Grounded assistants
Connect model behavior to the right sources, context, permissions, and citations.
- Retrieval and source authority
- Context design
- Fallback behavior
All capabilities
Automate
AI & workflow automation
We focus on the work around the model: source authority, retrieval, orchestration, evaluation, review, fallbacks, and the product experience that lets people use automation with confidence.
AI agentsDocument workflowsHuman review
Apply intelligence with control
When this capability matters.
Put AI inside a real operating workflow with grounded context, clear review points, durable state, and measurable quality.
01
A team has an AI prototype but no reliable operating or evaluation model.
02
People repeatedly interpret, route, summarize, or reconcile the same information.
03
Automation needs clear source authority, ownership, and human review.
Focus areas
The decisions inside the work.
Agent workflows
Coordinate multi-step work with durable state, ownership, and explicit human gates.
- Task orchestration
- State and handoffs
- Review and approval
Quality & governance
Define what good means and make failure visible before production users find it.
- Evaluation strategy
- Guardrails and monitoring
- Audit evidence
What the work produces
Artifacts another person can trust.
- 01AI use-case and risk model
- 02Grounded assistant or agent workflow
- 03Human review experience
- 04Evaluation and guardrail suite
- 05Production monitoring plan
Ways to engage
Use the amount of team the problem needs.
Focused intervention
Resolve one high-leverage product, workflow, architecture, or release problem without creating a sprawling program.
Best for a defined decision or blocked initiative.
Product initiative
Take a system from definition through design, implementation, and launch with one accountable delivery thread.
Best for a new product or material workflow change.
Embedded partnership
Work alongside your leadership and delivery teams across a roadmap, strengthening the system as it evolves.
Best for ongoing product and operational ownership.
Common questions
Useful detail, up front.
Do we need to train a custom model?
Usually not at the start. We first test whether strong context, retrieval, tools, and workflow design solve the problem before adding model training complexity.
How do you handle hallucinations?
We combine source grounding, constrained actions, explicit uncertainty, evaluation, fallback behavior, and human review based on the cost of being wrong.
Can AI operate across our existing systems?
Yes, when permissions and system boundaries are explicit. We design the integrations and audit trail as part of the product, not as an afterthought.
Start with the real problem
Have a difficult system to untangle?
Bring us the workflows, edge cases, handoffs, and constraints. We will help you find the product hiding inside them—and carry it through to working software.