screen, rank and research, and each step leaves a written report.

The result

A sourced brief in minutes, with a verdict and confidence level for every idea. A person still approves each trade.

InputsRobinhood MCP · web
Live quotesWatchlistsEarnings & filingsAnalyst ratingsNews
AI agents
Quote scoutBuy screenSell screenRankerNews researcher
Output

Daily opportunities brief

Ranked buys and sells, why each one moved, and what the data can't confirm.

Decision

You review and approve

Orders are previewed first and placed only after your explicit confirmation.

For informational purposes only. Not investment advice or a recommendation to buy or sell any security. Investing involves risk, including loss of principal. Robinhood is a trademark of its owner. No endorsement or affiliation is implied.

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