What we do.
Five services. Most clients start with a study and go from there.
Feasibility studies
Most AI programs fail before they start. The problem wasn't worth solving, or the data to solve it didn't exist. A feasibility study finds that out before you commit a budget.
We spend two to four weeks inside the business. We interview the people who do the work, look at the data as it actually is, and test the one or two assumptions everything else depends on. You get a written recommendation. Sometimes the recommendation is don't.
What you get
- Candidate use cases, ranked by value and difficultyDocument
- Data audit: what exists, what's usable, what's missingDocument
- Technical approach, cost, and timeline for the top candidatesDocument
- Readout with your leadership teamSession
Research and prototypes
A problem worth solving still has to be solvable on your data. That's a research question. We answer it with experiments.
We set the evaluation first: what good enough means, how it's measured, on which data. Then we run the serious candidates against it, from simple baselines up to current models, and build a prototype of the one that wins. We report what didn't work too. That's usually the more useful part.
What you get
- Evaluation set and metric your team keeps usingCode and data
- Experiment log comparing approaches on your dataDocument
- Working prototype on real inputsSoftware
- Build recommendation with architecture, cost, and riskDocument
LLM and agent systems
Language models are good at a narrow set of things. Reading and writing text. Pulling structure out of documents. Answering questions over a body of knowledge. Taking defined actions with the right tools. Push them past that without constraints and they get unreliable.
We build systems that keep the model inside what it does well. Document processing with human review on low-confidence cases. Retrieval over your own knowledge, with citations. Assistants with narrow tool access and full logging. Every one ships with an evaluation suite, so you know when a model update makes things better or worse.
Typical systems
- Document extraction and classification pipelinesOperations, finance, legal
- Knowledge assistants over internal policies and recordsSupport, clinical, compliance
- Agents that run defined workflows across internal toolsBack office
- Evaluation and monitoring for LLM features you already haveProduct teams
Machine learning models
Not every problem is a language problem. Forecasting demand. Scoring risk. Catching anomalies in sensor data. Optimizing a schedule. These are classical machine learning and operations research, and a lot of the measurable value still sits there.
We design models for your domain and train them on your data. Baseline first. Real evaluation. A clear account of where the model is uncertain. Documentation written for the people who will maintain it.
Typical problems
- Demand and capacity forecastingSupply chain, logistics, energy
- Credit, fraud, and operational risk scoringFinancial services
- Anomaly detection on equipment and transaction dataManufacturing, payments
- Scheduling and allocation optimizationField operations, healthcare
Production and handover
A model is not a system. Production means pipelines that run every day, deployment that fits your infrastructure, monitoring that catches drift, and a team that knows what to do when it does.
We build that layer and hand it over. Your engineers pair with ours from the start. Runbooks, architecture notes, and decision records get written as we go. The program ends when your team is running the system without us.
What you get
- Production pipelines and deployment in your environmentSoftware
- Monitoring for drift, quality, cost, and latencySoftware
- Runbooks, architecture notes, decision recordsDocuments
- Training for the operating teamSessions