AI & Data
Agentic RAG
development
Classic RAG retrieves once and hopes. Agentic RAG lets the model plan its retrieval, query multiple sources, judge what it found and search again until the answer is grounded. We build these systems so your AI answers from your knowledge, with citations.
- Type
- Retrieval AI
- Pattern
- Retrieve · Reason · Verify
- Grounding
- Cited sources
- Index
- Hybrid + rerank
- Best for
- Knowledge assistants
In short
Agentic RAG, at a glance
- Responses are grounded in your documents and cite their sources, not the model's memory.
- The agent decomposes hard questions and searches multiple ways before answering.
- Self-critique and source verification catch ungrounded claims before they reach the user.
- If the first results are weak, the agent reformulates and searches again.
What we build with Agentic RAG.
Most RAG failures come from a single, naive lookup feeding the model irrelevant chunks. Agentic RAG treats retrieval as a reasoning task: the agent decomposes the question, searches several ways, reranks and critiques results, and retrieves again before answering.
We engineer the full pipeline — ingestion, chunking, hybrid search, reranking and grounded generation — with evaluations that measure faithfulness and relevance, so the system earns trust on your actual documents.
Knowledge assistants
Chat over your docs, wikis and tickets with grounded, cited answers.
Hybrid retrieval
Vector + keyword search with reranking for precision on real corpora.
Query planning
Agents that decompose, route and re-search until the answer is solid.
Faithfulness evals
Measured grounding and relevance so hallucinations get caught in CI.
The case for Agentic RAG.
Answers from your data
Responses are grounded in your documents and cite their sources, not the model's memory.
Plans its retrieval
The agent decomposes hard questions and searches multiple ways before answering.
Fewer hallucinations
Self-critique and source verification catch ungrounded claims before they reach the user.
Re-retrieves on doubt
If the first results are weak, the agent reformulates and searches again.
Hybrid + rerank
Vector and keyword search with a reranker beats naive similarity on messy corpora.
Any source
PDFs, wikis, databases and APIs unified behind one retrieval layer.
How we engineer
with Agentic RAG.
RAG system design
Architect the full pipeline for your corpus: ingestion, chunking, indexing and grounded generation.
- Chunking & metadata strategy
- Hybrid index design
- Citation & grounding
The stack we pair
with Agentic RAG.
Vector & search
Frameworks
Models
Quality
Outcomes, not just output.
A six-step cycle, repeated until it's right.
Transparent, predictable and collaborative. You always know what's shipping next and why.
Discovery
We map the business, users and constraints, then pressure-test the problem before a line of code.
Planning
Architecture, scope, and a sprint roadmap with clear milestones, budgets and success metrics.
Design
Research-led UX and high-fidelity interfaces, validated with prototypes before build.
Development
Senior-led engineering in two-week sprints with demoable increments and continuous review.
Testing & QA
Automated and manual testing, security review and performance hardening before release.
Launch & Care
Confident deployment, monitoring and SLA-backed support that keeps things humming.
Agentic RAG questions, answered.
Still unsure if Agentic RAG is right for your project? A senior engineer will tell you straight on a free call.
Standard RAG does one retrieval and stuffs the results into the prompt. Agentic RAG lets the model plan: it breaks the question down, searches multiple sources, judges whether the results are good enough, and re-searches if not. That dramatically improves answers on hard, multi-part questions.
It greatly reduces it. Answers are grounded in retrieved sources and cited, and we add self-critique and faithfulness checks that flag claims not supported by the documents. We measure this with evals rather than assuming it.
PDFs, Office docs, wikis, knowledge bases, databases and APIs. We build ingestion pipelines that parse, chunk and index them, respect access controls, and keep the index fresh as content changes.
With an evaluation harness using golden question sets and metrics for faithfulness, context relevance and answer relevance (e.g. Ragas). These run in CI so quality can't quietly regress as you change prompts or data.
Yes. We make retrieval access-control aware so each user only ever sees answers grounded in documents they're allowed to read, which is essential for internal knowledge assistants.
Considering an alternative stack?
Ready to build with Agentic RAG?
Book a free 30-minute consultation. We'll pressure-test your idea and map a Agentic RAG approach, whether or not we end up working together.
What happens after you hit send.
You book in 60 seconds
Share a few details below. No lengthy forms, no sales gatekeeping.
A 30-minute strategy call
You talk to a senior engineer about your actual problem, not an account manager.
A clear path forward
You leave with concrete recommendations and a rough scope, whether or not we work together.
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