AI-driven experiences that stay transparent, explainable, and trusted.
We design, prototype, and ship AI copilots, analytics, and automation with ethical guardrails and measurable impact.
-27%
Support tickets reduced
AI analytics startup deflected tickets with explainable copilots.
18
Evaluation harnesses
Prompt quality and safety monitored before and after launch.
5 days
Prototype turnaround
Mixed-fidelity tests validated prompts and UI before engineering.
Opaque reasoning
Users abandon AI features when outputs lack explanations or provenance.
Impact: Explanation frameworks increase perceived reliability and usage.
Missing guardrails
Without evaluation harnesses, hallucinations and unsafe actions slip into production.
Impact: Safety layers and fallbacks keep humans in control.
Broken feedback loops
No path to capture, triage, and learn from user feedback.
Impact: Feedback pipelines improve model quality and business metrics.
Capabilities for AI-first teams
Product design, engineering, and governance working together from day zero.
Experience design
Craft explainable and ethical AI interactions that feel trustworthy.
- Prompt patterns with rationale, citations, and confidence indicators.
- Human-in-the-loop workflows balancing speed with oversight.
- Evaluation of tone, bias, and safety before scaling.
Product engineering
Deliver reliable AI systems with instrumentation and fallbacks.
- Prompt orchestration, RAG pipelines, and tool execution layers.
- Evaluation harnesses measuring accuracy, toxicity, and drift.
- Observability dashboards, logging, and alerting for AI behaviours.
Venture advisory
Align business objectives with responsible AI practices.
- AI opportunity framing workshops and model selection support.
- Policy, compliance, and risk frameworks matched to your industry.
- Feedback governance and iteration rituals with stakeholders.
AI product engagement cadence
Six-week loop to discover, validate, and launch responsible AI experiences.
Phase 1
AI opportunity & risk framing
Align stakeholders on goals, guardrails, and data readiness.
Key outputs
- Opportunity scorecards with value vs. risk analysis.
- Data + prompt readiness audit with gap remediation plan.
- Responsible AI policy alignment and playbooks.
Phase 2
Experience design & prototyping
Prototype prompts, flows, and UI with mixed-fidelity testing.
Key outputs
- Prompt libraries with success criteria and evaluation metrics.
- Interactive prototypes covering success, failure, and escalation paths.
- User research readouts informing UX and governance decisions.
Phase 3
Implementation & evaluation
Build AI orchestration, guardrails, and instrumentation.
Key outputs
- Production-ready prompt orchestration and retrieval pipelines.
- Evaluation harness covering hallucination, bias, and latency KPIs.
- Monitoring dashboards with alerts and fallback playbooks.
Phase 4
Launch & learning loop
Deploy, monitor, and iterate with structured feedback.
Key outputs
- Launch checklist with human override protocols.
- Feedback ingestion tooling and triage rituals.
- Post-launch experiment plan for continuous improvement.
AI product accelerators
Operationalise responsible AI with ready-made systems.
Prompt architecture repository
Versioned prompts with context, input, and evaluation history.
Evaluation harness templates
Test suites for accuracy, bias, toxicity, and latency.
Responsible AI guidelines
Policy alignment covering privacy, explainability, and ethics.
Escalation & fallback playbooks
Decision trees for human override and safe shutdown.
Analytics instrumentation kit
Logging, tracing, and metric dashboards for AI performance.
Related AI launches
Shipping copilots, analytics, and automation with guardrails.
Ship AI your customers can trust.
Work with a squad that combines design, engineering, and responsible AI practice.