Enterprise Data Orchestration
Build the data foundation that lets AI systems reason over ledgers, ERPs, SaaS databases, documents, and operational events without leaking sensitive context.
Most failed AI initiatives are not model failures. They are data architecture failures: stale exports, weak chunking, missing permissions, and retrieval layers that cannot explain what context was used.
ViaCatalyst designs secure ingestion, indexing, retrieval, and context assembly pipelines so high-growth B2B platforms can move from demos to auditable production AI.
Outcomes
Core capabilities
We normalize fragmented internal data sources into controlled pipelines ready for retrieval and agent workflows.
We build retrieval layers that combine semantic similarity with keyword recall, metadata filters, and permission-aware access.
We turn raw retrieval results into model-ready context windows that are measurable, traceable, and grounded.
Process
Technology
Operational impact
Designed an ingestion and projection pipeline for finance teams working across ledgers, historical quarter data, and planning assumptions.
Converted brittle spreadsheet handoffs into a governed data flow ready for automated balance sheet projection, review, and exception handling.Related case patterns
Automating projection workflows across ledgers, historical quarter data, assumptions, validation checks, and review queues.
Adding an AI capability layer to a live B2B SaaS product without creating a brittle prompt wrapper or exposing tenant data.
Assessing whether a manual internal workflow is ready for autonomous agents, deterministic gates, and production observability.
Yes. We design around your current identity, network, data, CI/CD, and approval boundaries, then recommend only the changes needed to make the AI system production-ready.
We can use commercial LLM APIs, private endpoints, or self-hosted models depending on risk profile. Proprietary data is isolated from public training paths and access is designed around least privilege.
Yes. Most engagements begin with a paid architecture audit or two-week discovery sprint that produces a capability map, risk register, and implementation roadmap.
Architecture inquiry
Share your source systems, data sensitivity, and target workflows so we can map the retrieval architecture and risks before build begins.
Next step
Book a focused architecture audit and we will map the data, agent, evaluation, and security work required for a reliable first release.
