Now scheduling discovery engagements for Q4 2026
From forecasting models to LLM-powered assistants, we build AI features with evaluation, monitoring and guardrails — and tell you honestly when a simpler approach will do.
Typical results
Concrete outputs you can review, test and keep, whether we stay on or hand over.
Feasibility, data readiness and ROI for each AI opportunity.
Retrieval-augmented assistants, document processing and agents with evaluation suites.
Forecasting, classification and anomaly detection deployed as APIs.
Versioned models, drift monitoring and retraining pipelines.
Recommended by default, adapted to your existing stack.
Workshops with stakeholders and users, a review of existing systems, and a shared definition of success.
System design, technology choices, security model and a delivery plan with a fixed estimate for the first release.
Cross-functional squads ship working software every sprint, with a demo you can click through.
Automated tests, security scans and performance checks run on every change; UAT with your team before release.
CQ Fluency, Healthcare
An LLM-powered pipeline that classifies, extracts and routes healthcare documents for translation, with human review on every sensitive step.
No. We use enterprise endpoints with data retention disabled and can deploy models in your own cloud.
A senior engineer will reply within one business day with questions, a rough approach and next steps. No sales script.
