The roadblocks standing between enterprise AI and real ROI. Turning AI momentum into sustainable enterprise value requires more than spinning up models. Businesses often hit production walls without the infrastructure to support live workloads, while large enterprises end up with costly, disjointed AI initiatives scattered across business units. Talk to us
The enterprises winning with agents did two things: they built on governed data, and they wrote the governance before the regulator asked for it. Talk to us
Enterprise Knowledge Agents Workforce-facing agents answering from governed internal content, policies, procedures, and documentation, with source citations and access control enforced at retrieval time. Talk to us
RAG Pipeline Engineering Chunking, embedding, and vector indexing engineered as production pipelines with continuous refresh, on Databricks Vector Search, pgvector, and platform-native stores. Talk to us
Grounding and Evaluation Faithfulness and relevance evaluation built into the pipeline, so hallucination is measured and managed, not discovered by the user who acted on a wrong answer. Talk to us
Customer-Facing Retrieval Agents Support and self-service agents grounded in product and account data, with escalation paths engineered for the questions the agent should not answer alone. Talk to us
Process Automation Agents Claims intake, order processing, onboarding, reconciliation, and other multi-step workflows executed end to end, with human review gates on exceptions and high-stakes steps. Talk to us
MCP and System Integration Model Context Protocol servers and API integrations connecting agents to SAP, Salesforce, ServiceNow, ticketing, and custom systems through governed, reusable connections. Talk to us
Multi-Agent Orchestration Planner, worker, and reviewer agent patterns for complex workflows, with state management, retries, and escalation engineered for production reliability. Talk to us
Human-in-the-Loop Design Approval boundaries, confidence thresholds, and review queues designed so people control what matters and agents handle what repeats. Talk to us
Automated Schema Mapping LLM-inferred source-to-target mappings for migrations and integrations, with confidence scoring and engineer review, compressing the longest manual phase of every data project. Talk to us
Metadata and Catalog Enrichment Column descriptions, table documentation, and business glossary entries drafted by LLMs from schema, sample data, and usage patterns, then curated into Unity Catalog, Purview, or your catalog of record. Talk to us
Business Logic Extraction Business rules and transformation logic extracted from legacy stored procedures, ETL jobs, and application code, documented before the knowledge retires with the engineer who wrote it. Talk to us
Sensitive Data Classification PII, PHI, and regulated data identified and tagged across the estate with LLM-assisted classification, feeding access policies and compliance evidence. Talk to us
AI Readiness Assessment A structured evaluation of your data foundation, integration surfaces, security posture, team skills, and use-case portfolio, delivered as a written diagnostic with a prioritized shortlist, not a maturity scorecard that gathers dust. Talk to us
Architecture and Platform Decisions Model selection, build-versus-buy, single-vendor versus multi-model, cloud placement, and agent framework choices, grounded in your workloads and negotiating leverage, not in a vendor's roadmap. Talk to us
Governance and Risk Framework Risk classification aligned to the EU AI Act and sector regulation, human-oversight boundaries, audit trails, model documentation, and the approval workflow that lets new agents ship quickly and defensibly. Talk to us
Phased Implementation Roadmap A sequenced roadmap from first production agent through scaled portfolio, with ROI targets per phase, platform investments timed to need, and the case the CFO can take to the board. Talk to us
Evaluation and Monitoring Continuous quality evaluation, response tracing, and drift detection, so degradation is caught by the dashboard, not by the user who acted on a wrong answer. Talk to us
Cost and Token Accountability Per-agent, per-workflow cost instrumentation, LLM calls, tokens, and retrieval queries attributed to the feature driving them, connected to Sundew's FinOps discipline. Talk to us
Safety and Guardrails Prompt injection defense, PII redaction, content filtering, and output validation engineered into the serving layer, tested continuously rather than assumed. Talk to us
Lifecycle and Change Management Versioned prompts, model upgrade testing, rollback procedures, and documented change control, so an agent update is a managed release, not a quiet regression. Talk to us
Proven in production, grounded in data. Sundew's agentic AI practice is built on production delivery and twenty years of enterprise data experience, on the platforms and standards defining the agent era. 5,000+ Monthly active users on Sundew-engineered enterprise agents 20 yrs Of enterprise data experience grounding every agent 04 Strategy pillars: readiness, architecture, governance, roadmap
Ready to Turn AI Vision into a Board-Funded Roadmap? Skip the hype and consult directly with data engineers who build production-grade agents. We'll evaluate your data readiness, establish strict risk governance, and chart a phased path to scalable execution. Talk to us