Service
The data layer underneath your AI — retrieval, grounding, lineage, and the pipelines that make any of it trustworthy.
Frequently Asked
Not always. For corpora under a few million chunks with stable retrieval patterns, Postgres with pgvector or even hybrid keyword + rerank can outperform a dedicated vector DB on cost, ops complexity, and latency. We size the storage layer to the workload, not the trend.
Fine-tuning teaches a model new behaviours or style by adjusting weights; it does not reliably teach it new facts. RAG injects fresh, source-attributed facts at inference time. Most enterprise problems are fact problems, so most enterprise solutions are RAG problems — sometimes with light fine-tuning on top.
Acknowledged from day one. Chunking strategy, metadata extraction, deduplication, access control, and freshness pipelines all matter more than the embedding model. We spend more time on ingestion than retrieval — that's where production quality is decided.
Yes — and we usually should. The vector layer is best treated as a materialised view over the lakehouse or warehouse, not a parallel system. We keep lineage and access control authoritative in your existing data plane.
Streaming ingestion with eventual-consistency guarantees on the vector layer, plus a 'recency-aware' retrieval pattern that knows the difference between a contract from 2019 and a board memo from this morning.
Retrieval to Audit
What we build
We build the data layer that makes enterprise AI actually work — retrieval pipelines that find the right chunk, grounding strategies that survive citation review, lineage that holds up in an audit, and the unglamorous ETL underneath all of it.
Most AI projects fail at the data layer, not the model. We start there.
How we approach it
- Source audit. What corpora exist, who owns them, what are the access controls, how fresh are they, how messy. The audit is the project’s first deliverable — most teams discover their actual data estate for the first time during it.
- Ingestion design. Chunking, metadata extraction, deduplication, language handling, OCR for scans, table-aware parsing for documents that mix prose and structured data. This is 60% of the engineering work.
- Retrieval strategy. Hybrid sparse + dense, reranking, query expansion, metadata filtering, recency weighting. Tuned per corpus, evaluated continuously.
- Grounding and citation. Outputs link back to source chunks with character-level offsets. If the model cites it, you can show the user where it came from.
- Lineage and governance. Every retrieved document carries its access-control context. The system refuses to ground a response on data the user is not entitled to see.
- Eval and drift. Retrieval evals (recall@k, MRR, faithfulness), grounding evals (citation precision, hallucination rate), drift monitors on corpus and query distributions.
Capabilities
- Document ingestion at scale — PDF, Office, HTML, email, ticketing systems, code, scans.
- Multi-language ingestion (English, Hindi, vernacular Indic; document AI for mixed scripts).
- Knowledge graphs for entity-aware retrieval.
- Vector storage: pgvector, Qdrant, Weaviate, Pinecone, OpenSearch — picked by workload.
- Embedding model selection and (where it earns it) fine-tuning.
- Reranking with cross-encoders, including domain-tuned rerankers.
- Streaming pipelines for real-time corpora.
- Access-controlled retrieval with row- and chunk-level policy enforcement.
- Evaluation harnesses: retrieval, grounding, end-to-end answer quality.
Reference architectures we ship
- Document intelligence platform. Ingestion → enrichment → retrieval → answer-with-citation. Used for contract analysis, claim adjudication, regulatory filings, technical knowledge bases.
- Conversational knowledge surface. Domain-tuned RAG behind a copilot, with strict access control and a feedback loop into the corpus.
- Agent-ready knowledge layer. Retrieval exposed as a tool to agents, with rate limits, audit trails, and structured citation contracts.
Why Eldridge Morgan
We treat the data layer as the product, not the supporting cast. We’ve seen enough enterprise AI projects fail at ingestion to know that the embedding model is the last thing you should be arguing about.
Sectors Served
- Energy (Oil & Gas)
- Financial Services
- Private Equity
- Logistics & Transportation
- Industrials & Manufacturing
- Healthcare
- Consumer & Retail
- Technology & Telecom
- Public Sector
Related Reading
- DPDP Act and Generative AI: What Indian Enterprises Must Implement
- When BM25 Beats Your Embedding Model: Hybrid Retrieval in the Wild