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Knowledge & Retrieval

AI Knowledge Systems: Every Answer From Your Own Data

We turn the documents, tickets, procedures, and records scattered across your company into a retrieval system that answers questions with citations, respects who may see what, and feeds the same grounded knowledge to your chat and voice agents.

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Who This Is For

Built for companies where the answer exists but nobody can find it

  • Operations teams whose procedures live in PDFs, wikis, and old emails, and whose new hires learn by asking the one person who knows.
  • Support teams answering the same product and policy questions from memory while the documentation drifts out of date.
  • Professional services firms with precedents, proposals, and matter files that are valuable but unsearchable.
  • Companies deploying chat or voice agents that need those agents to answer from verified, current knowledge rather than guesswork.

What We Build

A knowledge layer for people and agents

Enterprise knowledge base with retrieval

Documents, wikis, tickets, emails, and database records indexed with metadata, chunked and embedded for retrieval, kept current by scheduled syncs.

Internal AI assistant

Staff ask questions in plain language and get answers with citations to the source, scoped by role and department.

Customer-facing answers

The same knowledge powers website and WhatsApp assistants and voice agents, so customers get consistent, approved information.

Document intelligence

Contracts, manuals, and reports summarized, compared, and queried; key terms and obligations extracted into structured records.

Access control and audit

Per-document and per-role permissions enforced at retrieval time, with logs of who asked what and which sources were used.

Quality and freshness monitoring

Unanswered questions surfaced as documentation gaps, stale sources flagged, and answer quality measured against a test set.

The problem with company knowledge

Every company already has the answers: in procedures, contracts, tickets, emails, and the heads of a few experienced people. What it lacks is a way to get them quickly, reliably, and only to the people allowed to see them. Generic chat tools do not solve this; they either cannot see your data or see too much of it.

Retrieval built for your content

DPI builds retrieval-augmented systems: your sources are ingested through connectors, chunked and embedded with models chosen for your language and document types, stored in PostgreSQL with pgvector or in Qdrant, and searched with hybrid retrieval and re-ranking tuned on your questions. The language model answers only from retrieved passages and cites them. Permissions from the source systems are enforced at query time.

The same knowledge layer feeds every assistant we build. A customer asking on WhatsApp, a caller talking to a voice agent, and an employee asking in the internal assistant all get answers from the same verified sources.

Evaluation and upkeep

We assemble a test set of real questions with known answers and measure retrieval and answer quality before launch. After launch, syncs keep the index current, stale documents are flagged, and unanswered questions are reported as gaps to the owners of each area. Quality is reviewed monthly, together with usage by team and the questions that matter most.

Hosting and data control

Embedding and language models can run hosted under enterprise terms or self-hosted on your infrastructure. Indexes, logs, and documents can stay entirely in your environment, with retention rules per source. Regulated deployments get private hosting and audit trails by default.

How It Works

How a knowledge system is built

  1. Knowledge audit

    Inventory of sources, owners, formats, and freshness; the questions people actually ask; the permissions that must hold.

  2. Ingestion and retrieval design

    Connectors, chunking, embeddings, metadata, hybrid search, and re-ranking tuned on your content, with access rules built in.

  3. Evaluation

    A test set of real questions with known answers; retrieval and answer quality measured and tuned before rollout.

  4. Rollout and upkeep

    Launch to one team, expand by department, keep syncs and permissions current, and report gaps to documentation owners.

Stack & Integrations

Knowledge stack and sources

We build retrieval on PostgreSQL with pgvector or Qdrant, orchestrate with LangChain and LangGraph, and connect to the systems where your knowledge already lives.

PostgreSQL with pgvector Qdrant vector database LangChain and LangGraph OpenAI, Anthropic Claude, AWS Bedrock Self-hosted Qwen and open-source embedding models Google Workspace and shared drives Ticketing, CRM, and document management systems Databases and internal APIs Redis, Docker, Kubernetes, AWS

Industries

Knowledge systems by industry

Real Estate

Leases, property records, vendor contracts, and owner reporting.

Manufacturing

SOPs, quality documentation, equipment manuals, and supplier records.

Engagement Models

Engagement models

Knowledge audit

Two weeks: source inventory, question analysis, permission model, architecture, and a rollout plan.

Build and roll out

Fixed-scope delivery of the knowledge system for the first team with evaluation, connectors, and training.

Managed knowledge

Ongoing syncs, permission upkeep, quality monitoring, gap reports, and expansion to new departments and agents.

FAQ

Questions about AI knowledge systems

Is this an enterprise knowledge management system?

It does what one is meant to do, built for your company rather than sold as a shelf product: a single retrieval layer over documents, tickets, procedures, and databases spread across departments, with permissions, citations, and an owner for keeping it current. The difference from a knowledge management platform is that we build and operate it around your systems instead of asking your team to migrate content into a new one.

How does this compare to buying enterprise knowledge management software?

Software gives you a place to store and search content; it does not connect to your existing systems, decide what a given role may see, or feed a chat or voice agent with the same grounded answers. We build the retrieval layer to sit over the content and systems you already have, so nothing has to move, and the same knowledge serves people and AI agents from one source.

Is this just a search box over our files?

Search finds documents; a knowledge system answers questions, cites the passage, respects permissions, and knows when it does not know. It also becomes the knowledge layer for chat and voice agents, so the whole company answers from one source.

How do you keep answers accurate?

Retrieval grounds every answer in your sources, the assistant cites them, and a test set of real questions measures quality before launch and over time. Unanswered questions are reported as documentation gaps to the owners.

What about permissions?

Access rules from your systems are carried into the index and enforced at retrieval time, so people only get answers from documents they may see. Every query and its sources are logged.

Can it run entirely on our infrastructure?

Yes. Embedding and language models can run self-hosted, with PostgreSQL, pgvector, or Qdrant in your environment, so documents never leave your control.

How is the knowledge kept current?

Scheduled and event-driven syncs from your sources, versioning of documents, stale-source flags, and a review process with the owners of each area.

How long does it take?

A knowledge system for one team over a defined set of sources typically launches within weeks of the audit. Company-wide rollouts are staged by department.

Related

Next Step

Bring one workflow. Leave with a production plan.

Tell us where calls, tickets, documents, or approvals pile up. We map the workflow, size the impact, and propose a deployment you can measure.

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