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:
- Identify new capabilities and the opportunities they create.
- 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.
We Navigate the AI Landscape For You
New models, tools, and vendors appear every week, each promising transformation. We track what’s changing, evaluate new capabilities against your actual business needs, and recommend only what’s proven to work. When off-the-shelf tools fall short, we build what’s missing.
AI development is closer to applied research than to software engineering — projects start with a hypothesis, and feasibility is not guaranteed. We translate your business problem into measurable technical problems, design small experiments, and break work into milestones with clear decision points. We monitor what we ship so it keeps working as your data and the landscape evolve.
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. Minimal systems mean shorter feedback loops, lower costs, and faster iterations.
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 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 Create Insights
We clearly communicate what works and what’s still uncertain — through custom dashboards, interactive tools for exploring model behavior and retrieval quality, and visualizations that surface problems before they reach your users. You’ll know where opportunities lie, and when and why models will begin to fail.
In regulated industries like finance and healthcare, interpretable and auditable AI is a requirement. We design for compliance from the ground up, with proper logging, evaluation frameworks, and human oversight built in.