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02Knowledge at Scale

Answers from everything your company knows.

We turn scattered documents, tickets, and systems into an assistant that finds the right information, answers in plain language, and shows where it came from — respecting who is allowed to see what.

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A search interface returning an answer with citations from documents, spreadsheets and email

How it works

Find. Ground. Answer.

  1. 01

    Find

    Hybrid semantic and keyword search across PDFs, wikis, tickets, and databases — filtered by metadata and permissions.

  2. 02

    Ground

    Answers are built from retrieved passages, so every claim can be traced to a source your team can open.

  3. 03

    Answer

    Clear, conversational responses with citations — and a refusal when the sources don't support an answer.

Documents feeding a search system that returns a grounded answer with citations

The challenge

Why knowledge is hard to use today

  • 01

    Information in PDFs and documents

    Critical details sit in files that keyword search can't read or rank properly.

  • 02

    Knowledge that's hard to find

    People know it exists somewhere but not where — so they ask a colleague instead.

  • 03

    The same questions, repeatedly

    Experts spend hours answering what is already written down.

  • 04

    Knowledge trapped in systems

    Answers span wikis, CRMs, and ticket history that don't talk to each other.

What we build

Knowledge at Scale, in practice

Seven building blocks for turning organizational knowledge into something people and systems can use.

  • RAG systems

    Retrieval-augmented generation with chunking, embeddings, re-ranking, and grounding checks.

    • Chunking
    • Re-ranking
    • Grounding

  • Enterprise knowledge assistants

    Assistants that answer employee and customer questions with citations and access control.

    • Citations
    • Access control
    • Refusal

  • Document Q&A

    Ask questions of contracts, policies, and reports and jump straight to the source passage.

    • Passage jump
    • Citations
    • Filters

  • Knowledge bases

    Structured, maintainable stores of approved content with ownership and freshness tracking.

    • Ownership
    • Freshness
    • Governance

  • Internal AI assistants

    Task-focused helpers for support, sales, HR, and operations, grounded in internal sources.

    • Support
    • Sales
    • Operations

  • Retrieval pipelines

    Ingestion and indexing pipelines that keep content current as documents change.

    • Ingestion
    • Refresh
    • Metadata

Quality and access

Retrieval you can inspect and improve

Good answers come from good retrieval. We build the pipeline in stages we can measure, so when an answer is wrong you can see exactly which stage to fix.

  • Document- and tenant-level access control; PII-aware pipelines
  • Retrieval quality and grounding tested against your own question set
  • Caching and scoped retrieval to keep latency and cost predictable
A retrieval pipeline from documents through ingestion and index to a grounded answer, with an access-control shield

System flow

How the system works

  1. Documents / Data
  2. Ingestion
  3. Chunking
  4. Embeddings
  5. Retrieval
  6. Context
  7. LLM
  8. Response

The outcome

Turn organizational knowledge into an accessible, intelligent system.

What changes for the teams that rely on it.

  • Searching across five tools

    One question, one sourced answer

  • Experts answering repeat questions

    Self-serve answers with citations

  • Knowledge that goes stale in files

    Indexes that update as content changes

Under the hood

AI / ML capabilities

We choose rules, classical ML, deep learning, LLMs, RAG, or agents based on the problem, data, and reliability needs—not hype. Software, data, and ML engineers on one team.

  • 01

    Production RAG: ingestion, chunking, embeddings, re-ranking

  • 02

    Hybrid semantic + keyword search with metadata filters

  • 03

    Grounded answers with citations; refuse when context is insufficient

  • 04

    Multi-format document intelligence and model routing when needed

Evaluation, security & production

  • 01

    Retrieval quality, grounding, and hallucination regression tests

  • 02

    Document- and tenant-level access control; PII-aware pipelines

  • 03

    Latency and cost controls: caching, batching, scoped retrieval

  • 04

    User feedback loops and monitoring in live environments

How we deliver

A clear path from first call to production

  1. 01

    Audit the sources

    Identify what knowledge exists, where it lives, who owns it, and who may see it.

  2. 02

    Build the pipeline

    Ingest, clean, chunk, and index content with metadata and permissions preserved.

  3. 03

    Evaluate answers

    Test retrieval and responses against real questions until they meet the agreed bar.

  4. 04

    Launch & learn

    Release with feedback capture and monitoring, then tune from real usage.

Technology

Tools and platforms we work with

We choose the simplest technology that fits the problem — and work in your stack where you have one.

OpenAI

Anthropic

LlamaIndex

LangChain

Pinecone

Weaviate

PostgreSQL

Python

Hugging Face

AWS

Engagement outcome

What you receive

  • A working assistant or search experience over your content
  • Ingestion pipelines with scheduled refresh
  • Evaluation set and quality baseline for answers
  • Access control mapped to your existing permissions
  • Documentation of sources, models, and data flows

Where it applies

Industries and capabilities

FAQ

Questions we hear most

Straight answers about how we work on this kind of project.

How do you prevent made-up answers?

Answers are generated from retrieved passages and cited. We test against an agreed question set and configure the system to decline when the sources don't contain the answer.

Will it respect document permissions?

Yes. Access rules are carried into the index and applied at query time, so people only get answers from content they are allowed to see.

Can it work with our private data and models?

We design around your privacy requirements — including private deployments and providers that don't train on your data — and document where data flows.

How do we keep content up to date?

Ingestion pipelines refresh on a schedule or on change events, and content has owners and freshness tracking so stale material is flagged.

Keep exploring

More solutions

Ready to start with Knowledge at Scale?

Tell us about the problem. We'll come back with an honest view of the right approach, and what a first increment looks like.

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