AI case studies.

Equity Index Strategy Backtesting

Strategy evaluation used to run once a quarter. It now runs daily, so investment managers can test an idea the same week they have it.

The challenge

A US pension fund wanted to evaluate candidate equity index strategies against historical data before committing capital to them. The existing evaluation process ran on a quarterly cycle, and strategy ideas waited on it.

What we built

We built a web application for backtesting equity index strategies, using GenAI to process the historical financial data the strategies are tested against, and automated the fund's proprietary model for tracking changes across S&P US indexes.

Outcome

Strategy evaluation moved from a quarterly process to a daily one. Investment managers test ideas the week they have them, and the strategies that survive arrive at the committee already backtested.

Legal Interview & Document Automation

An AI agent now runs the first client interview and drafts the pre-litigation documents. The drafts pass the firm's compliance review.

The challenge

Pre-litigation interviews and document preparation at a major US law firm ran on attorney hours. Routine intake work was consuming time the firm wanted on complex matters.

What we built

An LLM interview agent that conducts initial client interviews and drafts pre-litigation documents, integrated with the firm's case management platform and built to follow its established interview protocols.

Outcome

The agent now runs initial client interviews and produces pre-litigation drafts that clear the firm's document-quality and compliance review. Attorney hours go to the matters that need them.

AI Data Agent for Self-Serve Analytics

People outside the data team now ask the database questions in plain English and get their answers without writing SQL or filing a ticket.

The challenge

A technology company's Redshift database held answers its business teams couldn't reach. Every question had to go through someone who could write SQL.

What we built

An AI agent that translates plain-language questions into SQL against the Redshift schema, so staff outside the data team can query the database themselves.

Outcome

Non-technical staff across departments now pull their own answers from the database, without SQL and without a ticket queue.

Store Labor Planning & Optimization

The retailer now sets store staffing from a profitability model and a labor optimizer. Profits went up without cutting service levels.

The challenge

A large US retailer's planning process couldn't balance store staffing against profitability targets. Some stores carried unnecessary labor cost while others missed their service levels.

What we built

A predictive model for store-level profitability paired with an optimization engine for labor allocation, built on historical performance data and local market conditions.

Outcome

The retailer now plans workforce allocation from the model's staffing recommendations. Store profitability improved without trading away service levels.

AI-Powered Content Generation for EdTech

A GenAI pipeline now drafts the lesson plans, quizzes, and explanations. The writing team spends its time on curriculum and quality control instead of first drafts.

The challenge

An EdTech startup's personalized-learning model needed more lesson plans, quizzes, and explanatory texts than its writers could produce.

What we built

A GenAI content pipeline with prompting developed per material type: lesson plans, quizzes, and explanatory texts, each with its own generation and review path.

Outcome

The pipeline now drafts the bulk of new material. The startup's creators spend their time on curriculum design and quality control instead of first drafts.

GenAI Strategy for Enterprise Software

Three proposed pilots became one. The initiative tied to renewal economics got funded, and the other two were stopped before they cost anything.

The challenge

An enterprise software startup wanted GenAI in its product and its operations but had no basis for deciding which initiatives deserved funding.

What we built

We worked with the leadership team on a GenAI strategy grounded in their current product suite, market position, and internal capabilities.

Outcome

The strategy produced a funded shortlist tied to business objectives, and an explicit list of initiatives not to pursue. Two of the three proposed pilots were stopped before they consumed budget. The initiative tied to renewal economics got its funding accelerated.

Unstructured Data Mapping Pipeline

Client data that used to be mapped by hand now flows through a pipeline. The company handles more of it, faster, and the result no longer depends on who did the work.

The challenge

A data integration provider was mapping growing volumes of unstructured client data to its internal schema by hand. It was slow, and the results varied from operator to operator.

What we built

A GenAI pipeline that categorizes and structures incoming data from varied sources and maps it to the company's schema, using NLP and machine-learning models in place of manual review.

Outcome

Mapping that required manual intervention now runs through the pipeline. The company handles a larger volume of client data, faster, and the operator-to-operator inconsistency is gone from the standard path.

LLM Agent for Medical Device Support

A voice agent now handles roughly 60% of inbound patient calls on its own. The human team works the complex cases.

The challenge

Patient inquiries were outrunning a medical device company's support team, and the answers had to be accurate. These are people asking about a device they depend on.

What we built

A voice LLM agent that conducts calls with patients directly, integrated with the company's product documentation and support databases so its answers stay current.

Outcome

The agent handles routine inquiries and follow-up calls. The human team works the complex cases, instead of the queue.

AI-Powered Executive Coaching Platform

The coach's method now runs as interactive AI sessions, so executives he never had calendar time for can work with it.

The challenge

A CEO coach with a methodology built over decades of advising Fortune 500 leaders couldn't take on more clients without diluting it. Demand had outgrown one calendar.

What we built

An AI platform that encodes his methodology. It combines LLMs with scenario modeling and runs the kind of coaching interaction he runs, in his sequence and his voice.

Outcome

His methodology now reaches executives he would never have had calendar time for, in interactive sessions that follow his approach rather than a generic chatbot script.

Demand Forecasting Center of Excellence

The forecasting center is staffed by analysts trained locally. Planning accuracy improved, and the region now has forecasting talent it did not have before.

The challenge

A Caribbean conglomerate wanted demand forecasting across its portfolio of businesses, staffed locally, in a market where that talent had to be developed rather than hired.

What we built

We designed and stood up the forecasting function end to end: forecasting models, a training curriculum for local analysts, and the hiring and development program to make the capability self-sustaining.

Outcome

The center of excellence runs on trained local analysts. Planning accuracy improved, and the region gained a forecasting talent pipeline it didn't have before.