Generative AI and large language models can draft, summarize, search, and answer in seconds - but a model left on its own invents facts and can't show its work. ALINEDS builds retrieval-augmented generation (RAG) systems that ground every response in your own verified documents, return a citation for each answer, enforce role-based access to sensitive content, and can run in a private, data-resident environment. For government and regulated organizations, that turns generative AI from a liability into an accurate, auditable tool your teams can actually deploy.
Why it matters
Public-sector and regulated teams can't act on an answer they can't verify. A wrong or unsourced response isn't just embarrassing - it can breach policy, expose PII, or drive a bad decision. RAG matters because it makes generative AI traceable and governed: every answer points back to an approved source, and sensitive data stays under your control.
What you get
Grounded, cited answers
Every response is generated from your retrieved source documents and returns a citation, so users can verify it and auditors can trace it.
Hybrid retrieval tuned to your content
We combine keyword and semantic (vector) search with re-ranking so the system retrieves the right passages from your document types, not just the closest guess.
Permissioned, role-based access
Retrieval respects your access model: users only see content they're cleared for, with PII redaction and an audit log on every query.
Private, data-resident deployment
Your prompts and documents stay in your environment and are never used to train public models; on-premises options are available for strict isolation needs.
Evaluation and guardrails
We measure answer quality and add guardrails so the system says "I don't know" and points to a human instead of guessing when the source isn't there.
Integration with your systems
We connect RAG to the repositories your teams already use - document stores, SharePoint, knowledge bases - respecting their existing permissions.
How it works
Ground
We ingest your sources, clean and chunk them, and generate embeddings so your content becomes retrievable.
Retrieve
On each question, hybrid search finds candidate passages and a re-ranker orders them by relevance.
Generate
The model answers using only the retrieved passages and returns citations to the source.
Govern
Access controls, audit logging, and ongoing evaluation keep the system secure, measurable, and improving over time.
Where it fits
Policy and procedure Q&A
Staff ask plain-language questions and get cited answers from approved manuals, regulations, and SOPs instead of searching PDFs by hand.
Constituent and help-desk support
Front-line teams answer public or internal inquiries faster, grounded in current, approved information.
Records and knowledge search
Employees find and summarize information across large document repositories with sources attached.
Program and grant research
Teams synthesize requirements and prior documents to draft and review submissions, with every claim traceable.
Key distinctions
RAG vs. a general-purpose chatbot
| Aspect | RAG (grounded) | General chatbot |
|---|---|---|
| Source of answers | Your verified documents | The model's training data |
| Citations | Every answer cites a source | Usually none |
| Accuracy on your content | High - retrieved from your data | Prone to hallucination |
| Data control | Private, data-resident | Often sent to a public model |
| Access control | Role-based, per document | None |
Compliance & security
Accurate, auditable, and data-resident
RAG deployments are engineered with encryption in transit and at rest, role-based access, PII redaction, and audit logging on every query, with private and data-resident hosting options. Designs align to NIST 800-53 and NIST CSF 2.0, support FedRAMP and GovRAMP expectations, and apply HIPAA or FERPA controls where the underlying content is sensitive.
- NIST 800-53
- NIST CSF 2.0
- NIST AI RMF
- FedRAMP
- GovRAMP
- HIPAA
- FERPA
Key terms
- Retrieval-Augmented Generation (RAG)
- A technique that retrieves relevant passages from your own data and gives them to a language model, so its answer is grounded in your content rather than only its training.
- Vector / semantic search
- Search that matches on meaning rather than exact keywords, by comparing numerical representations (embeddings) of text.
- Hallucination
- When a language model produces a confident but false or unsupported answer - the core risk RAG is designed to prevent.
Frequently asked
What is RAG and why does it matter for government?
Retrieval-augmented generation grounds an AI model's answers in your own verified documents instead of its general training data. For government, that means responses are accurate, current, and traceable to a source - essential when decisions must be defensible and auditable.
How do you stop the AI from hallucinating or citing the wrong source?
Every answer is grounded in retrieved source content and returns a citation. We use hybrid retrieval and re-ranking to surface the right passages, plus evaluation and guardrails so the system defers to a human instead of guessing when the source isn't there.
Will our data be used to train the model or leave our environment?
No. We deploy with privacy and data residency as defaults: your prompts and documents stay in your environment, are encrypted in transit and at rest, and are never used to train public models.
Can this run in a private or air-gapped environment?
Yes. We support private and data-resident deployments, including on-premises options, for organizations with strict isolation or residency requirements.
Which document sources can it use?
Document stores, SharePoint, knowledge bases, and similar repositories. We connect to what you already use and respect the existing permissions on that content.
How is this different from the search we already have?
Keyword search returns a list of documents to read; RAG returns a direct, cited answer synthesized from those documents, while still respecting who's allowed to see what.
What do we need to get started?
A defined use case and a set of source documents. We assess your data readiness, stand up a scoped pilot, and expand from there.