SERVICE 03 · GENAI, LLM & RAG

Answers grounded in your own documents

Retrieval-augmented LLM apps that answer from private knowledge, cite the source, and respect who may see what.

Let's talk See what we build

Citation on every answer
Permissions inherited
Zero-retention APIs

DocsWikiPDFTickets ChunkSplitEmbedVectorRetrieveHybridGroundCite

RAG

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What we built

What every retrieval build ships with

The value is in retrieval and guardrails, not the model. Each build ships with the 4 layers that keep answers accurate, permitted, and affordable.

Retrieval grounding

The model synthesizes only from the exact passages retrieved, with a clickable citation back to the source on every answer.

Proof layer Chunking and embedding strategy chosen per corpus, hybrid retrieval where keywords matter, reranking, and answer grounding checks.

Guardrails and evals

Output guardrails and an evaluation set measure hallucination control on your real questions, not on a demo.

Proof layer A graded question set drawn from your history gates launch; when retrieval finds nothing, the system says so instead of inventing an answer.

Access control

The retrieval layer inherits your directory permissions, so users only see passages from documents they are allowed to open.

Proof layer Row-level security at the index, permission checks at query time, and deletion requests that propagate through the vector store.

Cost routing

Small, fast models handle routing and rewriting; heavy models are reserved for complex synthesis.

Proof layer Per-query token telemetry against a monthly ceiling, with the volume assumption written down so it can be challenged.

Named stack

Claude OpenAI Gemini LangChain RAG pgvector Pinecone PostgreSQL Supabase Voyage embeddings MCP Python TypeScript Next.js AWS Docker

or more on every answer, linked to the source passage: 1 citation
of public models on your data, under zero-retention API terms: 0 training
higher recall from hybrid retrieval versus vector-only search: 20–40 %
of models: small for routing, large for synthesis, to control cost: 2 tiers

Use cases

Questions your documents already answer

If someone spends the day searching PDFs, wikis, threads, or contracts for an answer that exists somewhere, retrieval is the fix. These are the shapes we build most often.

Internal knowledge assistant

Answers staff questions from policies, runbooks, and past decisions with a citation back to the source document or message on every answer.
Operations · HR · Engineering
Contract and clause review

Splits contracts into clauses, scores each against your playbook, and hands a lawyer a ranked list to sign off rather than a blank PDF.
Legal · Procurement · Real estate
Grounded support replies

Drafts replies from your product docs and live order context, so every response is on-brand and traceable to a real source.
E-commerce · SaaS · Property management
Search over chat history

Indexes Slack or Teams conversations so plain-language questions about company history return the original thread, not a guess.
Agencies · Startups · Distributed teams
Structured extraction

Pulls entities, figures, and dates out of unstructured documents into a queryable database with the source span recorded for each field.
Lending · Insurance · Compliance
Policy and compliance Q&A

Answers regulatory questions only from approved policy documents, with access limited by role and every answer logged for audit.
Healthcare · Finance · Public sector
Customer-facing product copilot

Answers user questions inside your product from documentation and account data, escalating to a person when retrieval finds nothing.
SaaS · Fintech · Marketplaces
Writing grounded in your archive

Drafts, rewrites, and summarizes across a private corpus, keeping every suggestion tied to the material the author already owns.
Media · Research · Consulting

Client stories

Retrieval already answering the question

Each one started as a person searching by hand. Each one now retrieves under access control and cites the source.

All case studies

How we ship

Built to be grounded, governed, and owned

Every retrieval system leaves with the same 4 things defined in the specification before code exists.

01 Retrieval quality

A demo proves it can answer once

Production requires knowing how often it answers correctly, and on what.

The question set: 50 to 200 graded question and answer pairs per corpus at launch, drawn from your real search history including the awkward ones.
Hybrid retrieval: Dense embeddings plus keyword matching, then reranking. Acronyms and internal product names do not survive vector-only search.
Grounding checks: Every claim in an answer is checked against the retrieved passages before it leaves the system. Unsupported claims are dropped or the answer is withheld.

02 When it is wrong

Retrieval misses sometimes

The engineering question is what happens next.

Nothing relevant retrieved: The system says so and points to a person. It does not answer from general knowledge.
Conflicting sources: Both passages are surfaced with dates and owners, so the reader decides rather than the model picking silently.
Answer fails verification: Blocked before delivery and logged as a regression case, so the question set only grows.

03 Governance

Permissions and audit are engineering properties

Defined in the specification, not a policy filed after launch.

Permissions inherited: The index carries your directory or row-level permissions, and every query is filtered by who is asking before retrieval runs.
The audit trail: Every question, retrieved passage, answer, and citation is logged and exportable without our help.
Retention and deletion: How long embeddings and logs are kept, and how a deletion request propagates through the vector index, not just the source system.

04 Run cost

The system cannot spend more than you approved

Query volume and token usage drive the bill, so both are measured per request against a ceiling.

Model routing: Small, fast models handle query rewriting and routing; heavy models are reserved for complex synthesis.
Alert thresholds: 70% and 90% of monthly budget, with an automatic halt at 100%.
Embedding and storage: Re-indexing frequency and vector storage are named in the estimate published before you sign.

WHO RUNS IT AFTER LAUNCH

Operate retainer We monitor retrieval quality, re-index on your schedule, run the question set on every change, and respond within an agreed window.

Handover Your team runs it. You get the runbook, the question set, the dashboards, and 2 training sessions. We stay reachable for 30 days.

WHAT WE WILL NOT BUILD

A chat box over documents with no citations. If the reader cannot check the source, it is not grounded.
Retrieval over data we are not permitted to see, or an index that ignores your existing permissions.
Systems that answer from general model knowledge when retrieval finds nothing.
Pipelines that send private data to a provider without zero-retention terms named in the specification.
Anything where full-text search already solves it. We will tell you.

For U.S. SLED prime contractors

Records search and constituent Q&A, behind the prime.

For SLED scope under NAICS 511210, we index public records and 311 knowledge bases and answer constituent queries as your subcontractor, never facing the agency.

NAICS 511210 541512 518210

See SLED Subcontracting

NDA-first, subcontract-only. We work behind the prime, under your brand. We do not pursue prime contracts and we never face the agency.

Data stays yours. Private API endpoints and zero-retention agreements mean your data never trains a public model.

Deployed in your VPC. Models run inside a secure virtual private cloud with role-based access at the retrieval layer.

FAQ

GenAI and RAG, answered.

Straight answers about grounded LLM apps. If yours isn't here, ask it on the call, we answer the hard ones first.

Ask the hard one

Will our private corporate data be used to train public models?

No. We use private API endpoints and zero-retention agreements so your data never leaves your controlled environment.

How do you prevent the system from inventing facts?

Strict retrieval grounding and multi-model verification mean the model can only synthesize answers from the exact documents provided to it.

How do we control who sees what internal information?

The retrieval engine inherits your existing active directory permissions, so users only retrieve documents they are authorized to view.

What drives the ongoing operational cost?

Query volume and token usage. We optimize by using smaller, faster models for routing and reserving heavy models for complex synthesis.

How is this different from an AI agent?

This is the retrieval and LLM-application layer, the part that grounds answers in your documents. Agents add orchestration and tool use on top of it.

Service Agentic engineering → Service AI consulting and readiness → Service Data and platform → Industry Healthcare →

Start the conversation

Stopsearching.Start
retrieving

Tell us where your team loses hours hunting through documents. That is where the first index goes.

Scope a RAG build

30 minutes the engineer who leads delivery no deck, no pitch

Answers grounded in your own documents