RAG development company — grounded LLM answers, on your data.

Frequently asked questions

What does a RAG application development company do?

A RAG application development company builds the full stack between "we have private data" and "our users get accurate, sourced AI answers from it". That includes data ingestion and chunking, embedding pipeline, vector database setup, hybrid retrieval with keyword + semantic + metadata filters, LLM orchestration, source attribution, guardrails, evaluation test sets, and production monitoring. We do not deliver a notebook or a demo — we ship a system that operates in production and improves over time.

What is RAG?

RAG (retrieval-augmented generation) is a pattern where an LLM answers a user question by first retrieving relevant context from your own data (docs, database, knowledge base), then generating a grounded answer with source citations — instead of relying purely on the LLM's training data. It is the dominant pattern for "AI on private data" because it is faster, cheaper, and safer than fine-tuning.

RAG vs fine-tuning — which should I use?

RAG first, almost always. Fine-tuning is great for style, tone, or structured-output consistency. RAG is better for "answer from current information" — which is what 90% of business AI projects actually need. Fine-tuning on facts is a trap: it bakes stale data into a model that is expensive to re-train.

How much does a production RAG project cost?

Simple internal RAG (1 data source, 1 interface): $5,000–$15,000. Production customer-facing RAG (multiple sources, eval, monitoring, guardrails): $15,000–$50,000. Enterprise RAG with strict compliance (audit logs, PII redaction, role-based retrieval): from $50,000. Upwork escrow on everything.

Can you host it or does it have to be in our cloud?

Your choice. We can deploy to your AWS/GCP/Azure account (most common for enterprise), or we can run it on our infrastructure with a data-processing agreement. For regulated industries we recommend in-your-cloud deployment with a VPC and private endpoints.

How do you prevent hallucinations?

Grounded prompting (the LLM is explicitly told to only answer from provided context and to say "I do not know" when context is thin), source attribution (every answer includes the source chunks so users can verify), re-ranking to surface the most relevant context, and continuous evaluation to catch drift. We test this on real queries before shipping, not just demos.

How do I know if a RAG application development company actually knows what they are doing?

Ask three questions. First: name one production RAG system you have shipped — real user traffic, not a demo. Second: what evaluation metrics do you track (recall@k, MRR, faithfulness, answer relevance) and at what point in the engagement do you write the test set? Third: explain your chunking strategy for a PDF contract versus a Slack export. A company that answers all three specifically has shipped RAG in production. A company that pivots to "we use LangChain and Pinecone" has built demos. We are the former — ask us the same three questions.

Can I hire your RAG application development company for a small paid pilot before a full engagement?

Yes — most sensible first projects with any RAG application development company should be a fixed-scope paid pilot, not a six-month contract. Our standard pilot is 3 weeks, $6,500, on Upwork escrow with milestone payment per week. Scope: one data source, one channel (usually a Slack bot for your own team first — not customer-facing), 100 labelled evaluation questions, and a written go/no-go report at the end. If the report says the retrieval is not yet good enough, the project ends there — you leave with the labelled test set, an honest read on your data quality, and no pressure to continue. Most pilots do continue into a production build, but a clean "not yet" is also a valid deliverable.

What should I ask on a discovery call with a RAG application development company?

Six questions that filter the demo-only shops from the ones who have shipped. One: "Show me a production RAG system you built and the retrieval metric it holds — recall@k, MRR, or faithfulness on a labelled set." A real RAG application development company will name the metric and the number, not point at a screenshot. Two: "When in the engagement do you write the evaluation set — Week 1 or after the pipeline is built?" The correct answer is Week 1, from real user queries. Three: "How would you chunk a 200-page PDF contract versus a Slack export versus our product database?" One-size chunking is a red flag. Four: "Which vector database would you pick for our data volume, and why not the other three?" Vague "we use Pinecone" is not an answer. Five: "How do you handle the case where the top-k chunks miss the right context — does the LLM guess, or refuse?" Refuse. Six: "Who owns the deployed system after handover — you, us, or a joint runbook?" We answer all six specifically on the first call; if a shortlist candidate cannot, they have not shipped production RAG.

What is the difference between a RAG development company and a RAG application development company?

It comes down to what gets delivered. A RAG development company can hand you a notebook, a Streamlit page, or a scripted retrieval that works on a fixed test set — a demo. A RAG application development company delivers an application: a running service with auth, rate limits, structured observability, a monitored evaluation loop, a UI or API your team ships into, and a runbook. The engineering, testing, and hand-off work between those two artifacts is roughly the same as the gap between a prototype and a shipped SaaS product. Teamz Lab quotes the second by default; the first is only sensible for internal research spikes.

What information should I prepare before contacting a RAG application development company?

Seven things get every scoping call to a fixed fee in one session instead of three. One: total source content in GB plus rough document count split by type (PDF, HTML, Slack / ticket exports, database rows, code). Two: update cadence — hourly, daily, weekly, or static. Three: access control model — single corpus, per-team, per-tenant, or role-based. Four: latency budget per query (2 seconds is a different project than 200ms). Five: 30 real user questions copy-pasted from Slack / Zendesk — the seed for the Week 1 eval set. Six: compliance context (SOC 2 / HIPAA / GDPR / none) — it changes the deployment target and roughly doubles the timeline when it applies. Seven: your existing stack (auth, cloud, CRM, helpdesk) so proposed infrastructure aligns with what you already run. Teams with this list get a fixed-fee written scope within 48 hours; teams without it spend Week 1 of the engagement gathering it anyway.