Agentic AI, implemented.
Agentic systems that operate inside real business constraints.
Whatever the strategy deck says, the problem is implementation. The models work. Getting them to change how work happens is the hard part.
What we build.
Agents and copilots
- Agents
- Systems that act inside a workflow, beyond answering questions about it
- Copilots
- Assistance embedded where the work already happens, with no separate tool to open
Knowledge and automation
- RAG and knowledge systems
- Retrieval that is accurate, current, permission-aware, and traceable to a source
- Workflow automation
- Removing manual steps that consume capacity without adding judgment
Applications and data
- Enterprise AI applications
- Applications where AI is the core of how the product works
- Data and systems integration
- The foundation the rest of it depends on, which is usually the real project. Agentic systems fail on data and integration far more often than they fail on reasoning.
The distance between an AI pilot and an AI system.
The pilot stopped for a reason, and the reason is rarely the model.
The API call is one line. The work is in the permissions, the data, the failure modes, and the people around it.
Context before solutions.
Understanding the work well enough to know where an agent belongs and where it does not. A common failure is automating a step that was never the bottleneck.
Every engagement starts by understanding the work. Technology selection comes after, and that order matters more here than anywhere else, because agentic systems are unusually sensitive to context. The same architecture that works in one organization fails in another with different data, incentives, and risk tolerance.
The questions we answer before building anything
- 01What outcome matters, and how will we know if it moved?If nobody can name the measure, the project has no definition of done.
- 02Where is execution breaking down today?Automating around a broken process usually preserves the break and hides it.
- 03What is the smallest system that would prove this works in production?Something real and narrow, used by actual people, which rules out a demo.
Understanding the work well enough to know where an agent belongs and where it does not. A common failure is automating a step that was never the bottleneck.
Every engagement starts by understanding the work. Technology selection comes after, and that order matters more here than anywhere else, because agentic systems are unusually sensitive to context. The same architecture that works in one organization fails in another with different data, incentives, and risk tolerance.
The questions we answer before building anything
- 01What outcome matters, and how will we know if it moved?If nobody can name the measure, the project has no definition of done.
- 02Where is execution breaking down today?Automating around a broken process usually preserves the break and hides it.
- 03What is the smallest system that would prove this works in production?Something real and narrow, used by actual people, which rules out a demo.
When the answer is not an agent.
Fixing the process, or writing conventional software that behaves the same way every time, solves some problems better than an agent would. Reaching for an agent in those cases adds cost and a new category of failure in exchange for very little.
When an agent is usually the wrong tool
- 01The task is fully deterministic and already well specified.
- 02The cost of being occasionally wrong is higher than the cost of being always slow.
- 03The real bottleneck is a decision nobody is empowered to make, which no technology fixes.
- 04The underlying data is not good enough yet, and the agent would only surface that faster and at greater cost.
A no in the first conversation costs an hour. A yes that fails seven months in costs the seven months.
Name the workflow that keeps stalling.
Tell us where it breaks. Whether an agent belongs there, and what it would take, is the first thing we work out.
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