GenAI copilots, predictive analytics, automation, and vision/NLP—planned, piloted, and scaled with governance and measurable ROI.
MVP increments
Evals, safety, RAG
KPI-led delivery
GenAI copilots for agents, knowledge RAG, intent routing, quality automation.
Predictive scoring, outreach copilots, proposal drafting, win/loss insights.
Forecasting, anomaly detection, approvals automation, compliance checks.
Weekly drops with clear checkpoints; pilots typically 6–10 weeks
Readiness, data, security, and a category heatmap aligned to KPIs.
Architecture, guardrails, eval plan, and a pilot scope with owners and success metrics — planned in weekly increments.
GenAI/copilot, predictive, or automation pilot with RAG, safety, and analytics — shipped weekly so you see value fast.
MLOps/GenAI ops, cost/latency tuning, rollouts, training, and adoption playbooks.
We use AI tooling (Cursor) to accelerate build cycles while keeping quality and safety.
AI-assisted code generation for boilerplate and tests lets us drop value every week.
Cursor pair-programming plus CI/CD, linting, and automated checks keep output reliable.
Scaffold pilots, prompt/eval iterations, and UI variants quickly to validate with users.
Human review + policy guardrails ensure AI-generated code meets security and compliance standards.
RAG knowledge bases, prompt hardening, policy filters, telemetry, and feedback loops.
Feature stores, propensity models, anomaly detection, dashboards, and alerting.
Human-in-loop approvals, policy-aware flows, monitoring, and audit trails.
Document AI, classification, extraction, QA/inspection, with secure storage and access.
Lineage, PII handling, masking, RBAC, and secure connectors for private data.
Policy filters, structured outputs, eval harnesses, and red-teaming to keep AI safe.
Tracing, feedback capture, latency/cost dashboards, and rollout playbooks.
We score opportunities by impact, feasibility, data readiness, and risk, then pilot the highest ROI category first (e.g., GenAI chat vs. predictive vs. automation).
Policy filters, structured outputs, eval harnesses, red-teaming, PII controls, and audit trails are included from day one—aligned to your governance needs.
Blueprint, build, guardrails, telemetry, and success metrics for one category. We measure ROI, then decide how to scale or tune.
Yes. We integrate securely with your data sources, enforce least-privilege access, and adapt to your stack (cloud, analytics, CRM, ticketing, etc.).
We implement CI/CD, monitoring, evals, cost/latency tuning, and playbooks so AI stays reliable after launch.
Pick a category—GenAI copilot, predictive model, or automation—and we’ll deliver a guarded pilot with clear KPIs in 6–12 weeks.
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