gopherlabs.ai
Services

Production-grade data engineering for AI

Six engagements that compose into a complete program. Start narrow, scale wide.

Data Assessment

A two-week diagnostic across your sources, schemas, and current retrieval quality. We hand back a prioritized roadmap your team can execute — with or without us.

  • Source inventory and access map
  • Retrieval quality baseline with evals
  • Prioritized roadmap and effort estimates

Structured Cleanup

Warehouse-grade hygiene for tabular sources. Entity resolution across CRMs, ERPs, and product data with full lineage and reversibility.

  • Deduplication and entity resolution
  • Schema normalization and validation
  • Column-level lineage and audit trails

Unstructured Processing

Contracts, PDFs, transcripts, tickets, wikis — parsed, chunked, and enriched with the metadata your retriever actually needs.

  • Layout-aware document parsing
  • Domain-tuned chunking strategies
  • Metadata and ontology enrichment

RAG Pipeline Engineering

End-to-end retrieval pipelines: hybrid search, reranking, query rewriting, and grounded generation. Versioned, observable, and evaluated.

  • Hybrid vector + lexical retrieval
  • Reranking and query understanding
  • Continuous evals and regression gates

Governance & Compliance

PII redaction, row- and chunk-level ACLs, audit logging, and data residency controls — engineered in, not bolted on.

  • PII detection and redaction at ingestion
  • Per-tenant ACLs to the chunk level
  • Audit logs and lineage for every retrieval

Managed LLM Ops

We stay on the pager. Monitoring, drift detection, model swaps, scheduled re-indexing, and incident response for your AI workloads.

  • 24/7 monitoring and on-call rotation
  • Drift detection and auto re-indexing
  • Quarterly model and prompt reviews

Not sure where to start?

Most engagements begin with a two-week assessment. Tell us your stack and we'll send back a tailored proposal within 48 hours.