Evaluation loops
Representative tests, acceptance criteria, failure modes, and quality reviews that make AI output improvable.
Reviewable AI adoption systems.
We help teams turn high-trust AI workflows into systems they can inspect, operate, and improve.
What the work includes.
Evals, tools, review gates, teaching surfaces, and feedback loops built around the workflow your team actually needs to run.
The strongest fit is not generic AI automation. It is adoption work for teams that need evidence, handoff, privacy controls, and durable practice before they can trust an AI workflow.
Agent tools and skills
Review tools, agent skills, structured procedures, context pipelines, and typed outputs that turn prompts into repeatable workflows.
Workflow demos
Reference implementations and realistic demos that show stakeholders how the capability works against their actual materials.
Training and handoff
Use-case libraries, quick-start guides, review playbooks, and operator documentation built for teams that need to own the practice.
Governance by design
Review gates, audit trails, privacy checks, observability, and escalation paths for work that has to survive scrutiny.
Adoption feedback
Mechanisms for capturing what people correct, where trust breaks, what needs product work, and what should become reusable.
Accessibility and education teams
Teams with real content, review pressure, privacy constraints, and AI workflows that need evidence before adoption.
Frontier AI deployment teams
Groups helping customers or internal teams turn LLM capability into production workflows, reference implementations, and repeatable enablement patterns.
High-trust content operations
Workflows under accessibility, procurement, legal, governance, or stakeholder review where visible reasoning and correction loops are part of the product.
Engagements
Start narrow, prove the work, then build only what the evidence supports.
Start Here
1-2 weeks
AI Adoption Systems Discovery
A paid working engagement for teams with a workflow they think AI could help, real materials to test, and too many adoption risks to responsibly scope a build.
Outputs
- Representative sample and workflow test
- Prototype, demo, or harness for the part that matters most
- Eval questions, failure modes, review needs, and risk map
- Build, buy, narrow-scope, or stop recommendation
Good fit
- Document transformation or semantic content workflows
- Alt text, image description, or accessibility review systems
- Teams that need adoption evidence before asking for production budget
If Warranted
Phased build
Production AI Workflows
For teams that have tested the workflow, found the shape of the build, and are ready to make it reliable enough for real use by operators, reviewers, and stakeholders.
Outputs
- Production agent, pipeline, or review workflow from a defined scope
- Documented procedures, review tools, structured prompts, evaluation harnesses, and operator-facing workflows
- Human review gates and domain-specific failure handling
- Deployment, observability, documentation, and operational handoff
Example build areas
- PDF-to-semantic-content pipelines
- Accessibility-aware content agents
- Expert review workflows that need audit trails
After Launch
Monthly or quarterly
Enablement & Adoption Support
For teams with a launched workflow that has to keep working as content, models, standards, and user expectations change.
Outputs
- Reserved specialist access for production questions
- Prompt, model, provider, and tool change assessment
- Training updates, use-case libraries, and demo refinement
- Review of quality drift, failed cases, and new edge cases
Good fit
- Teams operating a production AI workflow
- Organizations with accessibility, legal, or procurement review pressure
- Clients that need continuity without hiring the specialist role full time
Why discovery comes first
Discovery is where the team turns a promising AI capability into a buildable adoption system. We test real content, real review constraints, and a working prototype so the production scope is based on evidence instead of appetite.
That gives both sides the materials needed to make a clear call: representative test cases, visible failure modes, eval questions, assumptions, non-goals, and a next-step recommendation grounded in the workflow itself.
Bring the real workflow.
Send the content, review process, failure modes, and timeline. We will help decide whether discovery is the responsible next step.
Request a discovery fit check