Solutions
Systems with a job to do
Six kinds of work we take on. Each has a problem statement, what we build, and a hard limit.
AI Agents
The problem
Manual workflows stay slow when the work needs retrieval, judgment, and action — not only a chat reply.
What we build
Autonomous and semi-autonomous agents with scoped tools, memory, and guardrails for multi-step work.
Limit
We will not ship an agent without defined permissions and a failure path.
Typical use cases
- Research and analysis
- Document processing
- Internal operations assistants
- Support workflows with clear escalation
Outcome
Fewer handoffs on defined tasks, with humans kept on the decisions that matter.
Agentic AI Systems
The problem
One model call cannot cover processes that span systems, roles, and approval gates.
What we build
Multi-agent architectures with shared context, orchestration, and human escalation.
Limit
We will not design agent networks without a clear owner for each write action.
Typical use cases
- Cross-system workflows
- Multi-step decision pipelines
- Research and synthesis with review
- Operations that need audit trails
Outcome
Coordinated work that mirrors how the organization already decides.
AI Applications
The problem
A chatbot on an existing product rarely changes outcomes if the data model and flows stay unchanged.
What we build
Applications where AI is embedded in user flows, data, and controls from the start.
Limit
We will not treat a prototype UI as a product without evaluation and ops.
Typical use cases
- AI-native product features
- Knowledge systems with retrieval
- Decision support interfaces
- Operator tools with oversight
Outcome
Features that improve a named user job — not novelty for its own sake.
Enterprise AI Automation
The problem
Rule-based automation breaks when inputs are unstructured or the path needs context.
What we build
Automation that reads documents and events, decides within policy, and connects to existing systems.
Limit
We will not automate a process we cannot observe or roll back.
Typical use cases
- Invoice and contract processing
- Compliance review support
- Report generation
- Onboarding workflows
Outcome
Higher throughput on defined document and ops paths, with auditability.
Custom AI Solutions
The problem
Off-the-shelf tools miss proprietary data, workflow shape, or compliance constraints.
What we build
Purpose-built systems designed around your data, integrations, and success criteria.
Limit
We will not start a custom build without a written success criterion and owner.
Typical use cases
- Domain assistants
- Proprietary knowledge systems
- Industry-specific automation
- Implementation of an agreed architecture
Outcome
A system that fits the organization — not the reverse.
AI Strategy and Architecture
The problem
Ad-hoc experiments create debt when there is no path from trial to production.
What we build
Roadmaps, architecture, technology evaluation, and implementation plans tied to outcomes.
Limit
We will not recommend a stack we would not operate under your constraints.
Typical use cases
- Readiness assessment
- Architecture review
- Build versus buy analysis
- Sequenced delivery plans
Outcome
Clear direction and fewer false starts before engineering spend.
Tell us what is not working.
Describe the workflow, the constraint, and the decision you need to make. We will say what we can take on — and what we will not.