// RAG Implementation
RAG pipelines your legal team signs off on.
Retrieval-augmented generation built for accuracy, permissions, and auditability. Grounded citations, document-level ACLs, and evaluation harnesses that catch regressions before users do.
// Who this is for
Built for teams who are past the experiment phase.
CTOs shipping internal knowledge assistants or customer-facing search that must cite sources and respect permissions.
APAC enterprise leads in BFSI, healthcare, and legal where hallucinations and data leaks are existential, not annoying.
AI-native founders building vertical copilots who need retrieval quality to be a moat, not a bottleneck.
// What we deliver
The scope, in plain language.
Every engagement is scoped against your business outcome, not a fixed menu. What you see below is the typical shape — we tighten it with you in the first week.
- Document ingestion pipeline: parsing, chunking strategy, metadata extraction, and incremental re-indexing for evolving corpora.
- Embedding and retrieval stack selection — dense, sparse, hybrid, or reranked — benchmarked against your actual queries.
- Vector database setup and tuning (pgvector, Qdrant, Weaviate, OpenSearch, or managed) with backup and DR.
- Document-level access control: permissions enforced at retrieval, not papered over in the prompt.
- Grounded answer generation with inline citations, confidence signals, and refusal behavior on low-recall queries.
- Evaluation harness covering retrieval precision/recall, answer faithfulness, and citation accuracy — run on every change.
- Admin tooling: corpus health dashboards, query analytics, and feedback loops for continuous improvement.
// How we work
The Ankor 7-stage framework, applied to rag implementation.
- 01Discover
Align on business outcome, constraints, and success metric.
- 02Define
Pin down scope, architecture, and the evaluation bar.
- 03Design
Model, data, and UX design — with trade-offs on the table.
- 04Data
Audit, remediate, and pipe the data the build actually needs.
- 05Develop
Ship the system in small, testable increments against the eval bar.
- 06Deploy
Rollout with shadow mode, guardrails, and rollback.
- 07Drive
Operate, measure, and iterate — handoff or retainer.
// Outcomes you can expect
Ranges, not guarantees. Specific, not boastful.
Answers grounded in retrieved context with traceable citations, measured on your evaluation set.
From kickoff to a production RAG service with your corpus, your permissions, and your users.
End-to-end query latency target for typical corpora — tuned per deployment.
// Why Ankor
A decade of shipping software, repointed at production AI.
- 10
- years shipping software
- 190+
- clients delivered
- 260+
- products shipped
- 800K+
- daily users served
Serving clients across APAC, the US, and EMEA.
RAG is mostly not about the LLM
Nine out of ten RAG problems are retrieval problems. Chunking strategy, embedding choice, metadata filtering, reranking, corpus hygiene. We spend the first half of an engagement on those, because the best model in the world cannot answer questions grounded in documents it never sees.
The second half is where legal and security get real: document-level permissions enforced at query time, data residency, redaction of sensitive fields, and an audit trail of every answer with its source. This is the bar regulated industries actually need.
The stack we reach for
Embedding models: a mix of OpenAI, Cohere, and open-weight (BGE, E5) depending on residency constraints. Vector DBs: pgvector when the corpus fits, Qdrant or Weaviate when it does not, OpenSearch when you already run it. Rerankers: Cohere Rerank or open-weight cross-encoders. Orchestration: LlamaIndex, LangChain, or a tight hand-rolled pipeline — picked for maintainability, not novelty.
Your stack gets documented. Your team gets trained. Your lawyers get an architecture diagram they can read.
// FAQ
Questions we get a lot.
Why not just use a commercial 'chat with your docs' product?
For simple single-tenant internal use, sometimes you should — we will say so. Custom builds earn their cost when you need document-level ACLs, regulated data residency, domain-specific retrieval tuning, or evaluation rigor that off-the-shelf tools cannot provide.
How do you prevent hallucinations?
Four layers: strict grounded-generation prompting with citation requirements, a reranker that filters low-relevance context, a faithfulness check in the evaluation harness that flags un-cited claims, and explicit refusal behavior when retrieval confidence is low. We measure and report faithfulness — not assume it.
Can RAG respect who is allowed to see which documents?
Yes, and this is where most naive RAG builds break. We enforce ACLs at the retrieval layer — only documents the user is entitled to see ever enter the context window. We do not rely on the model to 'remember' permissions.
What does the evaluation harness actually measure?
Retrieval metrics (hit rate, MRR, recall@k against a labeled query set), generation metrics (faithfulness, answer relevance, citation accuracy), and task-level metrics you define (e.g., 'did it find the right policy clause'). It runs in CI so model or prompt changes cannot silently regress quality.
Do you support on-prem or private-cloud deployment?
Yes. We regularly ship RAG stacks on client infrastructure with open-weight embedding models and self-hosted vector DBs — full data sovereignty with no external API calls. See private LLM deployment for the broader pattern.
// Ready to ship?
Let's talk about what to build first.
Short call. No deck. We will tell you honestly whether we are the right team for your problem.
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