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Codemotives

AI Solutions

Answers Grounded in Your Data

Retrieval-augmented generation lets a model answer from your documents instead of guesswork. We build the retrieval pipeline, grounding and evaluation that make it dependable.

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Overview

RAG Development

RAG quality is decided mostly by retrieval, not by the language model. Codemotives focuses on the pipeline: how documents are parsed and split into chunks, which embedding model represents them, how a vector store and keyword search are combined, and how results are ranked before the model ever sees them. Poor retrieval produces confident, wrong answers regardless of model size.

We then ground the generated answer in the retrieved passages, show citations so users can verify, and measure the whole system on a test set built from your own questions. We are candid about limits: messy source documents, out-of-date content and questions the corpus cannot answer all need explicit handling, including a clear "I could not find this" response.

Context

Problems we solve

  • 01

    Poor retrieval quality

    If the right passage is never retrieved, the model cannot answer correctly. Chunking and ranking choices decide most outcomes.

  • 02

    Hallucinated answers

    Models may fill gaps with plausible fiction. Grounding rules, citations and abstention reduce this but cannot eliminate it.

  • 03

    Messy source content

    Duplicated, outdated or poorly formatted documents degrade results. Content cleanup is often the highest-value work.

  • 04

    Access control on content

    Users should only get answers from documents they are allowed to see, which retrieval must enforce.

Capabilities

What we deliver

  1. 01

    Ingestion pipelines

    Parsers for PDFs, web pages, wikis and databases, with scheduled refresh and removal of outdated content.

  2. 02

    Chunking strategy

    Structure-aware splitting with metadata, tuned against your document types rather than a default size.

  3. 03

    Embeddings and vector stores

    Model selection and indexing in a vector database, often combined with keyword search for exact terms.

  4. 04

    Reranking and grounding

    Reranking of candidates, answers that cite sources, and refusal when evidence is insufficient.

  5. 05

    Retrieval evaluation

    Test questions with known answers to measure whether the right passages are found and used.

  6. 06

    Permission filtering

    Metadata and identity checks so results respect existing document access rules.

Where it fits

  • Support agents searching product documentation
  • Policy and procedure question answering
  • Research across technical manuals and reports
  • Customer-facing help center answers with citations
  • Sales teams querying product and pricing documents

Technologies we use

  • Python
  • pgvector
  • Pinecone
  • Qdrant
  • Elasticsearch
  • LangChain
  • LlamaIndex
  • Sentence Transformers

Approach

How we deliver

  1. 1

    Discover

    We learn your goals, users, constraints and existing systems before proposing anything.

  2. 2

    Plan

    Scope, priorities, architecture and milestones are agreed so everyone knows what ships when.

  3. 3

    Design

    User flows, interfaces and technical design are shaped and reviewed with you early.

  4. 4

    Build

    Work is delivered in short iterations with regular demos, so progress is always visible.

  5. 5

    Test

    Functional, integration and performance checks run throughout, not only at the end.

  6. 6

    Launch & support

    We deploy, monitor and keep improving the product after it goes live.

FAQs

Common questions

Have a question that is not covered here? Get in touch.

Will RAG stop the model from hallucinating?

It reduces it, but does not remove it. Grounding in retrieved text, citations and a willingness to say "not found" help a great deal, and evaluation on your own questions shows how often problems remain.

RAG or fine-tuning?

For answering from changing knowledge, RAG is usually the better starting point because you can update documents without retraining. Fine-tuning suits changes in style, format or specialized behavior, and the two can be combined.

Where does our data go?

That depends on the architecture. Embeddings and generation can run through hosted APIs or entirely within your own environment using open models. We lay out each option so your privacy team can decide.

Explore

Keep exploring

Let's build what's next

Tell us about your idea, product or legacy system. Send us a message and we will help you scope the right next step.

Get in touch