Every successful project begins with a clear AI Development roadmap. As your AI development services partner, we define success metrics and select the right models, data sources, and tech stack. Whether you need a custom model, an AI agent, or an app around it, we agree on API contracts and acceptance criteria so the build meets your specific business goals.
RAG Development Services
AI for Knowledge: RAG Development Services
Krazimo's RAG development services turn your company's documents and knowledge into a trustworthy AI assistant — grounded in your own data, governed by your access rules, by ex-Google engineers. Every answer cites a real source.
Krazimo's RAG development services turn your company's documents and knowledge into a trustworthy AI assistant — grounded in your own data, governed by your access rules, by ex-Google engineers. Every answer cites a real source.
overview
Every answer cites a real source and respects who's allowed to see what, so your team trusts it enough to actually use it.
RAG development services for company knowledge
Your company already knows the answer to most questions — it’s just buried in documents, wikis, tickets, and people’s heads. AI for Knowledge turns that scattered knowledge into a trustworthy assistant your team can actually ask. Krazimo’s RAG development services build it, grounded in your approved data and governed by your access rules, by ex-Google engineers.
What “AI for Knowledge” means
It’s an AI layer over your institutional knowledge: ask a question in plain language, get an answer drawn from real internal sources, with citations you can verify. No hallucinated policies, no answers from data someone shouldn’t see.
RAG development services
Retrieval-augmented generation is the engine. Our RAG development services cover the whole pipeline — ingesting and chunking your sources, embeddings and retrieval quality, the generation layer, and the access controls that enforce who can see what. We tune retrieval on your real questions, because a RAG system is only as good as what it retrieves.
Grounding & accuracy
Accuracy is the entire point. We measure answer quality against a real question set, cite sources on every answer, and add guardrails against hallucination and data leakage — so people trust it enough to actually use it. This is the same evaluation-first stance behind our LLM development services.
Use cases: support, internal search, ops
Customer support deflection, employee self-service and onboarding, internal search across docs and wikis, and ops teams answering policy and procedure questions are where knowledge AI pays off first. It often grows out of a broader AI software development engagement. Ready to unlock your knowledge? Book a demo.
Knowledge AI we’ve shipped
- Case Logic (legal AI) — a secure, state-aware legal assistant grounded in dense, jurisdiction-specific documents, built so a confident hallucination can’t create legal exposure.
- Blockchain Q&A — natural-language access to raw on-chain data that normally takes deep technical tooling to read.
- Chip Inc research assistant — turning scattered research material and heavy computation into something a researcher can simply ask, with project memory across the work.
How a knowledge AI build works
We ingest and structure your sources, tune retrieval on your real questions, add citations and access controls, and measure answer quality against a real question set before launch — then monitor it. Grounding and access control are the product, not features bolted on later.
Where knowledge AI pays off
Customer support deflection, employee self-service and onboarding, internal search across documents and wikis, and regulated domains where every answer must trace to a source. It often grows out of a broader AI software engagement.
how we work
01
Scope, Plan & Stack Selection
02
Architecture and Database Design
Our senior engineers design modular services and robust data and model pipelines from day one. We prioritize security, evaluation, and performance budgets across the AI software development process, and bake observability into every system—so each AI solution we build is maintainable, measurable, and transparent.
03
AI Software Development & Engineering
We use AI assistants to handle boilerplate and test generation, freeing our engineers to focus on the hard parts—model quality, guardrails, and critical paths. This hybrid approach speeds delivery without sacrificing the code-review standards expected of a top-tier engineering team, covering custom AI software development for complex concurrency and data challenges.
04
System Integration & Migration
We connect your new AI system to existing identity, billing, and analytics platforms through clean adapters. Our build process includes repeatable CI/CD pipelines, evaluation suites, and feature flags so integration happens without disrupting your current business operations—a “no-rip-and-replace” philosophy.
05
Harden, Launch & Cloud Services
Before launch, we run load tests, red-teaming, and security scans so your AI development project is production-ready. We provide full documentation so your team can extend the system, and post-launch we offer ongoing AI software development services to iterate in low-risk slices as your needs evolve.
Case studies
AI CRM
Custom AI CRM
How Our AI CRM Gets People Their Botox
Emer Med unifies every patient touchpoint into a single operating layer, enabling faster responses, cleaner follow-ups, and a premium experience at scale.
AI Call Center
Voice Bots
Let the Phones Run Themselves!
BlinkVoice deploys voice agents that answer calls and complete real workflows, so routine requests are handled instantly and staff are reserved for the moments that matter.
AI Sales
AI Sales Training
Gamifying Sales Training
Sales training breaks when practice is rare and coaching doesn’t scale. PitchMee turns real calls and simulations into measurable practice with fast feedback.
Financial Advisor Evaluation
Financial Services AI
A Clear Benchmark for Financial Advisors
Point93 gives advisors a credible benchmark through deterministic scoring and peer comparisons, then translates results into practical next steps.
what our partners are saying
Not sure where AI actually fits your business?
Take the 60-second AI Fit Finder. A senior, ex‑Google engineer reviews your answers and comes back with a concrete first step — book a call at the end if it’s a fit.
FAQs
What are RAG development services?
RAG (retrieval-augmented generation) development turns your own documents, wikis, and data into a trustworthy AI assistant that answers from your approved sources — grounded, cited, and governed by your access rules. Krazimo designs, builds, and deploys these knowledge systems end to end.
How much does a RAG or knowledge-AI system cost?
It depends on your data volume, the number of sources, and the accuracy and governance you need. A focused pilot on a single knowledge base costs far less than an enterprise-wide rollout, so we scope a fixed first build before you commit. See our guide to <a href="https://krazimo.com/ai-development-cost/">AI development cost</a> for what drives the number.
How is RAG different from fine-tuning an LLM?
Fine-tuning bakes knowledge into the model — expensive and hard to update. RAG retrieves your current documents at answer time, so answers stay accurate, update instantly when your data changes, and can cite their source. For most knowledge use cases, RAG is the right tool.
How do you keep answers grounded in our documents and stop hallucinations?
We ground every response in your approved sources, add retrieval guardrails and inline citations, evaluate against real questions before launch, and keep a human in the loop until the system has earned trust.
How do you handle who can see what (access permissions)?
The assistant respects your existing access rules — each user only gets answers from documents they are permitted to see — enforced at retrieval time, not bolted on afterward.
What data sources can you connect?
Documents, wikis, knowledge bases, support tickets, and databases — wherever your knowledge lives. We onboard your sources, configure the models and guardrails, and deliver a governed, production-grade knowledge assistant.