Applied AI, no hype.
We use AI when it demonstrably improves outcomes: fewer repetitive tasks, better answers, faster decisions—with evaluation, privacy, and defined fallback paths.
What we build.
Practical applications that reduce manual work, improve consistency, and surface information faster.
Internal agents & copilots
Assistants for support, operations, or internal teams—faster, more consistent responses integrated with your existing tools.
Document extraction & classification
Structured output from documents, emails, and forms—extraction, tagging, and routing at scale while reducing manual review.
Semantic search & knowledge base
Natural-language search over internal documents, wikis, and emails—find what you need without exact keywords.
Workflow automation
Multi-step processes, approval flows, and integrations that run reliably—with observability and error handling built in.
Responsible by design.
Four principles we apply to every AI engagement.
Privacy & data handling
We design for data minimization from the start. We choose providers and configurations where your data is not used for training by default—unless explicitly agreed.
Evaluation & quality metrics
Every system ships with measurable quality targets—precision, recall, or task-specific baselines—so you know if it's working.
Human-in-the-loop
We define where humans review, override, or confirm AI output. Automation without an escape valve is a liability.
Observability & fallbacks
Logging, tracing, and defined fallback behavior so the system degrades gracefully and problems are diagnosed quickly.
What you get.
- Proof-of-concept or prototype with defined evaluation criteria
- Evaluation report: metrics, baselines, and quality targets
- System design and prompt architecture documentation
- Integration plan: APIs, data pipelines, and access controls
- Monitoring plan: alerting thresholds and fallback definitions
Want to talk it through?
Tell us what you're building and what stage you're in. We'll quickly tell you if we can help—and how.