We build AI systems grounded in real expertise — not systems that fill knowledge gaps with well-phrased guesses. For clients ranging from early-stage startups to large organizations, we:
- Define business requirements and scope realistic timelines.
- Evaluate and augment datasets, build infrastructure, and create processes.
- Develop solutions as simple as possible, and as complex as needed.
- Identify new capabilities and the business opportunities they create.
AI development is closer to applied research than to software engineering. It requires workflows that enable fast iteration and experimentation, smart risk mitigation, and tight feedback loops with business experts. Models need monitoring in production and updating as new data arrives and concepts drift. We bring the research discipline to make this work.
We Build LLM-Based Systems
Large language models are powerful, but deploying them effectively requires more than API calls. Off-the-shelf models hallucinate, lack your domain knowledge, and can’t access your proprietary data. We build systems that ground LLMs in your organization’s actual expertise.
Retrieval-Augmented Generation (RAG) connects language models to your documents, databases, and knowledge bases. We design retrieval pipelines that surface the right context, reducing hallucinations and keeping responses grounded in your data. This works well when your knowledge is already documented and you need accurate, verifiable answers.
Fine-tuning adapts models to your domain’s vocabulary, style, and reasoning patterns. We help you curate training data, run experiments, and evaluate results systematically. This is the right approach when you need consistent behavior that reflects your organization’s expertise, not generic responses.
AI agents go beyond question-answering to take actions: querying databases, calling APIs, executing workflows. We build agents with appropriate guardrails and human oversight, turning language models into tools that actually get work done.
Often the best solution combines these approaches. We help you navigate the trade-offs and build hybrid systems that leverage each technique where it works best.
We Manage Uncertainty
Unlike conventional software, AI projects start with a hypothesis where feasibility is not guaranteed. Plans often change based on what the initial data exploration reveals.
We are experienced researchers, rigorously trained to check all explicit and implicit assumptions. We translate your business problem into a set of technical problems and define how to measure progress. We design small experiments, rapidly iterate on the results, and break large projects into milestones with well-defined decision points and realistic timelines.
We Create the Right Datasets
Your models are only as good as the data they were trained on. You need to understand the processes generating your data and carefully evaluate what was measured, and how. Many AI efforts go astray when data or metadata turns out to be insufficient, or assumptions about the data-generating process were never checked.
For LLM applications, this extends to curating retrieval corpora, designing evaluation datasets, and establishing ground truth for domain-specific tasks. Can you collect the right data? Is labeling going to be time-consuming or expensive? How do you verify that your knowledge base is complete and accurate? We help answer those questions and show you how to create valuable data assets at a fraction of the expected time and cost.
We Build the Right Solutions
The AI vendor landscape evolves fast. APIs disappear, pricing changes, and there is often a mismatch between your problem and the one solved by off-the-shelf software. Duct-taping third-party services together leads to fragile, hard-to-maintain systems. There is real value in building your own processes and tools, and in owning that edge over your competition.
We also avoid unnecessary complexity. If a simple retrieval system works, you don’t need an agent. If a smaller model handles your task, you don’t need the largest one. We build simple solutions as both benchmark and sanity check. Minimal systems mean shorter feedback loops, lower costs, and faster iterations.
We Create Insights
We clearly communicate what works and which aspects are still uncertain. We want you to know where opportunities lie, so we maintain a log of ideas and results as we experiment. We want you to understand when and why models will begin to fail, so you recognize it when it happens in production.
Businesses need interpretable, auditable AI — in regulated industries like finance and healthcare, this is a requirement. We design for compliance from the ground up, with proper logging, evaluation frameworks, and human oversight built into the system architecture.
Interface design and visual presentation of data are essential, not an afterthought. We build custom tools for data exploration, interactively visualizing model behavior and retrieval quality. We can help you build tools for interactive experimentation and dashboards that surface problems before they reach your users.