Service
RAG integration for existing products
Retrieval pipelines over your databases, docs, and APIs — scoped per tenant, deployed in your repo, and designed to fail gracefully when context is missing.
Who this is for
B2B SaaS teams with a large or changing knowledge corpus — support docs, internal wikis, product records, or customer data — where users need synthesized answers, not keyword search alone.
Problems we solve
Common failure modes when copilot, retrieval, or middleware features are bolted on without an integration plan.
- Demo RAG wired to a public docs folder with no tenant isolation or ACL-aware retrieval
- Embedding and chunking chosen for the POC, not for your data shape, update frequency, or query patterns
- Vector store bolted on beside your app instead of behind the same auth and API boundaries as the rest of the product
Typical deliverables
- Retrieval architecture — chunking, embedding model selection, and index strategy matched to your data and refresh cadence
- Server-side retrieval layer with RBAC — users only embed and retrieve documents they are permitted to see
- Grounded generation path through your LLM middleware — citations, fallbacks when retrieval confidence is low, and observability on hit rate
- Rollout plan behind feature flags — internal users first, then tenant canaries, with eval baselines before GA
How we deliver
Your eng team stays on the roadmap. We handle the AI integration layer — scoped sprints, PRs to your repo, and handoff docs so your team can operate what we ship.
We start with a technical audit of your data sources, auth model, and the specific user workflows that need retrieval — not a generic vector database install. A working prototype validates retrieval quality against real queries before full build commitment. Code lands in your repository with runbooks so your team can operate chunking, re-indexing, and prompt changes after handoff.
Step 1
Technical audit
Map your architecture, API boundaries, data flows, and auth model. Identify the lowest-risk, highest-value integration point.
Step 2
Architecture & prototype
API contracts, middleware design, and a working proof against your real stack — validated before full build commitment.
Step 3
Build & deploy
Production code in your repo. Staging, load testing, and canary rollout behind feature flags — with runbooks for your team.
Step 4
Operate & expand
Monitor latency, cost, and output quality. Iterate on evals and prompts, then extend to the next workflow boundary.
Related guides
Deeper technical notes from our resources library.
RAG without the platform rewrite
How to add retrieval over your existing data without standing up a separate vector platform or pausing the product roadmap.
May 28, 2026
When not to use RAG
RAG is the default answer for every AI feature — but often the wrong one. A decision guide for engineering leaders scoping retrieval, tools, and middleware.
June 6, 2026
LLM middleware: what it is, why you need it, and how to implement it
A practical guide to the server-side layer between your app and the model — auth, rate limits, routing, logging, and the patterns that keep AI features production-ready.
June 7, 2026
Common questions
- Do we need to migrate to a new platform to add RAG?
- No. We integrate retrieval behind your existing APIs and deploy through your current CI/CD. Your databases, identity provider, and frontend stay in place — we add the retrieval and generation layer as a service boundary inside your stack.
- When is RAG the wrong approach?
- When the data is already in the request, the answer is a deterministic lookup, or you need live system state instead of documents. We assess that before recommending retrieval — see our guide on when not to use RAG for the decision framework.
- How long does a first RAG feature typically take to ship?
- An audit and architecture proposal usually takes one to two weeks. A first production retrieval feature often ships in four to eight weeks depending on data readiness, ACL complexity, and review cycles — broken into incremental milestones behind feature flags.
Scope an integration for your stack
Describe the feature you are planning — we will map architecture, effort, rollout strategy, and what production-ready means for your system.
Get an integration plan