Perpetio

LLM and RAG development services for business

AI that answers from your own knowledge.

We build LLM apps and RAG systems that search your documents, data and tools — and answer with sources, so your team and customers can trust every reply.

Book a free consultationHow RAG works
Clutch rating
4.8 / 5
apps shipped
200+
to a working RAG pilot
2–4 weeks
reply to every request
< 24h
knowledge.yourcompany.comAnswers with sources
Company knowledge assistant · 12,400 documents
How many vacation days do new hires in Poland get?
New hires in Poland get 20 days a year1, rising to 26 after 10 years of total work experience2. Unused days roll over until September 301.
SOURCES
1HR Policy — Poland 2026.pdfPage 4 · updated Jan 2026
2Labour Code summary (Confluence)Section 3.2
Ask a follow-upOpen policy

Answers from policies, wikis and files — with a source for every claim

We build RAG on the models that fit your data and budget
  • OpenAI
  • Claude
  • Gemini
  • Llama
  • Mistral
  • AWS Bedrock
  • Azure OpenAI
What we build

LLM apps grounded in your data.

From one internal assistant to a full knowledge platform for customers and staff.

  • Knowledge-base assistants

    Chat over wikis, policies and help centers for staff or customers.

    • Internal
    • Customer-facing

  • RAG pipelines

    Ingestion, chunking, embeddings, retrieval and reranking — tuned on your data.

    • Hybrid search
    • Reranking

  • Document Q&A

    Ask questions about contracts, manuals and reports and get page references.

    • PDF
    • Citations

  • Talk to your data

    Natural-language questions over databases and BI, with safe text-to-SQL.

    • Text-to-SQL
    • BI

  • Fine-tuning

    Models adapted to your terms, format and domain when prompting isn’t enough.

    • LoRA
    • Domain terms

  • Private & self-hosted LLMs

    Open models in your cloud or on-prem — data never leaves your walls.

    • On-prem
    • Llama
    • Mistral

  • Evaluation & quality

    Test sets, accuracy scores and hallucination checks before every release.

    • Evals
    • Accuracy

  • Access control

    Users only get answers from documents they are allowed to see.

    • Permissions
    • SSO

  • LLMOps & cost control

    Caching, model routing and monitoring to keep answers fast and cheap.

    • Caching
    • Routing
Data it can use

Connect the knowledge you already have.

We keep the index in sync, so answers are always up to date.

Choosing an approach

Long context, RAG or fine-tuning?

Three ways to give an LLM your knowledge.

  • Simple

    Long-context prompt

    Speed to launch
    Fresh data
    Savings at scale

    BEST FORA few documents, low volume

  • Most popular
    Recommended

    RAG

    Speed to launch
    Fresh data
    Savings at scale

    BEST FORLarge, changing knowledge bases

  • Specialized

    Fine-tuning

    Speed to launch
    Fresh data
    Savings at scale

    BEST FORFixed format, domain language

Often the best answer is both.

RAG keeps facts fresh; light fine-tuning teaches format and terms.

Help me choose
RAG by industry

Knowledge assistants for your industry.

Answers grounded in your documents — with sources your team can check.

Healthcare

Clinical guidelines assistant

Clinicians ask questions and get answers from approved protocols, with references.

  • Protocols
  • Formulary
  • EHR
Build this assistantHealthcare apps →

HOW THE ANSWER IS FOUND

  1. Nurse asks a question“Max paracetamol dose, 12 kg child?”
  2. Searches approved protocolsPediatric formulary 2026
  3. Answer with sourcePage and section
  4. Safe, consistent answersFewer calls to doctors
How RAG works

From your documents to a trusted answer.

Four steps run behind every reply — tuned and tested on your own data.

  1. 01 · INGEST

    Collect your data

    Docs, databases and tools synced on a schedule.

    • Files & wikis
    • Databases
    • CRM & tickets
  2. 02 · INDEX

    Chunk & embed

    Split into passages, turned into vectors, with permissions kept.

    • Smart chunking
    • Embeddings
    • Access tags
  3. 03 · RETRIEVE

    Find the best passages

    Hybrid search plus reranking picks what really answers the question.

    • Vector + keyword
    • Reranking
    • Query rewriting
  4. 04 · ANSWER

    Generate with sources

    The LLM answers only from those passages and cites them.

    • Grounded answer
    • Citations
    • Hallucination checks

Answers your team can trust

Every reply is grounded, cited and measured.

  • Answers only from your sources
  • Citations for every claim
  • “I don’t know” when data is missing
  • Per-user document permissions
  • Accuracy tested before release
  • Your data is never used to train models
Tech stack

Proven RAG building blocks.

We choose the vector store, embeddings and model for your data size and privacy needs.

LLMs & embeddings

  • OpenAI
  • Claude
  • Gemini
  • Cohere
  • Llama
  • Mistral

RAG frameworks

  • LlamaIndex
  • LangChain
  • LangGraph
  • Ollama
  • MCP
  • Dify

Vector & search

  • pgvector
  • Qdrant
  • Elasticsearch
  • Redis
  • Supabase
  • MongoDB

Cloud AI

  • AWS Bedrock
  • Azure OpenAI
  • Vertex AI

On-device

  • Core ML
  • ML Kit
  • TensorFlow Lite

Quality & ops

  • LangSmith
  • Langfuse
  • Datadog
Case studies

LLM products we have shipped.

All case studies
How we work

A trusted RAG system in weeks.

We measure accuracy on your real questions before anyone relies on it.

  1. 011 week

    Data audit

    Sources, quality, access rules.

  2. 021 week

    Test questions

    Real questions with correct answers.

  3. 032–4 weeks

    Pilot

    RAG on your data with accuracy score.

  4. 042–6 weeks

    Integration

    Into your app, Slack or portal.

  5. 051 week

    Launch

    With feedback and monitoring.

  6. 06ongoing

    Improve

    Tune retrieval from real usage.

  • RAG audit

    1–2 weeks

    Check if your data is ready.

    • Source review
    • Accuracy plan
    • Cost estimate
  • Most popular

    RAG pilot

    2–4 weeks

    Working assistant on your data.

    • Accuracy report
    • Citations
    • Go / no-go

  • Knowledge platform

    2–4 months

    Company-wide, secure and synced.

    • All sources
    • Permissions
    • Admin & analytics

  • Dedicated AI team

    monthly

    Keep improving and adding sources.

    • AI engineers + QA
    • Evals each release
    • Scale anytime
More AI services

Need something more specific?

FAQ

Questions about LLM & RAG.

Didn't find yours? Ask us directly — we reply within 24 hours.

What is RAG?

Retrieval-augmented generation: before answering, the AI searches your documents and data, then answers only from what it found — with sources.

How accurate will it be?

We build a test set of your real questions and measure accuracy before launch. Most pilots reach 85–95% correct answers, and the system says “I don’t know” instead of guessing.

Can we keep our data private?

Yes. We can use enterprise APIs that don’t train on your data, or run open models fully in your cloud or on-prem.

How much does a RAG system cost?

A pilot on one knowledge source usually costs $12,000–$30,000. Company-wide platforms depend on the number of sources and users.

Will users see documents they shouldn’t?

No. Permissions from your source systems are kept in the index, so each user only gets answers from documents they can access.

When should you use RAG instead of fine-tuning?

Use RAG when answers must come from documents that change often or need sources. Use fine-tuning to change a model’s style or format. Most business knowledge assistants Perpetio builds use RAG.

Let’s make your knowledge answer back.

Tell us what you're working on. You'll get a clear plan, not a sales pitch.

< 24h

Response time

Free

First consultation

NDA

On request

Lara Kushnei

Lara Kushnei

Business Growth Executive

HQ office

Kaupmehe tn 7-120, Tallinn 10114, Estonia

R&D office

8A Yaneva str., Lviv 79026, Ukraine