RAG over your knowledge
Retrieval-augmented generation grounded in your docs, wikis, and data — with citations, so answers are traceable.
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LLM integration & RAG
LLM integration that's grounded in your data
We bring large language models into the tools and data you already use — retrieval grounded in your sources (RAG), with the evaluations and guardrails that keep answers accurate. AI inside your product, not a demo beside it.
The model is the easy part. Reliability comes from the engineering around it — retrieval over your sources, evals, guardrails, and monitoring — which is exactly what we build.
Is this you?
Who it's for
A good fit if you
Want AI features inside your product, app, or internal tools.
Have documents, knowledge, or data the AI should answer from — accurately.
Need answers grounded and cited, not confidently wrong.
Probably not a fit if you
Just want to paste into a chatbot occasionally.
Can't expose any data or systems for the AI to work with.
Don't care whether the answers are accurate.
What we build
AI features that answer from your sources
AI features in your product
Summarize, draft, classify, and search — built into your app or internal tools where the work happens.
Semantic search
Search by meaning across your content and data — far beyond keyword matching.
Classification & routing
Auto-classify replies, tickets, and documents and route them — reliably, at volume.
Evals & guardrails
Evaluation suites, hallucination checks, and guardrails that keep the output accurate and on-policy.
Safe, model-agnostic setup
We pick the right model per use case and integrate it with data security by design.
How we work
A grounded prototype in weeks, then scale
Discovery & scoping
We map the use case, sources, and accuracy bar — and the fastest path to value.
Prototype
A grounded RAG prototype on your real sources in weeks — with evals from the start.
Production build
Guardrails, evaluation suite, monitoring, and data security, baked in.
Launch & iterate
We ship, measure answer quality on real usage, and improve. You own the code.
Why Waveo
Where reliability actually comes from
Anyone can call an API. The hard part is making it accurate, safe, and integrated.
Grounded & guarded
Retrieval over your sources, citations, evals, and guardrails — we've built systems that block unverified claims by design.
Real engineering
AWS Solutions Architect certified — integrations that scale and stay reliable, with monitoring.
The product around the model
We can build the app, data layer, and agents too — not just a single model call.
Proof
LLM systems we've shipped
Client details kept confidential; described by domain and outcome.
LLM · guardrails
Drafting & verification agents
An AI SEO system where LLMs draft across a large catalog and verification agents with deterministic guardrails block unverified claims before publish.
Read the case LLM · classificationReply classification at scale
An outbound engine that auto-classifies every reply and routes it — turning tens of thousands of emails into qualified pipeline.
Read the case Related serviceAI agents
When an LLM feature needs to take actions across your tools, not just answer — agents built for production.
Explore serviceHow engagements work
Start small, prove accuracy, then scale
Every engagement starts with a paid discovery sprint, so you get a concrete plan, measured answer quality, and an estimate before committing to a build.
Discovery sprint
A fixed, paid sprint that grounds a prototype on your sources, measures quality, and returns a plan and estimate.
Fixed-scope pilot
A production LLM feature for one use case, with evals and guardrails, at an agreed scope.
Ongoing partnership
We extend AI across your product and run it as an extension of your team.
FAQ
Questions we hear a lot
What is RAG and why do we need it?
RAG (retrieval-augmented generation) grounds the model's answers in your own sources and returns citations — so responses are accurate and traceable instead of made up. It's how you get an LLM to answer reliably from your data.
How do you stop the AI from hallucinating?
Grounded retrieval, citations, evaluation suites, and guardrails that check output before it's shown — plus monitoring in production. We've built systems that block unverified claims by design.
Which model do you use?
We're model-agnostic and pick per use case and constraints. The reliability comes from the retrieval, evals, and guardrails around the model — not the model alone.
Is our data safe?
Yes — data security is designed in, and we choose deployment and model options that fit your privacy and compliance needs.
How fast can we see results?
A grounded prototype on your real sources in weeks, with measured answer quality. Production depends on scope, but we work in weeks, not quarters.
Get started
Want AI inside your product — done right?
Tell us the use case and the sources it should answer from — we'll come back with how we'd ground and ship it.
Book a discovery call