Time-boxed pushes to hit KPIs—with reversible rollouts and measurable acceptance criteria.
Agentic AI Systems
Multi-agent automations with guardrails and HITL review.
•Eliminate manual back-office steps
•Policy-aligned actions
•Explainable decisions
What you get: Orchestration graph in code (agents, tools, policies) with retries/timeouts · Evals suite: jailbreak, toxicity, groundedness, accuracy gates · Guardrails and safety gates enforced in CI and runtime · Reviewer console (approve/annotate/retry) with audit trail · Run log and trace viewer (inputs, prompts/versions, tool calls, outputs) · Budget caps and alerts; cost per transaction export
GenAI Product Accelerator
RAG features shipped to production safely.
•MVP in weeks
•Measurable accuracy
•Usage analytics & observability
What you get: Vector pipeline + knowledge ingestion (automated re-indexing) · RAG orchestration layer with prompt versioning and fallbacks · Evals suite: accuracy (exact-match + semantic), hallucination gates, toxicity filters · CI/CD integration with regression gates (accuracy thresholds) · Observability dashboard: usage, cost per query, latency p95 · Safety monitoring: PII detection, content filters, rate limits
Computer Vision FastTrack
PoC→prod pipeline on edge/cloud.
•Detect/track reliably
•Low latency at the edge
•Ops dashboards that stick
What you get: Custom model trained on your footage (detection/tracking/classification) · Edge deployment package: ONNX/TensorRT optimized for Jetson/x86/ARM · Inference pipeline with <target latency (typically 60-200ms p95) · Precision/recall benchmarks on test set with confusion matrices · MLOps workflow: drift monitoring, review UI, re-labeling, retraining hooks · Production runbook: deployment, rollback, troubleshooting, scaling
Data & Analytics Platform
GIS + KPI dashboards with a data-quality spine.
•Faster ops insight
•DQ pipeline with alerts
•Narrative reporting with anomalies
What you get: Data connectors with retry logic and monitoring (source → warehouse) · KPI catalog with definitions, owners, refresh schedules, and SLAs · Data quality pipeline: profiling, validation rules, alerts on critical failures · GIS-enabled dashboards with zoom, filter, layer controls, and export · Narrative report generator with automated summaries and anomaly detection · Runbook: troubleshooting, scaling, adding KPIs, data refresh procedures
MLOps & Model Operations
Registry, monitoring/drift, and model governance.
•Traceable models
•Drift alerts
•Fast rollback
What you get: Model registry with versioning, lineage tracking, and metadata tagging (MLflow/W&B/custom) · Drift monitoring dashboard with statistical tests (KL divergence, PSI, data quality) · Automated rollback workflow with last-known-good fallback and rollback criteria · Evaluation harness with precision/recall/F1 benchmarks and confusion matrices · CI/CD integration for model deployment with GitHub Actions/GitLab CI pipelines · MLOps runbook with troubleshooting, scaling guidelines, and cost guardrails
